Nonnegative matrix factorization (NMF) has attracted increasing attention in data mining and machine learning . However, existing NMF methods have some limitations. For example, some NMF methods seriously suffer from noisy data contaminated by outliers, or fail to preserve the geometric information of the data and guarantee the sparse parts-based representation. To overcome these issues, in this paper, a robust and sparse NMF method, called correntropy based dual graph regularized nonnegative matrix factorization with local coordinate constraint (LCDNMF) is proposed. Specifically, LCDNMF incorporates the geometrical information of both the data manifold and the feature manifold, and the local coordinate constraint into the correntropy based objective function. The half-quadratic optimization technique is utilized to solve the nonconvex optimization problem of LCDNMF, and the multiplicative update rules are obtained. Furthermore, some properties of LCDNMF including the convergence, relation with gradient descent method , robustness, and computational complexity are analyzed. Experiments of clustering demonstrate the effectiveness and robustness of the proposed LCDNMF method in comparison to several state-of-the-art methods on six real world image datasets.

Nonnegative matrix factorization (NMF) has been a state-of-the-art data representation method, since it contains the psychological and physiological evidence for parts-based representation in the human brain. However, many existing NMF methods fail to ensure the decomposed results to be sparse, or ignore some useful geometrical structure information in the data. In this paper, a sparse NMF method, called dual graph regularized nonnegative matrix factorization with l1-norm sparsity constraint (l1-DNMF) is proposed to solve the two problems together. In addition, to satisfy the locality condition and sparsity constraint simultaneously, we also propose the dual graph regularized nonnegative matrix factorization with local coordinate constraint (LDNMF). By using the multiplicative update algorithm to solve the optimization problems of l1-DNMF and LDNMF, we derive two efficient alternating iterative methods. Experimental results on four image datasets demonstrate the promising performance of the new methods compared with several related methods for clustering applications.

Heart disease is one of the leading causes of mortality worldwide. Cardiac rehabilitation plays a crucial role in enhancing quality of life and helping cardiac patients in returning to functional baseline. This paper studies cardiac patients during rehabilitation exercises by monitoring their ECG signals. A novel minimum error entropy based R peak detection scheme is proposed. ECG signals from different types of rehabilitation exercises are processed and analyzed. Unlike traditional methods which rely on intermittent measurements, the described ECG based rehabilitation monitoring provides continuous and detailed information about patients' cardiac activities. The findings from this study will be helpful in the personalized cardiac rehabilitation.

Nonnegative matrix factorization (NMF) is a significant matrix decomposition technique for learning parts-based, linear representation of nonnegative data, which has been widely used in a broad range of practical applications such as document clustering, image clustering, face recognition and blind spectral unmixing. Traditional NMF methods, which mainly minimize the square of the Euclidean distance or the Kullback-Leibler (KL) divergence, seriously suffer the outliers and non-Gaussian noises. In this paper, we propose a robust sparse nonnegative matrix factorization algorithm, called l1-norm nonnegative matrix factorization based on maximum correntropy criterion (11-CNMF). Specifically, l1-CNMF is derived from the traditional NMF algorithm by incorporating the l1 sparsity constraint and maximum correntropy criterion. Numerical experiments on the Yale database and the ORL database with and without apparent outliers show the effectiveness of the proposed algorithm for image clustering compared with other existing related methods.

In this letter, two cycle frequencies estimation algorithms are proposed to estimate the number of weak cyclostationary signals and their cycle frequencies in the presence of higher power interferer signal. Both cycle frequencies estimation algorithms are eigenanalysis-based methods that use the cyclic autocorrelation function of the received signals. In particular, a new eigen-analysis cyclic null spectrum is introduced to determine the number of cyclostationary signals and estimate their cycle frequencies. The performances of the proposed cycle frequency estimation algorithms are demonstrated with numerical examples and compared with the existing cycle frequency estimation algorithms. It is shown that both the proposed cycle frequencies estimation algorithms have better accuracy than the existing methods and could work with small sample size for faster estimation.

Constrained adaptive filtering algorithms have been extensively studied in many applications. Most existing constrained adaptive filtering algorithms are developed under the mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises. This assumption however fails to model the behavior of non-Gaussian noises found in practice. Motivated by the robustness and simplicity of maximum correntropy criterion (MCC) for non-Gaussian impulsive noises, this paper proposes a new adaptive filtering algorithm called constrained maximum correntropy criterion (CMCC). Specifically, CMCC incorporates a linear constraint into a MCC filter to solve a constrained optimization problem explicitly. The proposed adaptive filtering algorithm is easy to implement, has low computational complexity, and can significantly outperform those MSE based constrained adaptive algorithms in heavy-tailed impulsive noises. Additionally, the mean square convergence behaviors are studied under energy conservation relation, and a sufficient condition to ensure the mean square convergence and the steady-state mean square deviation (MSD) of the CMCC algorithm are obtained. Simulation results confirm the theoretical predictions under both Gaussian and non-Gaussian noises, and demonstrate the excellent performance of the novel algorithm by comparing it with other conventional methods.

Sixteen conventional heart beat variability (HRV) parameters and eight vital signs have shown promise in the prediction of cardiac arrest within 72 hours. Besides these 24 parameters, we proposed adding two new features for cardiac arrest prediction, which are approximate entropy (ApEn) and sample entropy (SpEn). ApEn and SpEn are nonlinear HRV parameters capable of characterizing heart conditions. These two entropies were derived from electrocardiography recordings and combined with the existing 24 features to form feature combinations. The experiments were conducted by using linear kernel Support Vector Machine classification technique to investigate the effects of using ApEn, SpEn together with 24 parameters on cardiac arrest prediction. The dimensionality reduction approach, Principal Component Analysis, was applied to suppress the dimensionality. Results reveal that the prediction performance of adding ApEn and SpEn to the 24 parameters is improved significantly compared to using the 24 parameters only. Dimension reduction has additional positive effects on improving the prediction results.

Conventional wideband beamformers are known highly sensitive to gain and phase errors in microphone characteristics, especially for small-sized arrays. Recently, a robust design approach for nearfield wideband beamformers using worst case performance optimization has been proposed. However, the worst case performance optimization criterion might be overly conservative in practice, which may lead to undesired low-pass filtered distortion of target signals. In this paper, a robust design approach for nearfield wideband beamformers using the worst case mean performance optimization is proposed, which is less conservative than the existing approach. Unlike the existing robust design approaches which have no control over array response variance, the presented approach further proposes to impose the passband response variance constraint to enhance the stability of passband array response and hence to reduce the target signal distortion. Moreover, some insights into the properties of array response variance are derived, which are helpful to better understand the robustness characteristics of nearfield wideband beamformers. Design examples demonstrate the effectiveness of the proposed robust design approach.

Traditional risk score prediction is based on vital signs and clinical assessment. In this paper, we present an intelligent scoring system for the prediction of cardiac arrest within 72 h. The patient population is represented by a set of feature vectors, from which risk scores are derived based on geometric distance calculation and support vector machine. Each feature vector is a combination of heart rate variability (HRV) parameters and vital signs. Performance evaluation is conducted on the leave-one-out cross-validation framework, and receiver operating characteristic, sensitivity, specificity, positive predictive value, and negative predictive value are reported. Experimental results reveal that the proposed scoring system not only achieves satisfactory performance on determining the risk of cardiac arrest within 72 h but also has the ability to generate continuous risk scores rather than a simple binary decision by a traditional classifier. Furthermore, the proposed scoring system works well for both balanced and imbalanced datasets, and the combination of HRV parameters and vital signs shows superiority in prediction to using HRV parameters only or vital signs only.

The widespread availability of photo manipulation software has made it unprecedentedly easy to manipulate images for malicious purposes. Image splicing is one such form of tampering. In recent years, researchers have proposed various methods for detecting such splicing. In this paper, we present a novel method of detecting splicing in images, using discrepancies in motion blur. We use motion blur estimation through image gradients in order to detect inconsistencies between the spliced region and the rest of the image. We also develop a new measure to assist in inconsistent region segmentation in images that contain small amounts of motion blur. Experimental results show that our technique provides good segmentation of regions with inconsistent motion blur. We also provide quantitative comparisons with other existing blur-based techniques over a database of images. It is seen that our technique gives significantly better detection results.

This paper presents an alternative approach for heart rate measurement. Instead of using an ECG sensor, the proposed design uses sound signals received from a microphone which does not require skin-contact. Specifically, the design uses an air conductive microphone and an efficient algorithm to estimate the heart beat parameters of the wearer. The estimates are obtained for different activities undertaken by the wearer. The activities studied include sitting, jumping, reading, laughing, singing, and coughing. Data are collected from researchers and students working in the laboratory, and in the presence of lung sound, and other environmental sounds and noise. Preliminary results show that, the method can be an effective alternative means of monitoring cardiac (heart) sounds in a natural environment.

This paper proposes an iterative robust capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector based on conventional RCB formulation. Instead of using a fixed uncertainty level, we adaptively update the uncertainty level at each iteration. The uncertainty is updated based on the projection of the presumed steering vector onto the noise subspace. The iteration converges when the noise subspace projection is zero. We additionally impose a constraint that restricts the estimated steering vector to be within a spatial sector in order to overcome inappropriate updating of the uncertainty due to subspace swap at low signal-to-noise ratio (SNR) case. Simulation results show that the adaptive uncertainty based IRCB converges faster than the fixed uncertainty based IRCB. Besides alleviating the need to assume any knowledge on the mismatch, the proposed beamformer requires lower computational cost as compared to the convex optimization based beamformer design.

Many advanced adaptive beamformers are robust against arbitrary array steering vector (ASV) mismatches within a presumed uncertainty set. Adaptive array tolerating significant steering direction error usually requires a large size of ASV uncertainty set. In such case, however, the output signal-to-interference-plus-noise ratios (SINRs) of robust methods degrade quickly with the increasing size of the uncertainty set. In this paper, we propose a new compact ASV uncertainty set which is modelled explicitly by the uncertainty on steering direction and the other arbitrary ASV errors. A robust adaptive beamformer is derived based on this new ASV uncertainty set. To eliminate the non-convex constraint on array magnitude response, we force the real part of array response to exceed unity regarding the ASVs within the uncertainty set. Furthermore, using the worst-case optimization technique, the resultant beamformer is formulated as a quadratic optimization problem with semi-infinite second-order cone (SOC) constraints. Numerical studies show that a large robust response region is easy to achieve and the resultant beamformer achieves high performance on SINR enhancement.

Fake images have become ubiquitous today in society, as they can be created at the expense of a few clicks using photo manipulation software. The detection of splicing in images has received research attention in recent years. In this paper, we present a novel method of detecting splicing in images, using discrepancies in motion blur. We use motion blur estimation through spectral matting in order to detect inconsistencies between the spliced region and the rest of the image. Experimental results show the effectiveness of our technique. Comparison studies show that this technique performs better than existing blur-based techniques when applied to motion blur.

Due to the constraint on half-wavelength inter-element spacing of a uniformly spaced array, sparse arrays are usually designed to be non-uniformly spaced. Through proper design, unequally-spaced sparse arrays can have higher spatial resolution, lower sidelobe and less number of sensors required in comparison with uniformly spaced arrays. However, the synthesis of a sparse array is a non-convex process as the array beampattern is an exponential or trigonometric function of sensor positions. In this paper, we propose a synthesis scheme, where the design of sparse arrays is formulated as a convex optimization problem. The array weights are optimized to achieve minimum Peak Sidelobe Level (PSL) as well as maximizing the sparsity of the array by optimizing an objective function that includes two terms, one measures the PSL and the other measures the sparsity of array. Sparse array is then obtained by removing those sensors with weights approximately equal to zero. The proposed design scheme eliminates the need of optimization according to sensor positions, which consequently solves the problem in non-convex optimization that cannot guarantee to find the optimum solution with reasonable computation time. Numerical studies show that it can be successfully applied to sparse array synthesis with low computational complexity. Moreover, lower PSL and higher resolution of mainlobe can be achieved in comparison with a uniformly spaced array.

A novel robust adaptive beamformer, with new robust constraints on array magnitude response was proposed by utilizing the autocorrelation sequence of array weight vector and the worst-case optimization technique. The proposed adaptive beamformer was formulated as a linear programming problem with second-order cone semi-infinite constraints, which can be eliminated by using the sampling technique. In this paper, we transform these semi-infinite second-order cone constraints into some norm constraints and linear matrix inequality (LMI) constraints. The advantage of this new formulation of the problem is that the sampling of the angles is avoided. The exact optimal result of the problem can be obtained instead of the approximated one provided by sampling technique. The resultant beamformer possesses superior robustness against arbitrary array imperfections and high performance on signal-to-interference-plus-noise ratio (SINR) enhancement even with a large controlled robust response region.

Common assumptions of the conventional approaches to acoustic source localization are usually that the microphones used are ideal and that the locations of the microphones are also knownapriori, which usually may not hold in practice. Therefore, the microphone arrays need to be calibrated carefully before use. However, it is not an easy task to calibrate microphone arrays perfectly. In this paper, we proposed an algorithm for acoustic source localization based on the least-squares support vector machines (LS-SVMs). The advantage of the proposed algorithm is that it requires no calibration of microphone arrays. The performance and effectiveness of the proposed method is demonstrated by simulation results and the real-data experiments.

Most existing frequency-invariant (FI) beamformer algorithms design array beampattern response across the entire frequency bands. In this paper, the design of array gain response is extended to both selective frequency and spatial region. The algorithm consists of an objective function that has a two-dimensional constraint. One dimension constraint is on frequency range; this is to ensure a selective frequency invariant region is formed. The second dimension constraint is on spatial direction; this is to maintain the array response of the beamformer constant for a small amount of angle centered at the desired direction. Having such constant gain response over a selective spatial region makes the array beamformer less sensitive to the exact position of the source. Advanced optimization method such as second order cone programming (SOCP) is used to solve this complex optimization problem with high efficiency and accuracy. Simulation results are compared with other existing algorithms. It demonstrates that the proposed method is able to achieve a constant gain over the specified sector of angle and at the same time having a lower mean square error on the FI performance over the specified frequency region.

Broadband beamformers are known to be highly sensitive to the errors in microphone array characteristics, especially for small-sized microphone arrays. This paper proposes an algorithm for the design of robust broadband beamformers with passband shaping characteristics using Tikhonov regularization method by taking into account the statistics of the microphone characteristics. To facilitate the derivation, a weighted least squares based broadband beamforming algorithm with passband shaping characteristics is also proposed, while keeping a minimal stopband level. Unlike the existing criteria for broadband beamforming for microphone arrays, the proposed approaches fully exploit the degrees of freedom in weighting functions of the criteria used to design broadband beamformers. By doing so, efficient and flexible broadband beamforming can be achieved with the desired passband shaping characteristics. In addition, this paper also shows that the robust broadband beamforming using the statistics of the microphone characteristics recently proposed (Doclo and Moonen, 2003 and 2007) belongs to the class of white noise gain constraint-based techniques, which gives an insight into the nature of the state-of-the-art approach. The performance of the proposed beamforming algorithms is illustrated by the design examples and is compared with the existing approaches for microphone arrays.

In this paper, a novel algorithm on beam pattern synthesis for linear aperiodic arrays with arbitrary geometrical configuration is proposed. The algorithm is based on an improved genetic algorithm (IGA) that simultaneously adjusts the weight coefficients and inter-sensor spacings of a linear aperiodic array. A novel section-based crossover and a self-supervised mutation process are developed to improve the convergence performance. The results from simulation illustrate that with the IGA, the peak sidelobe level (PSL) of the synthesized beam pattern has been successfully lowered. In addition, the computational cost of the proposed algorithm can be as low as being about 10% of that of a recently reported genetic algorithm based synthesis method. The robustness of the proposed IGA has been illustrated clearly from the statistic of multiple independent runs too. The excellent performance of the IGA makes it a promising optimization algorithm where expensive cost functions are involved.

In this paper, a novel robust adaptive beamformer is proposed based on semidefinite programming (SDP) and worst- case optimization. With the SDP formulation, the array output power and magnitude response can be expressed as linear functions. New constraints on magnitude response are introduced in the adaptive array. The proposed method can flexibly control the robust response region with a specific beamwidth and response ripple. In practical applications, the array suffers from having not only steering direction error, but also many other array imperfections. To make the adaptive beamformer robust against all kinds of array imperfections, the worst-case optimization technique is proposed to reconstruct the beamformer. By minimizing the array output power with respect to the worst-case effect of array imperfections, the resultant beamformer possesses superior robustness against arbitrary array imperfections. Since the constraints on magnitude response are inequality constraints, most of them are inactive in the optimization process so that few degrees of freedom (DOFs) of the adaptive beamformer are consumed. Consequently, the resultant beamformer has high performance on signal-to-interference-plus-noise ratio (SINR) improvement. Simple implementation, flexible performance control as well as significant SINR enhancement support the practicability of the proposed method.

In this paper, a novel robust adaptive antenna array with new linear matrix inequality (LMI) constraints on the array magnitude response is proposed. Most of the advanced robust adaptive beamformers, which are robust against arbitrary array steering vector (ASV) errors within a presumed uncertainty set, have poor performance facing a large uncertainty on direction-of-arrival (DOA). Since the DOA uncertainty may result in a large error in the ASV, a big uncertainty set is required to make the adaptive array robust against a large DOA error. The adverse effect is the degraded output signal-to-interference- plus-noise ratio (SINR). In this paper, a compact model of ASV uncertainty set is described by the uncertainties on DOA and other arbitrary ASV errors explicitly. Based on this new ASV uncertainty set, a novel robust adaptive beamformer is derived. In order to eliminate the semi-infinite constraint in the proposed beamformer, new LMI constraints are derived. The proposed beamformer possesses superior robustness against arbitrary array imperfections as well as a large DOA error. A large robust response region is easy to achieve and the resultant beamformers still have high performance on SINR enhancement.

In this paper, automatic identification of emotional states from human speech is addressed. While several papers have been published in the literature on speech emotion recognition, the features used are taken or modified from those used for speech recognition purposes. However, not all features used for speech recognition are of equal importance for emotion recognition. This paper addresses this issue and proposes a systematic method on feature selection for emotion recognition from speech signals. The idea is to work on a well-selected small feature set and use it to remove irrelevant information. Specifically, the proposed method uses the similar idea of the Canonical Correlation Analysis (CCA) to estimate the linear relationship between the various features and the emotional states. The outcome is a set of features that are of most relevance to the emotions. Experiments have been conducted using the LDC database and with the use of the Probabilistic Neural Network (PNN) as the classification method. The results obtained show that, comparable accuracies can be obtained for the emotional states tested with the use of only about 30% of the features considered. This implies that the computational load can be reduced greatly too.

In this paper, we proposed, a new approach to track the object of interest using bi-model algorithm. The bi-model comprises a long term model and a short term model to allow continuous tracking of the object of interest. To measure the similarity of the bi-model and the candidate targets, an invariant spatiogram descriptor was proposed. Compared to the original spatiogram the new definition is invariant to rotation without sacrificing the performance. The experiments on our dataset and the publicly available datasets verify the effectiveness of our proposed algorithm.

With the increasing demand for spoken language interfaces in human-computer interactions, automatic recognition of emotional states from human speeches has become of increasing importance. In this paper, we propose a novel hybrid scheme that combines the probabilistic neural network (PNN) and the Gaussian mixture model (GMM) for identifying emotions from speech signals. In order to handle mismatches more effectively, the universal background model (UBM) is incorporated into the GMM, and the resultant model is denoted as UBM-GMM. In the hybrid scheme, the strengths of the PNN and the UBM-GMM are combined through a novel conditional-probability based fusion algorithm. Experimental results show that the proposed scheme is able to achieve higher recognition accuracy than that obtained by using PNN or UBM-GMM alone.

In this paper, a novel robust adaptive beamformers with linear matrix inequality (LMI) constraints on magnitude response is proposed. The recently proposed robust adaptive beamformer has a drawback that some of the constraints are semi-infinite. Although the sampling technique provides a good approximation of the optimization problem, the cost is the heavy computation load. In order to overcome this problem, new LMI constraints are derived in this paper to replace the semi- infinite constraints so that the sampling is avoid and an exact solution can be obtained.

In this paper, a QR-RLS based Minimum Variance Distortionless Responses (MVDR) method, and its systolic array processor, are proposed. The QR-RLS based MVDR has many advantages, such as numerical stability, computational efficiency and pipelined structure in implementation. We also point out that the conventional method, MVDR using QR-RLS method by directly forcing the desired signal to zero is not correct. Numerical experiments are carried out to illustrate the effectiveness of the proposed method.

A blind cascade modular structure interference cancellation RAKE receiver is proposed to suppress multiple access interference for code division multiple access (CDMA) communications system in the multipath fading environment. In the receiver, blind constrained cascade modular structure detector is used in each arm of the RAKE receiver to extract part of the desired signal. In each module of the cascade modular structure detector, a vector filter is introduced for interference cancellation and the weight vector is determined based on maximum magnitude cross correlation criteria which can capture most of the interference presented in the output of preceding module. The output of all arms is coherent combined to enhance the desired signal for the final detection. The proposed receiver has simple structure and is robust for the multipath fading environment compared with other blind receivers. Performance of the receiver is characterized via computer simulation.

A fading channel identification and tracking method for orthogonal frequency division multiplexing (OFDM) communication system is proposed in this paper. The fading channel is modeled as an auto-regression (AR) process with unknown AR parameters. With the AR model of channel, a state-space representation of the the system is formulated. The system parameters are identified using subspace based method and Kalman filtering. With training symbols and feedback decisions, the system model is simplified to have fixed parameters contrast with other subspace based identification methods. Moreover, QR based subspace method is proposed to solve this problem efficiently. Numerical simulation results show that the proposed method can be applied in fast fading environment.

In this paper, we propose an iterative algorithm, which can achieve beampattern control and adaptive beamforming at the same time. Our proposed algorithm is computationally efficient because it avoids the calculation of matrix inverse which is often required by conventional beampattern synthesis algorithms. Besides this advantage, the new algorithm is superior in being able to shape the sidelobe region which is left uncontrolled by other adaptive beamforming algorithms. In addition, householder transform (HT) is applied to further reduce the computational load. Numerical results show that our proposed algorithm has the ability of nulling out interferences and maintaining a well-shaped beampattern simultaneously.

Weighted polynomial expansion (WPE) detectors can be used to approximate linear multiuser detectors to avoid the need to compute matrix inversion. For finite stage WPE detectors, weight vector optimization is an important process since it determines how effective the linear multiuser detectors can be approximated. In this letter, the optimal weight vector for the finite stage WPE detector is derived based on the mean-squared error (MSE) criteria. In addition, a low-complexity adaptive WPE detector based on the least-squares criteria is proposed to simplify the computation of the optimum weight vector. Simulation results show that the proposed adaptive WPE detector outperforms the existing WPE method.

In the past few years, many methods were proposed to model the so-called secondary path on line for an active noise control (ANC) system. In these methods, an auxiliary noise is employed to excite the modeling process. Among many factors to affect the performance of these methods, some play important roles. They are: 1) power of auxiliary noise; 2) ability to detect secondary path change; 3) optimal step sizes for adaptive filters used in an ANC system and 4) independence between ANC and secondary path modeling processes. In literature, however, there is lack of analysis to show how these factors affect the performance of the ANC system. Moreover, the aforementioned online secondary path modeling methods have not been systematically evaluated. In this paper, the performance of these methods is evaluated by investigating the above-mentioned factors through both statistical analysis and computer simulations.

Given the number of checking points, the speed of block motion estimation depends on how fast the block matching is. In this paper, a new framework, fine granularity successive elimination (FGSE), is proposed for fast optimal block matching in motion estimation. The FGSE features providing a sequence of nondecreasing fine-grained boundary levels to reject a checking point using as little computation as possible, where block complexity is utilized to determine the order of partitioning larger sub-blocks into smaller subblocks in the creation of the fine-grained boundary levels. It is shown that the well-known successive elimination algorithm (SEA) and multilevel successive elimination algorithm (MSEA) are just two special cases in the FGSE framework. Moreover, in view that two adjacent checking points (blocks) share most of the block pixels with just one pixel shifting horizontally or vertically, we develop a scheme to predict the rejection level for a candidate by exploiting the correlation of matching errors between two adjacent checking points. The resulting predictive FGSE algorithm can further reduce computation load by skipping some redundant boundary levels. Experimental results are presented to verify substantial computational savings of the proposed algorithm in comparison with the SEA/MSEA.

Weighted polynomial expansion (WPE) detector can be used to approximate linear multiuser detectors to avoid the computation of inverse of matrix. For finite stage WPE detector, weight vector optimum problem is very important to efficiently approximate linear multiuser detectors. In this paper, the optimal weight vector for the finite stage WPE detector is derived based on mean square error (MSE) criteria. And a low complexity adaptive WPE detector based on least square (LS) criteria is presented to simplify the computation of weight vector, because the direct computation of the optimal weight vector is complex in practical implementations. Simulation results show that the adaptive WPE detector can nearly achieve the performance of the exact optimal weight vector WPE detector with greatly reduced complexity.

In many practical cases for active noise control (ANC), the online secondary path modeling methods that use auxiliary noise are often applied. However, the auxiliary noise contributes to residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, a sudden and large change in the secondary path leads to easy divergence of the existing online secondary path modeling methods. To mitigate these problems, this paper proposes a new online secondary path modeling method with auxiliary noise power scheduling and adaptive filter norm manipulation. The auxiliary noise power is scheduled based on the convergence status of an ANC system with consideration of the variation of the primary noise. The purpose is to alleviate the increment of the residual noise due to the auxiliary noise. In addition, the norm manipulation is applied to adaptive filters in the ANC system. The objective is to avoid over-updates of adaptive filters due to the sudden large change in the secondary path and thus prevent the ANC system from diverging. Computer simulations show the effectiveness and robustness of the proposed method.

The use of orthogonal spreading codes has attracted much attention due to their ability to suppress interference from other users, compared with the nonorthogonal sequences in the synchronous case. In this paper, new sets of orthogonal sequences derived from the unified complex Hadamard transforms (UCHTs) are investigated. Various correlation properties of the sequences are mathematically derived and analyzed. It is shown that some UCHT sequences provide better autocorrelation properties than orthogonal Walsh-Hadamard sequences. Performance comparisons between UCHT sequences, Gold, small set of Kasami, and m-sequences show that some UCHT sequences outperform these well-known spreading sequences under simulation of systems in the presence of multiple access interference and additive white Gaussian noise.

In OFDM systems, when the cyclic prefix (CP) is shorter than the channel order, a time domain equalizer (TEQ) is needed to partially equalize the channel to constrain its impulse response to a predetermined length. This paper derives sufficient conditions under which the time domain equalization is optimum in producing maximum throughput. A new criterion of TEQ design is proposed under the guidance of these conditions. Simulation results show that the proposed method is able to produce a high throughput performance.

This work proposes a technique that improves fingerprint templates by merging and averaging minutiae of multiple fingerprints. The weighted averaging scheme enables the template to change gradually with time in line with changes of the skin and imaging conditions. The recursive nature of the algorithm greatly reduces the storage and computation requirements of this technique. As a result, the proposed template improvement procedure can be performed online during the fingerprint verification process. Extensive experimental studies demonstrate the feasibility of the proposed algorithm.

A weight-constrained filtered-x LMS (CFxLMS) algorithm is proposed for broadband feedforward active noise control system. Compared with the system using the normal FxLMS algorithm, the proposed system is more robust against interference to error microphone. Computer simulation results show the effectiveness of the algorithm.

In an active noise control (ANC) system using the filtered-x least mean square (FxLMS) algorithm, an online secondary path modeling method that uses an injected auxiliary noise is often applied. Such a method allows quick and full-band signal-independent modeling. In addition, it is suitable for multisecondary path modeling. Normally, the larger the auxiliary noise, the faster an accurate model can be obtained. However, it increases the residual noise of the ANC system. To mitigate this problem, in this letter, a new online secondary path modeling method is proposed. Rather than fixed, the power of auxiliary noise is varied according to the working status of the ANC system. More specifically, the auxiliary noise is large before the ANC system converges, and becomes small when the system converges. Computer simulations show its effectiveness and robustness.

Bark scale (also called critical band rate in the literature) has attracted increasing attention among audio engineers as a good measurement of the frequency resolving ability of the human auditory system. The warped filter technique has been applied to the Bark scale to construct an equalizer for a low-cost loudspeaker system. In a simulation, the resulting equalizer displays an overall better equalization effect than the conventional deconvolution-based equalizer.

A good active noise control (ANC) system with online secondary path modeling should have the property that the operation of the ANC controller and the modeling of the secondary path are mutually independent. A new ANC system with online secondary path modeling is presented. Three cross-updated least mean square (LMS) adaptive filters are used to reduce mutual disturbances between the operation of the ANC controller and the modeling of the secondary path. Computer simulations have been conducted and the results show that the proposed method is able to produce superior performance compared to existing methods.

The multiband approach in loudspeaker equalization has the advantage of being able to produce a very flat equalized response. However, its complex structure makes it impractical for low-cost loudspeaker implementation. In this paper, a tri-band equalization method is proposed. The method uses warped filter equalization to enhance the frequency resolution thereby reducing the need for having many equalization sub-bands. A technique has also been developed to adjust the warping effective region to a wider range. Computer simulation results show that the proposed method is able to improve the performance of the equalized response.

An algorithm for blind ISI cancellation in multi-user systems is developed in this paper. By minimizing the distance between the output of a filter and a reference signal with different time delay for two filters, the output of one filter turns out to be exactly equal to the ISI component of the output of the other filter. The difference between the output of these filters is the ISI-free estimate of the signal. With an appropriate selection of the time delay introduced for the reference signal, any coefficient of the channel impulse response which determines the scale of ISI-free estimate, can be anchored accordingly. Computer simulation has been conducted to illustrate the performance of this new method.

A method to improve the performance of the secondary path modeling for active noise control systems is proposed in this letter. The proposed method can further reduce the distortion introduced by the primary noise and the canceling signal to the secondary path modeling. Computer simulation shows that the proposed method can provide faster convergence and higher modeling accuracy.

Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy.

This paper presents an improved approach of minutiae detection that adaptively traces the gray level ridge of the originals fingerprint image with piecewise linear lines of different length. While tracing the ridges, the fingerprint image is smoothed with an oriented smoothing filter only at selected pixels where smoothing is necessary. After tracing all the ridges, a piece-wise linear skeleton image is obtained. Each ridge in the skeleton is labeled with a number so that each minutiae is associated with one or two ridge numbers. The post-processing is based not only on the location relationship of the minutiae, but also the associated ridge relationship and the certainty level of the minutiae. The performance of this approach is objectively assessed by using two large fingerprint databases.

We derive a series of upper and lower bounds for the minimum distance between two noise-free output sequences over unknown channels with fixed-energy finite-duration impulse responses. Using these bounds, we develop a method for determining the minimum distance. Such distance is crucial for determining the worst possible performance of maximum likelihood sequence estimation (MLSE).