Digital Signal Processing

Published by Elsevier
Online ISSN: 1095-4333
Publications
Article
The spectrum of the convolution of two continuous functions can be determined as the continuous Fourier transform of the cross-correlation function. The same can be said about the spectrum of the convolution of two infinite discrete sequences, which can be determined as the discrete time Fourier transform of the cross-correlation function of the two sequences. In current digital signal processing, the spectrum of the contiuous Fourier transform and the discrete time Fourier transform are approximately determined by numerical integration or by densely taking the discrete Fourier transform. It has been shown that all three transforms share many analogous properties. In this paper we will show another useful property of determining the spectrum terms of the convolution of two finite length sequences by determining the discrete Fourier transform of the modified cross-correlation function. In addition, two properties of the magnitude terms of orthogonal wavelet scaling functions are developed. These properties are used as constraints for an exhaustive search to determine an robust lower bound on conjoint localization of orthogonal scaling functions.
 
Conference Paper
Signal cancellation is a serious problem in adaptive nulling. The problem arises when the actual direction of arrival of the signal is slightly off the assumed direction of arrival. The adaptive algorithms consider the actual signal a jammer as the direction of arrival is not exactly specified. It is shown how to prevent signal cancellation when the direction of arrival is not known exactly. Multiple look-direction constraints are used to prevent signal cancellation. This paper outlines the principles and illustrates how it can be incorporated in an adaptive nulling situation using a deterministic direct data domain approach
 
Conference Paper
A new distributed Bragg reflector (DBR) laser with continuously and arbitrarily chirped gratings is theoretically analysed. The chirped gratings are defined by bent waveguides on homogeneous grating fields. The influence of the chirped gratings on tunability of multielectrode DBR lasers is presented via the study of different bending functions. It is theoretically shown that the tunability of these components can be improved using the appropriate chirping function
 
Conference Paper
This paper describes a parallel implementation of a Hidden Markov Model (HMM) for spoken language recognition on the MasPar MP-1. By exploiting the massive parallelism of explicit duration HMMs, we can develop more complex models for real-time speech recognition. Implementational issues such as choice of data structures, method of communication, and utilization of parallel functions are explored. The results of our experiments show that the parallelism in HMMs can be effectively exploited by the MP-1. Training that use to take nearly a week can now be completed in about an hour. The system can recognize the phones of a test utterance in a fraction of a second
 
Conference Paper
Summary form only given. The Rutgers University Center for Computer Aids for Industrial Productivity (CAIP) is one of several tightly focused advanced technology centers chartered by the New Jersey Commission on Science and Technology. CAIP's focus is industrial application of leading-edge computing technology. The Center is an industrial consortium. The budget supports about 70 researchers. Each member company has a representative on the Board of Directors for the Center. The Board meets quarterly to review the research program, to advise on industry needs, to ready paths to application for research, and to become familiar with graduate students completing their degree programs. The Center also works closely with new start-up companies to provide laboratory, computing, and consulting assistance in their initial phases. In its sixth year, the Center had placed over 60 advanced-degree graduates in its affiliated industries, helped to attract over ⊄ in funding for new start-up businesses, and continually moves relevant research results, some of which are covered by patents, to its member companies. The author reviews the structure and research activities of the Center, and describes several in-progress transfers of technology to industry
 
Conference Paper
We have studied the locking characteristics of semiconductor lasers through numerical calculation of the output intensity and change in carrier density of the slave laser during injection locking. We have also obtained the dynamic locking range by examining the roots of the secular determinant of the perturbed system. The lower boundaries of the static and dynamic locking ranges coincide, but the upper boundaries do not. Both the static and dynamic locking ranges are asymmetrical about zero detuning and dependent on injection ratio, linewidth enhancement factor and biasing condition. The upper boundary of the dynamically stable region exhibits an abrupt bend at a very low injection level. Unlike previous work, the locking characteristics at both low and high injection levels have been carefully studied
 
Article
Image enhancement is one of the most important issues in low-level image processing. Mainly, enhancement methods can be classified into two classes: global and local methods. In this paper, the multi-peak generalized histogram equalization (multi-peak GHE) is proposed. In this method, the global histogram equalization is improved by using multi-peak histogram equalization combined with local information. In our experiments, different local information is employed. Experimental results demonstrate that the proposed method can enhance the images effectively.
 
Article
Schmidt-Nielsen, Astrid, and Crystal, Thomas H., Speaker Verification by Human Listeners: Experiments Comparing Human and Machine Performance Using the NIST 1998 Speaker Evaluation Data, Digital Signal Processing10(2000), 249–266.The speaker verification performance of human listeners was compared to that of computer algorithms/systems. Listening protocols were developed to emulate as closely as possible the 1998 algorithm evaluation run by the U.S. National Institute of Standards and Technology (NIST), while taking into account human memory limitations. A subset of the target speakers and test samples from the same telephone conversation data was used. Ways of combining listener data to arrive at a group decision were explored, and the group mean worked well. The human results were very competitive with the best computer algorithms in the same handset condition. For same numbertesting, with 3-s samples, listener panels and the best algorithm had the same equal-error rate (EER) of 8%. Listeners were better than typical algorithms. For different numbertesting, EERs increased but humans had a 40% lower equal-error rate. Human performance in general seemed relatively robust to degradation.
 
Article
A careful comparison of three numeric techniques for estimation of the curvature along spatially quantized contours is reported. Two of the considered techniques are based on the Fourier transform (operating over 1D and 2D signals) and Gaussian regularization required to attenuate the spatial quantization noise. While the 1D approach has been reported before and used in a series of applications, the 2D Fourier transform-based method is reported in this article for the first time. The third approach, based on splines, represents a more traditional alternative. Three classes of parametric curves are investigated: analytical, B-splines, and synthesized in the Fourier domain. Four quantization schemes are considered: grid intersect quantization, square box quantization, a table scanner, and a video camera. The performances of the methods are evaluated in terms of their execution speed, curvature error, and sensitivity to the involved parameters. The third approach resulted the fastest, but implied larger errors; the Fourier methods allowed higher accuracy and were robust to parameter configurations. The 2D Fourier method provides the curvature values along the whole image, but exhibits interference in some situations. Such results are important not only for characterizing the relative performance of the considered methods, but also for providing practical guidelines for those interested in applying those techniques to real problems.
 
Article
This paper considers the identification problems of the Hammerstein nonlinear systems. A projection and a stochastic gradient (SG) identification algorithms are presented for the Hammerstein nonlinear systems by using the gradient search method. Since the projection algorithm is sensitive to noise and the SG algorithm has a slow convergence rate, a Newton recursive and a Newton iterative identification algorithms are derived by using the Newton method (Newton–Raphson method), in order to reduce the sensitivity of the projection algorithm to noise, and to improve convergence rates of the SG algorithm. Furthermore, the performances of these approaches are analyzed and compared using a numerical example, including the parameter estimation errors, the stationarity and convergence rates of parameter estimates and the computational efficiency.
 
Article
This paper proposes a computationally efficient method for estimating angle of arrival and polarization parameters of multiple farfield narrowband diversely polarized electromagnetic sources, using arbitrarily spaced electromagnetic vector sensors at unknown locations. The electromagnetic vector sensor is six-component in composition, consisting of three orthogonal electric dipoles plus three orthogonal magnetic loops, collocating in space. The presented method is based on an estimation method named propagator, which requires only linear operations but no eigenvalue decomposition or singular value decomposition into the signal and noise subspaces, to estimate the scaled electromagnetic vector sensors' steering vectors and then to estimate the azimuth arrival angle, the elevation arrival angle, and the polarization parameters. Comparing with its ESPRIT counterpart [K.T. Wong, M.D. Zoltowski, Closed-form direction finding and polarization estimation with arbitrarily spaced electromagnetic vector-sensors at unknown locations, IEEE Trans. Antennas Propagat. 48 (5) (2000) 671–681], the propagator method has its computational complexity reduced by this ratio: the number of sources to sextuple the number of vector sensors. Simulation results show that at high and medium signal-to-noise ratio, the proposed propagator method's estimation accuracy is similar to its ESPRIT counterpart.
 
Article
We introduce a new shrinkage scheme, hyper-trim that generalizes hard and soft shrinkage proposed by Donoho and Johnstone (1994). The new adaptive denoising method presented is based on Stein's unbiased risk estimation (SURE) and on a new class of shrinkage function. The proposed new class of shrinkage function has continuous derivative. The shrinkage function is simulated and tested with ECG signals added with standard Gaussian noise using MATLAB. This method gives better mean square error (MSE) performance over conventional wavelet shrinkage methodologies.
 
Article
Mamic, G., and Bennamoun, M., Representation and Recognition of 3D Free-Form Objects, Digital Signal Processing12 (2002) 47–76The problem of 3D object recognition has been one that has perplexed the computer vision community for the past two decades. This paper describes and analyzes techniques which have been developed for object representation and recognition. A set of specifications, which all object recognition systems should strive to meet, forms the basis upon which this critical review has been formulated. The literature indicates that there is a powerful requirement for a precise and accurate representation, which is simultaneously concise in nature. Such a representation must be relatively inexpensive and provide a means for determining the error in the surface fit such that the effects of error propagation may be analyzed in the system and appropriate confidence bounds determined in the subsequent pose estimation.
 
Article
Quality of service is a critical consideration in the design of mobile systems, since it allows the user to receive high quality services. Therefore, in 3GPP systems, in order to realise a particular service, the quality of service requirements in terms of performance and latency, have to be satisfied. Turbo code features include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving and an associated iterative decoding algorithm. Exploiting the quality of service classification according to the priority of latency or performance, possible examples of service scenarios are examined for flat Rayleigh fading channels with emphasis on the turbo decoding algorithm. Particularly, for two operating environments considering SOVA and log-MAP algorithms due to their data-flow similarities, this paper shows that SOVA is clearly optimal for most of the real-time applications, whereas for nonreal time applications with low data rate and small frames log-MAP is preferred. The use of the optimum algorithm in most scenarios results in a more efficient turbo decoder: applications that otherwise would have failed now can be realised.
 
Article
AC power lines have been considered as a convenient and low-cost medium for intra-building automation systems. In this paper, we investigate the problem of estimating the channel order and root mean squared (RMS) delay spread associated with the power lines, which are channel parameters that provide important information for determining the data transmission rate and designing appropriate equalization techniques for power lines communications (PLC). We start by showing that the key to the RMS delay spread estimation problem is the determination of the channel order, i.e., the effective duration of the channel impulse response. We next discuss various ways to estimate the impulse response length from a noise-corrupted channel estimate. In particular, four different methods, namely a signal energy estimation (SEE) technique, a generalized Akaike information criterion (GAIC) based test, a generalized likelihood ratio test (GLRT), and a modified GLRT, are derived for determining the effective length of a signal contaminated by noise. These methods are compared with one another using both simulated and experimentally measured power line data. The experimental data was collected for power line characterization in frequencies between 1 and 60 MHz.
 
Article
A problem of accelerometer and gyroscope signals' filtering is discussed in the paper. Triple-axis accelerometer and three single-axis gyroscopes are the elements of strapdown system measuring head. Effective noise filtration impacts on measured signal reliability and the computation precision of moving object position and orientation. The investigations were carried out to apply Kalman filter in a real-time application of acceleration and angular rate signals filtering. The filter parameter adjusting is the most important task of the investigation, because of unknown accuracy of the measuring head and unavailability of precisely known model of the system and the measurement. Results of calculations presented in the paper describe relation between filter parameters and two assumed criterions of filtering quality: output signal noise level and filter response rate. The aim of investigation was to achieve and find values of the parameters which make Kalman filter useful in the real-time application of acceleration and angular rate signals filtering.
 
Article
In this paper, we introduce a new approach to the method of non-parametric adaptive spectral analysis by using the Amplitude and Phase Estimation (APES) method, and taking into account the small sample errors of the sample covariance matrix. This approach is referred to as Adaptive Tuning Amplitude and Phase Estimation method (ATAPES). The main advantage of the ATAPES algorithm is its elimination of biased estimation exists with APES method, which is a biased peak location and corresponding problem of the biased amplitude estimation. The ATAPES method provides more accurate peak location and amplitude estimation with higher resolution than APES method.
 
Article
This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.
 
Article
In MIMO systems, channel estimation is important to distinguish the transmitted signals from multiple transmit antennas. When MIMO systems are introduced in cellular systems, we have to measure the received power from all the connectable base station (BS), as well as to distinguish all the channel state information (CSI) for the combination of transmitter and receiver antenna elements. One of the most typical channel estimation schemes for MIMO in a cellular system is to employ a code division multiplexing (CDM) scheme in which a unique spreading code is assigned to distinguish both BS and MS antenna elements. However, by increasing the number of transmit antenna elements, large spreading codes and pilot symbols are required to estimate an accurate CSI. To reduce this problem, in this paper, we propose a high time resolution carrier interferometry (HTRCI) for MIMO/OFDM to achieve an accurate CSI without increasing the number of pilot symbols.
 
Article
The main motivation of using an acoustic vector-sensor in direction-of-arrival (DOA) estimation applications has been its unambiguous two-dimensional directivity, insensitivity to the range of sources, and independence of signal frequency. The main objection lies in its lack of geometry-redundancy and limited degree of freedom. Four thus emerged challenging tasks and the corresponding solutions by recurring to the redundancies in the nonvanishing conjugate correlations of noncircular signals are described in the paper: (1) fulfilling source decorrelation in a multipath propagation environment; (2) enhancing processing capacity to accommodate more signals; (3) suppressing colored-noise with unknown covariance structure; and (4) deriving closed-form approaches to avoid iteration and manifold storage. Simulation experiments are carried out to examine the associated DOA estimators termed as: (1) phase-smoothing MUSIC (multiple signal classification); (2) virtual-MUSIC; (3) conjugate-MUSIC; and (4) noncircular-ESPRIT (estimation of signal parameters via rotational invariance techniques), respectively.
 
Article
This paper proposes a new underwater acoustic 2-D direction finding algorithm using two identically oriented vector hydrophones at unknown locations in non-Gaussian impulsive noise. The two applied vector hydrophones are four-component, orienting identically in space with arbitrarily and possibly unknown displacement. Each vector hydrophone has three spatially co-located but orthogonally oriented velocity hydrophones plus another pressure hydrophone. The proposed algorithm employs the spatial invariance between the two vector hydrophones, but requires no a priori information of vector hydrophones' spatial factors and impinging sources' temporal forms. We apply ESPRIT to estimate vector hydrophones manifold and then to pair the x-axis direction cosines with y-axis direction cosines automatically and yield azimuth and elevation angle estimates. We also consider the additive noise be non-Gaussian impulsive, which is often encountered in underwater acoustics applications. Two typical impulsive noise model, Gaussian-mixture noise and symmetric α-stable (SαS) noise models are adopted. Instead of using conventional second order correlation of array output data, we define the vector hydrophone array sign covariance matrix (VSCM) for Gaussian-mixture noise and vector hydrophone array fractional lower order moment (VFLOM) matrix for SαS noise with 1<α⩽2. These defined matrices may readily substitute customary vector hydrophone array covariance matrix for 2-D direction finding in impulsive noise.
 
Article
A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels.
 
Article
In this study, new neural network models with adaptive activation function (NNAAF) were implemented to classify ECG arrhythmias. Our NNAAF models included three types named as NNAAF-1, NNAAF-2 and NNAAf-3. Activation functions with adjustable free parameters were used in hidden neurons of these models to improve classical MLP network. In addition, these three NNAAF models were compared with the MLP model implemented in similar conditions. Ten different types of ECG arrhythmias were selected from MIT–BIH ECG Arrhythmias Database to train NNAAFs and MLP models. Moreover, all models tested by the ECG signals of 92 patients (40 males and 52 females, average age is 39.75±19.06). The average accuracy rate of all models in the training processing was found as 99.92%. The average accuracy rate of the all models in the test phases was obtained as 98.19.
 
Article
Pharmacological FMRI in humans involves BOLD signal acquisition before, during and after the administration of a drug, and often results in a heterogeneous pattern of drug-induced hemodynamic responses in the brain. Exploratory techniques, including blind source separation, can be useful for BOLD data that contains patterns of cross-dependencies. Bayesian source separation (BSS) is a multivariate technique used to calculate the presence of unobserved signal sources in measured FMRI data, as well as the covariance between data voxels and between reference waveforms. Unlike conventional univariate regression analysis, BSS does not assume independence between voxel time series or source components. In this study, BOLD measurement of the acute effect of an intravenous dose of cocaine, a substance shown previously to engage multiple sites within the orbitofrontal cortex, was processed with BSS. The utility of BSS in pharmacological FMRI applications was demonstrated in multiple examples featuring single-ROI, multiple-ROI and whole-slice data. The flexibility of the BSS technique was shown by choosing different modeling strategies to form the prior reference functions, including approximating the pharmacokinetics of cocaine, interpolating simultaneously measured behavioral data and using observed BOLD responses from known subcortical afferents to the cortex of interest.
 
Article
In nonparametric local polynomial regression the adaptive selection of the scale parameter (window size/bandwidth) is a key problem. Recently new efficient algorithms, based on Lepski's approach, have been proposed in mathematical statistics for spatially adaptive varying scale denoising. A common feature of these algorithms is that they form test-estimates different by the scale h∈H and special statistical rules are exploited in order to select the estimate with the best pointwise varying scale. In this paper a novel multiresolution (MR) local polynomial regression is proposed. Instead of selection of the estimate with the best scale h a nonlinear estimate is built using all of the test-estimates . The adaptive estimation consists of two steps. The first step transforms the data into noisy spectrum coefficients (MR analysis). On the second step, this noisy spectrum is filtered by the thresholding procedure and used for estimation (MR synthesis).
 
Article
Reynolds, Douglas A., Quatieri, Thomas F., and Dunn, Robert B., Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing10(2000), 19–41.In this paper we describe the major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented.
 
Article
As one of the important topics in computer vision, moving vehicle segmentation has attracted considerable attention of researchers. However, robust detection is hampered by the interferential moving objects in dynamic scenes. In this paper, we address the problem of the moving vehicles segmentation in the dynamic scenes. Based on the distinct motion property of the dynamic background and that of the moving vehicles, we present an adaptive motion histogram for moving vehicles segmentation. The presented algorithm consists of two procedures: adaptive background update and motion histogram-based vehicles segmentation. In the adaptive background update procedure, we make use of the lighting change of the scene and present a novel method for background evolving. In the motion histogram-based vehicles segmentation procedure, an adaptive motion histogram is maintained and updated according to the motion information in the scenes, and the moving vehicles are then detected according to the motion histogram maintained. Experimental results of typical scenes demonstrate robustness of the proposed method. Quantitative evaluation and comparison with the existing methods show that the proposed method provides much improved results.
 
Article
This paper presents a secure, robust, and blind adaptive audio watermarking algorithm based on singular value decomposition (SVD) in the discrete wavelet transform domain using synchronization code. In our algorithm, a watermark is embedded by applying a quantization-index-modulation process on the singular values in the SVD of the wavelet domain blocks. The watermarked signal is perceptually similar to the original audio signal and gives high quality output. Experimental results show that the hidden watermark data is robust to additive noise, resampling, low-pass filtering, requantization, MP3 compression, cropping, echo addition, and denoising. Performance analysis of the proposed scheme shows low error probability rates. The data embedding rate of the proposed scheme is 45.9 bps. The proposed scheme has high payload and superior performance against MP3 compression compared to the earlier audio watermarking schemes.
 
Article
Chen, S., Istepanian, R., and Luk, B. L., Digital IIR Filter Design Using Adaptive Simulated Annealing, Digital Signal Processing11 (2001) 241–251Adaptive infinite-impulse-response (IIR) filtering provides a powerful approach for solving a variety of practical problems. Because the error surface of IIR filters is generally multimodal, global optimization techniques are required in order to avoid local minima. We apply a global optimization method, called the adaptive simulated annealing (ASA), to digital IIR filter design. An important advantage of the ASA is the simplicity in software programming. Simulation study involving system identification application shows that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the ASA offers a viable tool to digital IIR filter design.
 
Article
The standard mixed-norm filtering algorithm exhibits slow convergence in stable distribution environment, requires a stationary operating environment, and employs a constant step-size that needs to be determined a priori. We proposed a new adaptive mixed moments filtering algorithm based on SαSG (symmetry α-stable Gaussian) noise model. The simulation experiments show that the proposed algorithm exhibits increased convergence rate and stability performance than the conventional mixed-norm algorithm.
 
Article
Fast time (range delay time) information has been well exploited for the terrain scattered interference or hot clutter mitigation. In this paper, an approach incorporating fast time in space–time adaptive processing (STAP) is introduced for monostatic (cold) clutter rejection. This method takes advantage of the coherence information of adjacent range bins. Compared to traditional STAP algorithms, the performance of monostatic clutter mitigation can be improved due to the fact that the additional range degree of freedom (DoF) can mitigate various deleterious factors in realistic scenarios which may increase the DoFs of clutter patches. Results of real measured data processing using airborne X-band and MCARM radar systems demonstrate the effectiveness of the proposed processor.
 
Article
Khuwaja, G. A., An Adaptive Combined Classifier System for Invariant Face Recognition, Digital Signal Processing12 (2002) 21–46In classification tasks it may be wise to combine observations from different sources. In this paper, to obtain classification systems with both good generalization performance and efficiency in space and time, a learning vector quantization learning method based on combinations of weak classifiers is proposed. The weak classifiers are generated using automatic elimination of redundant hidden layer neurons of the network on both the entire face images and the extracted features: forehead, right eye, left eye, nose, mouth, and chin. The neuron elimination is based on the killing of blind neurons, which are redundant. The classifiers are then combined through majority voting on the decisions available from input classifiers. It is demonstrated that the proposed system is capable of achieving better classification results with both good generalization performance and a fast training time on a variety of test problems using a large and variable database. The selection of stable and representative sets of features that efficiently discriminate between faces in a huge database is discussed.
 
Article
This paper reviews a technique of adaptive wavelet expansions and introduces the novel concept of “biased wavelets.” These are functions that are localized in time and in frequency but, unlike conventional wavelets, have an adjustable nonzero mean component. Under mild conditions, it is shown that a conventional mother wavelet can be used to construct a family of biased wavelets which spans the set of finite-energy functions L2(). Numerical tests suggest that the introduction of the adjustable “bias” considerably improves the representation capabilities of wavelet expansions. A problem of electrocardiographic data compression is used for illustration purposes. Test signals were extracted from the MIT–BIH ECG Compression Test Database.
 
Article
The code-division multiple-access (CDMA) multiple-input multiple-output (MIMO) systems with Alamouti's space–time block codes (STBC) have been widely investigated for the great demand of capacities and transmission rates in the future wireless communication systems. The semi-blind Capon multiuser receiver is designed to achieve the goal by the usage of linearly constrained minimum variance (LCMV) filters. However, the Capon receiver suffers finite samples and signature vector errors. In this paper, we propose a novel two-stage receiver containing partially adaptive linear filters in parallel followed by the channel estimator and symbol detector. Two transformation schemes based on subspace techniques are designed in the partially adaptive linear filters. The channel estimation is executed after the partially adaptive linear filter where the computational complexity is saved considerably. A performance enhancement method is introduced by using the forward–backward averaging technique. According to the weighting analysis, we also present a simplified two-stage receiver with lower complexity. Furthermore, the diagonal-loading (DL) technique based on the worse-case performance optimization is often used to alleviate the drawbacks of Capon receiver. In the end, we apply the proposed the forward–backward averaging technique and the weighting analysis onto the DL-based Capon receivers. Computer simulations are given to demonstrate the effectiveness of the two-stage partially adaptive linear receivers for the CDMA MIMO systems with Alamouti's STBC schemes and the performance improvement of the DL-based Capon receivers with the forward–backward averaging technique and the weighting analysis.
 
Article
Karystinos, G. N., Qian, H., Medley, M. J., and Batalama, S. N., Short Data Record Adaptive Filtering: The Auxiliary-Vector Algorithm, Digital Signal Processing12 (2002) 193–222Based on statistical conditional optimization criteria, we developed an iterative algorithm that starts from the matched filter (or constraint vector) and generates a sequence of filters that converges to the minimum variance distortionless response (MVDR) solution for any positive definite input autocorrelation matrix. Computationally, the algorithm is a simple recursive procedure that avoids explicit matrix inversion, decomposition, or diagonalization operations. When the input autocorrelation matrix is replaced by a conventional sample-average (positive definite) estimate, the algorithm effectively generates a sequence of MVDR filter estimators: The bias converges rapidly to zero and the covariance trace rises slowly and asymptotically to the covariance trace of the familiar sample matrix inversion (SMI) estimator. For short data records, the early, nonasymptotic, elements of the generated sequence of estimators offer favorable bias–covariance balance and are seen to outperform in mean-square estimation error constraint-LMS, RLS-type, and orthogonal multistage decomposition estimates (also called nested Wiener filters) as well as plain and diagonally loaded SMI estimates. The problem of selecting the most successful (in some appropriate sense) filter estimator in the sequence for a given data record is addressed and two data-driven selection criteria are proposed. The first criterion minimizes the cross-validated sample average variance of the filter estimator output. The second criterion maximizes the estimated J-divergence of the filter estimator output conditional distributions. Illustrative interference suppression examples drawn from the communications literature are followed throughout this presentation.
 
Article
The paper discusses an adaptive multiuser receiver for CDMA systems in which the scaled unscented filter (SUF) and the square root unscented filter (SURF) are used for joint estimation and tracking of the code delays and multipath coefficients of the received CDMA signals. The proposed channel estimators are more near-far resistant than the conventional extended Kalman filter (EKF) and present lower complexity than the conventional particle filter (PF) based methods. To present meaningful performance measures, the modified Cramer–Rao lower bound (CRLB) and computational complexity metrics are derived for the proposed and existing channel estimators. Computer simulation results demonstrate the superior performance of the proposed channel estimators. The proposed estimators are also shown to exhibit lower complexity relative to the PF.
 
Article
This paper presents a new artificial intelligent based neuro-fuzzy rule base adaptive median filter for removing highly impulse noise. Since the filter is rule base, it is called neuro-fuzzy rule base adaptive median (NFRBAM) filter. The NFRBAM filter is an improved version of switch mode fuzzy adaptive median filter (SMFAMF) and is presented for the purpose of noise reduction of images corrupted with additive impulse noise. The NFRBAM filter consists of a decision unit and three different types of filters. In the decision unit, the noisy input image is directed to the proper filter with respect to the noise density. Neuro-fuzzy rule based approach is used in both decision and filtering parts. In artificial neural network, multi layer perceptron (MLP) architecture with backpropagation (BP) algorithm is used for noise detection and removing highly impulse noise corrupted MR images. In fuzzy logic, bell-shaped membership function is employed in order to obtain better results. Experimental results indicate that the proposed filter is improvable with the increased fuzzy rules to reduce more noise corrupted images and preserve image details more than SMFAMF.
 
Article
This paper considers interference limited communication systems where the desired user and interfering users are symbol-synchronized. A novel adaptive beamforming technique is proposed for quadrature phase shift keying (QPSK) receiver based directly on minimizing the bit error rate. It is demonstrated that the proposed minimum bit error rate (MBER) approach utilizes the system resource (antenna array elements) more intelligently, than the standard minimum mean square error (MMSE) approach. Consequently, an MBER beamforming assisted receiver is capable of providing significant performance gains in terms of a reduced bit error rate over an MMSE beamforming one. A block-data based adaptive implementation of the theoretical MBER beamforming solution is developed based on the classical Parzen window estimate of probability density function. Furthermore, a sample-by-sample adaptive implementation is also considered, and a stochastic gradient algorithm, called the least bit error rate, is derived for the beamforming assisted QPSK receiver.
 
Article
To recover the desired signals without the knowledge of its direction of arrival for a wideband array system is a difficult problem and a direct application of the convolutive blind source separation (BSS) algorithms will not work even for a small number of sensors. In this paper, a novel approach is proposed to solve this blind adaptive wideband beamforming problem based on a uniform circular array. The received array signals are first transformed into different phase modes and each phase mode output is then processed by a filter to achieve a frequency independent response. As a result, a set of instantaneous mixtures of the original source signals is obtained and the original wideband beamforming problem can be readily solved using the standard instantaneous BSS algorithms.
 
Article
We present several adaptive beamforming approaches to improve the quadrupole resonance (QR) signal detection performance via exploiting both the spatial and temporal correlation of radio frequency interferences (RFIs). We operate in the framework of signal amplitude estimation with known signal waveform and make use of three adaptive beamforming approaches, viz., the standard Capon beamformer (SCB), the robust Capon beamformer (RCB), and the amplitude and phase estimation (APES) algorithm, to develop several new approaches for mitigating the spatially and temporally correlated RFIs. Simulated and experimental results are provided to demonstrate the effectiveness of the proposed approaches.
 
Article
A new LMS algorithm is introduced for improved performance when a sinusoidal input signal is corrupted by correlated noise. The algorithm is based on shaping the frequency response of the transversal filter. This shaping is performed on-line by the inclusion of an additional term similar to the leakage factor in the adaptation equation of leaky LMS. This new term, which involves the multiplication of the filter coefficient vector by a matrix, is calculated in an efficient manner using the FFT. The proposed adaptive filter is shown analytically to converge in the mean and mean-square sense. The filter is also analyzed in the steady state in order to show the frequency-response-shaping capability. Simulation results illustrate that the performance of the frequency-response-shaped LMS (FRS-LMS) algorithm is very effective even for highly correlated noise.
 
Article
Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose (sugar). Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. In this paper, we have detected on diabetes disease, which is a very common and important disease using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS). The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS. The proposed system has two stages. In the first stage, dimension of diabetes disease dataset that has 8 features is reduced to 4 features using principal component analysis. In the second stage, diagnosis of diabetes disease is conducted via adaptive neuro-fuzzy inference system classifier. We took the diabetes disease dataset used in our study from the UCI (from Department of Information and Computer Science, University of California) Machine Learning Database. The obtained classification accuracy of our system was 89.47% and it was very promising with regard to the other classification applications in literature for this problem.
 
Article
Bose, T., Venkatachalam, A., and Thamvichai, R., Multiplierless Adaptive Filtering, Digital Signal Processing12 (2002) 107–118When digital filters are designed with power-of-2 coefficients, the multiplications can be implemented by simple shifting operations. For VLSI implementations, multiplierless filters are faster and more compact than filters with multipliers. In this paper, an algorithm for finding and updating the power-of-2 coefficients of an adaptive filter is designed. The new method uses the well-known Genetic Algorithm (GA) for this purpose. The GA is used in a unique way in order to reduce computations. Small blocks of data are used for the GA and only one new generation is produced per sample of data. This, coupled with the fact that the coefficients are power-of-2, yields a computational complexity of O(N) additions and no multiplications. The algorithm is investigated for applications in adaptive linear prediction and system identification. The results are very promising and illustrate the performance of the new algorithm.
 
Article
The paper deals with the problem of adaptive array beamforming using a uniform circular array (UCA) in the presence of coherent interference. The well-known scheme of spatial smoothing (SS) widely used to tackle the coherent problem in the cases of 1-D uniform linear array (ULA) and uniform rectangular array (URA) cannot work for the UCA case. This is mainly due to the fact that the direction vector of each signal received by a UCA does not possess a Vandermonde structure. In this paper, we present a method based on subarray beamforming in conjunction with an averaging scheme to effectively mitigate the effect due to coherent interference in the UCA case. Theoretical analysis regarding the validity of the proposed method is also presented. Several simulation examples are provided for showing the effectiveness of the proposed method.
 
Article
We consider the almost sure convergence of the adaptive IIR filter based on the output error method. It is rigorously shown that the algorithm is globally stable, parameter estimation is consistent, and perfect noise cancellation is achieved in the presence of nonstationary colored noise. Due to the complexity of the analysis, we consider the scalar case, i.e., a one-pole IIR filter.
 
Article
In this paper, we present a novel modification to the standard particle swarm optimization (PSO) technique and illustrate the superiority of the proposed modified technique over other PSO-based techniques, with an application to the important area of adaptive channel equalization. Different published versions of the original PSO algorithms are first reviewed and the new proposed technique discussed in the context of the design of adaptive channel equalizers. An exhaustive simulation-based sensitivity analysis of the proposed PSO algorithm, with respect to its underpinning parameters, is carried out here so as to select the “best” (or near optimal) values of these parameters. The performance of various PSO algorithms, including our proposed algorithm, is compared in the context of adaptive channel equalization to that of the LMS algorithm through extensive simulations. This detailed comparison revealed the superior performance of our proposed PSO-based adaptive channel equalizer over both its LMS-based counterpart and other adaptive equalizers based on the published PSO algorithms. This superior performance was exhibited on both linear and nonlinear channels.
 
Article
Two new adaptive second-order infinite impulse response (IIR) notch filters with fast convergence rate, accurate estimation of notch frequency, and modest realization complexity are proposed in this paper. It is proven that the optimum filter coefficient can be determined by using the minimum output energy (MOE) criterion. Based on it, a simplified equivalent adaptive system is derived, and two recursive least squares (RLS)-like algorithms are presented to adaptively estimate the filter coefficient. Extensive simulations show the effectiveness and robustness of the new adaptive IIR notch filters.
 
Article
This paper deals with high-resolution radar (HRR) adaptive detection of range-distributed target embedded in compound-Gaussian clutter which is modeled as a spherically invariant random process (SIRP). Using multiple dominant scattering (MDS) model of range-distributed target, we justify that range-distributed target can be modeled as a subspace random signal. The unknown deterministic parameters are replaced by their ML estimates and then the nonadaptive detector is proposed. A closed-form expression for the probability of false alarm of the nonadaptive detector is derived and it ensures CFAR property with respect to the unknown statistics of the clutter texture component. Moreover, an adaptive detector is obtained relying on a two-step GLRT-based design procedure. Performances of these proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detector.
 
Article
There are three types of vibration noise in the air-conditioner electromotor. In order to get the reliable and accurate feature frequencies of each type of vibration noise, generally several spectra of same type of noise should be processed and the final feature frequencies are extracted and fused from them. Some principles of adaptive resonance theory being referred to, an algorithm is proposed to realize this object. In this algorithm several normalization computations, one threshold function and some positive feedback computations of spectrum vector form a close loop, simultaneously the fusion computation of different spectra is also embedded in this loop, in the course of loop computation, the feature frequencies can be strengthened while those interference frequencies can be greatly suppressed, ultimately a reliable and accurate feature spectrum can be formed automatically from many different spectra. The four spectra of each type of vibration noise of electromotor are processed and the results show that the feature frequencies of each type of vibration noise are extracted reliably and accurately by this algorithm.
 
Article
In this paper, a novel fuzzy adaptive median filter is presented for the noise reduction in MR images corrupted with heavy impulse (salt&pepper) noise. We propose a switch mode fuzzy adaptive median filter (SMFAMF) for removing highly corrupted salt&pepper noise without destroying edges and details in the image. The SMFAMF filter is an improved version of adaptive median filter (AMF) in order to reduce additive impulse noise in the images. The proposed filter can preserve details in the images better than AMF while suppressing additive salt&pepper or impulse type noises. In this paper, we placed our preference on bell-shaped membership function with adaptive parameters instead of triangular membership function without variable coefficients in order to observe better results. Experiments with the magnetic resonance (MR) image from healthy subject, an MR image having the opaque material, and an MR image having disease demonstrate the mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR) of the proposed method. The results show that the proposed method can be useful for MR images with impulse type noises.
 
Top-cited authors
Ram Bilas Pachori
  • Indian Institute of Technology Indore
Feng Ding
  • Jiangnan University
Pradip Sircar
  • Indian Institute of Technology Kanpur
Boualem Boashash
  • Qatar University
Bee Ee Khoo
  • Universiti Sains Malaysia