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Abstract This paper deals with signal processing theory related to finite impulse response (FIR) filters with linear phase. The aim is to show a new and complete proof of the equivalence between linear phase and symmetry or antisymmetry of the real coefficients of the filter. Despite numerous proofs are available in the literature, they are usually incomplete, even though the result is commonly used by the signal processing community. We hereby address a pending issue in digital signal processing: we first prove the uniqueness of the group delay for any decomposition amplitude-phase of the frequency response. Based on this first step, we then derive a complete proof of the equivalence: a FIR filter has (anti)symmetric coefficients if and only if the phase is linear. It must be emphasized that this brief paper deals with theoretical aspects of FIR filters. Highlights • The paper deals with (anti)symmetric FIR filter with linear phase. • A complete equivalence between symmetry and phase linearity is theoretically proved. • This paper closes the theoretical concept behind FIR filter with linear phase. • A proof of uniqueness of the group delay is also provided. [ABSTRACT FROM AUTHOR]

In the context of automatic speech recognition (ASR) systems, the front-end acoustic features should not be affected by signal periodicity (pitch period). Motivated by this fact, we have studied the role of pitch-synchronous spectrum estimation approach, referred to as TANDEM STRAIGHT, in this paper. TANDEM STRAIGHT results in a smoother spectrum devoid of pitch harmonics to a large extent. Consequently, the acoustic features derived using the smoothed spectra outperform the conventional Mel-frequency cepstral coefficients (MFCC). The experimental evaluations reported in this paper are performed on speech data from a wide range of speakers belonging to different age groups including children. The proposed features are found to be effective for all groups of speakers. To further improve the recognition of children's speech, the effect of vocal-tract length normalization (VTLN) is studied. The inclusion of VTLN further improves the recognition performance. We have also performed a detailed study on the effect of speaking-rate normalization (SRN) in the context of children's speech recognition. An SRN technique based on the anchoring of glottal closure instants estimated using zero-frequency filtering is explored in this regard. SRN is observed to be highly effective for child speakers belonging to different age groups. Finally, all the studied techniques are combined for effective mismatch reduction. In the case of children's speech test set, the use of proposed features results in a relative improvement of 21.6% over the MFCC features even after combining VTLN and SRN. [ABSTRACT FROM AUTHOR]

Due to the huge computation complexity of Dezert–Smarandache Theory (DSmT), its applications especially for multi-source (more than two sources) complex fusion problems have been limited. To get high similar approximate reasoning results with Proportional Conflict Redistribution 6 (PCR6) rule in DSmT framework (DSmT + PCR6) and remain less computation complexity, an Evidence Clustering DSmT approximate reasoning method for more than two sources is proposed. Firstly, the focal elements of multi evidences are clustered to two sets by their mass assignments respectively. Secondly, the convex approximate fusion results are obtained by the new DSmT approximate formula for more than two sources. Thirdly, the final approximate fusion results by the method in this paper are obtained by the normalization step. Analysis of computation complexity show that the method in this paper cost much less computation complexity than DSmT + PCR6. The simulation experiments show that the method in this paper can get very similar approximate fusion results and need much less computing time than DSmT + PCR6, especially, when the numbers of sources and focal elements are large, the superiorities of the method are remarkable. [ABSTRACT FROM AUTHOR]

In this paper, an asymptotically optimal restricted uniform polar quantizer (RUPQ) is designed for a Gaussian source subject to the mean-squared error (MSE) criterion. The asymptotic analysis of the RUPQ, provided in the paper, for the first time includes the overload distortion. This enables derivation of the closed-form formulas for the straightforward design of the asymptotically optimal RUPQ. In particular, the closed-form formulas are derived for the asymptotically optimal support limit of the magnitude quantizer and asymptotically optimal rate allocation between the magnitude and phase quantizers. Moreover, our analysis shows that the overload distortion of the asymptotically optimal RUPQ cannot be neglected for rates R ≤ 5.5 bit/sample in cases when the relative error in calculating signal to quantization noise ratio (SQNR) due to overload distortion neglecting ranges up to 1%. Further, it is shown that the closed-form SQNR formula derived for the asymptotically optimal RUPQ, useful for performance assessment, is reasonably accurate for rates R > 3.5 bit/sample . Results obtained analytically and verified through simulations show that the proposed RUPQ outperforms existing RUPQs in terms of SQNR. The performances near the optimum along with the simple design of the proposed RUPQ model provide its application in various systems such as cellular systems and ultrasound medical systems, in digital spectrum analyzer and in digital processing of orthogonal frequency division multiplexing (OFDM) modulated signals. [ABSTRACT FROM AUTHOR]

This paper presents the application of a radically novel approach, called the Microcanonical Multiscale Formalism (MMF) to speech analysis. MMF is based on precise estimation of local scaling parameters that describe the inter-scale correlations at each point in the signal domain and provides efficient means for studying local non-linear dynamics of complex signals. In this paper we introduce an efficient way for estimation of these parameters and then, we show that they convey relevant information about local dynamics of the speech signal that can be used for the task of phonetic segmentation. We thus develop a two-stage segmentation algorithm: for the first step, we introduce a new dynamic programming technique to efficiently generate an initial list of phoneme-boundary candidates and in the second step, we use hypothesis testing to refine the initial list of candidates. We present extensive experiments on the full TIMIT database. The results show that our algorithm is significantly more accurate than state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a review in the form of a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The paper formalizes our developments in the area of DSP with SVM principles. The use of SVMs for DSP is already mature, and has gained popularity in recent years due to its advantages over other methods: SVMs are flexible non-linear methods that are intrinsically regularized and work well in low-sample-sized and high-dimensional problems. SVMs can be designed to take into account different noise sources in the formulation and to fuse heterogeneous information sources. Nevertheless, the use of SVMs in estimation problems has been traditionally limited to its mere use as a black-box model. Noting such limitations in the literature, we take advantage of several properties of Mercerʼs kernels and functional analysis to develop a family of SVM methods for estimation in DSP. Three types of signal model equations are analyzed. First, when a specific time-signal structure is assumed to model the underlying system that generated the data, the linear signal model (so-called Primal Signal Model formulation) is first stated and analyzed. Then, non-linear versions of the signal structure can be readily developed by following two different approaches. On the one hand, the signal model equation is written in Reproducing Kernel Hilbert Spaces (RKHS) using the well-known RKHS Signal Model formulation, and Mercerʼs kernels are readily used in SVM non-linear algorithms. On the other hand, in the alternative and not so common Dual Signal Model formulation, a signal expansion is made by using an auxiliary signal model equation given by a non-linear regression of each time instant in the observed time series. These building blocks can be used to generate different novel SVM-based methods for problems of signal estimation, and we deal with several of the most important ones in DSP. We illustrate the usefulness of this methodology by defining SVM algorithms for linear and non-linear system identification, spectral analysis, non-uniform interpolation, sparse deconvolution, and array processing. The performance of the developed SVM methods is compared to standard approaches in all these settings. The experimental results illustrate the generality, simplicity, and capabilities of the proposed SVM framework for DSP. [Copyright &y& Elsevier]

Abstract: In this paper, we present a class of low-complexity decimation filters for oversampled discrete-time signals. The proposed class of filters improves the frequency response of classical comb filters in two respects. First, it introduces extra-attenuation around the so-called folding bands, i.e., frequency intervals whose spurious signals are folded down to baseband during the decimation process. Second, this class reduces the passband distortion via an effective droop-compensator block, thus increasing the passband of the decimation filters. Like comb filters, the proposed class can be realized through multiplierless architectures, which are also discussed thoroughly in the paper. Unlike comb filters, the proposed filters have superior spurious signal rejection and a greatly reduced droop in the signal passband. These features make the proposed filters suitable for multistage decimation applications, such as reconfigurable software radio receivers, as well as for decimating oversampled digital signals produced by ΣΔ A/D converters. The paper discusses several useful techniques for designing the proposed filters in a variety of architectures with emphasis on non-recursive architectures. Design examples are discussed to highlight the key frequency features along with implementation issues aimed at reducing the computational complexity of the filters. [Copyright &y& Elsevier]

Abstract: The aim of this paper is the presentation of the designing of asymptotic unrestricted polar quantizers with square cells, since it is known that square cells give minimal moment of inertia, which leads to minimization of distortion. Until now, polar quantizers with square cells have been designed only for the optimal companding function and their performances have been analyzed only for the stationary variance. In this paper, the design is done in a general manner, i.e. it is valid for any companding function and performances are analyzed in the wide range of variances. After that, this general design is applied for the logarithmic μ-law companding function. It is important that expressions for the numbers of magnitude and phase levels are obtained in the closed form, which simplifies the design. It is shown that the proposed polar quantizer has better performances (more than 3 dB higher the maximal and the average SQNR (signal-to-quantization noise ratio)) than the corresponding scalar quantizer with μ-law. Simulation is performed, and it is shown that theoretical and simulation results are matched very well. It is shown that the proposed polar quantizer can be used both for stationary and non-stationary signals, choosing the appropriate value of μ. This quantizer can be widely used, for all signals with Gaussian distribution. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In realizing PC-PMC mainly two problems should be solved. The first problem is that PMC method requires invertible modules in the front–end of the system while CMS normalization is not an invertible process. Moreover, when the recognition system is exposed to noisy speech, the adaptation of the PCA transform is required; therefore a framework is to be designed for adaptation of the PCA transform in the presence of noise. The method presented in this paper provides solutions to these problems. Our evaluations are done on the four different real noisy tasks using Nevisa HMM-based, Persian continuous speech recognition system. Experimental results demonstrate significant reduction in the system word error rate using PC-PMC. In addition, the effects of covariance matrix compensation, dynamic features adaptation and the effect of gain parameter in the PMC method are also studied and experimented in the real acoustic conditions. Besides, we have investigated the using of maximum likelihood linear regression (MLLR) and maximum a posteriori (MAP) adaptation techniques in the combination with the introduced PC-PMC method. Finally, a comprehensive discussion on the capabilities of the studied robustness methods is presented. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: In this paper, an intelligent analog modulation identification system is presented for interpretation of the analog modulated signals. This paper especially deals with combination of the feature extraction and classification for analog modulated signals. The analog modulated signals used in this study are six types (AM, DSB, USB, LSB, FM, and PM). Here, a discrete wavelet neural network-adaptive wavelet entropy (DWNN-ANE) model is used, which consists of two layers: discrete wavelet-adaptive wavelet entropy and multi-layer perceptron neural networks for intelligent analog modulation identification. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of DWT and adaptive wavelet entropy. The performance of the used system is evaluated by using total 1080 analog modulated signals. These test results show the effectiveness of the used intelligent system presented in this paper. The rate of correct classification is about 98.34% for the sample analog modulated signals. [Copyright &y& Elsevier]

Abstract: This paper considers connections between the cost functions of some parameter identification methods for system modelling, including the well known projection algorithm, stochastic gradient (SG) algorithm and recursive least squares (RLS) algorithm, and presents a modified SG algorithm by introducing the convergence index and a multi-innovation projection algorithm, a multi-innovation SG algorithm and a multi-innovation RLS algorithm by introducing the innovation length, aiming at improving the convergence rate of the SG and RLS algorithms. Furthermore, this paper derives an interval-varying multi-innovation SG and an interval-varying multi-innovation RLS algorithm in order to deal with missing data cases. [Copyright &y& Elsevier]

Abstract: The array signal processing problem has to do with two main issues: 1) the determination of source number; and 2) estimation of the directions-of-arrival (DOA) of these sources. In the classical methods, the determination of source number and the estimation of DOA parameters are executed independently rather than jointly. To jointly estimate source number and DOA, this paper proposes a simulated annealing-based algorithm. The key points of this paper are: i) the prior choices for some unknown parameters are calibrated to compute more efficiently; ii) a penalty term for the parameter number is added to the corresponding objective function Additionally to avoid overfitting; and iii) in order to move to the regions of interest quickly, some cumulants of properly chosen sensor outputs from the uniform linear array (ULA) is computed and their moduluses of Fourier transform (MFT) are mapped trickly to the uniform distribution. Finally simulation results are presented to validate the performance of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, based on the correlation properties of the pseudo-random (PN) code, a scheme is proposed to achieve frame synchronization in the OFDM system. According to the estimated path delay time, the channel impulse response could be obtained with the proposed scheme. Then, based on the estimated channel response, the signal could be compensated through the one-tap frequency domain equalizer. Simulation results of the proposed frame synchronization scheme and channel estimation are presented in the paper. The miss probability of frame timing with the proposed frame synchronization scheme performs better than that with the moving average scheme and Beek''s scheme. Besides, in the channel estimation, the estimator mean square error with the proposed algorithm is better than that with the algorithm using frequency domain pilots. Moreover, with the signal compensation by the estimated channel information, the frequency domain one-tap equalizer is used to improve the system performance. [Copyright &y& Elsevier]

Abstract: In this paper, the genetic algorithm (GA) based on Canonic Signed Digit (CSD) code was used to find the optimum design of a finite impulse response digital filter (FIR). By using the characteristics of the CSD structure, the circuit was able to be simplified and also the calculation speed was raised to increase the hardware''s efficiency. However, CSD structure cannot be guaranteed by a general GA after the evolution of chromosomes. Thus in this research an algorithm was proposed which the CSD structure can be maintained. A CSD coded GA was used to the evolution of chromosome to reduce the time wasted by trials and errors during the evolution and then to accelerate the training speed. In this paper, a new hybrid code for the filter coefficients was proposed to improve the precision of the coefficient of FIR. An example is shown in this paper to verify the efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents an area-efficient twos complement high radix division algorithm and its architecture. The advantage of using high radix division is to generate more quotient bits per iteration while binary division results in one bit per iteration. In the conventional restoring radix- division, the selection of the quotient digit requires the use of all the multiples of the divisor. As a result, the area increases exponentially (area of radix divider is about times the area of binary divider) while the speed increases linearly (speed of radix divider is approximately k times that of binary divider). In this paper, a new algorithm that makes use only of the even multiples of the divisor is proposed. The area is nearly halved since the number of multiples is reduced from to . We use the even multiples rather than the odd ones because the even multiples are easier to generate and some of them are produced by simply shifting the divisor. For example, the even multiples 2D and 4D are generated by shifting D one and two bits to the left, respectively, while the odd multiples 3D and 5D require an addition of D to 2D and D to 4D, respectively. [Copyright &y& Elsevier]

Abstract: Classification is a very important task for scene interpretation and other applications of multispectral images. Feature extraction is a key step for classification. By extracting more nonlinear features than corresponding number of linear features in original feature space, classification accuracy for multispectral images can be improved greatly. Therefore, in this paper, an approach based on the fuzzy c-means clustering (FCMC) and kernel principal component analysis (KPCA) is proposed to resolve the problem of multispectral images. The main contribution of this paper is to provide a good preprocessed method for classifying these images. Finally, some experimental results demonstrate that our proposed method is effective and efficient for analyzing the multispectral images. [Copyright &y& Elsevier]

Abstract: This paper develops local signal detection strategies for spectral resolution of frequencies of nearby tones. The problem of interest is to decide whether a received noise-corrupted and discrete signal is a single-frequency sinusoid or a double-frequency sinusoid. This paper presents an extension to M. Shahram and P. Milanfar (On the resolvability of sinusoids with nearby frequencies in the presence of noise, IEEE Trans. Signal Process., to appear, available at http://www.soe.ucsc.edu/~milanfar) the case where the noise variance is unknown. A general signal model is considered where the frequencies, amplitudes, phases and also the level of the noise variance is unknown to the detector. We derive a fundamental trade-off between SNR and the minimum detectable difference between the frequencies of two tones, for any desired decision error rate. We also demonstrate that the algorithm, when implemented in a practical scenario, yields significantly better performance compared to the standard subspace-based methods like MUSIC. It is also observed that the performance for the case where the noise variance is unknown, is very close to that when the noise variance is known to the detector. [Copyright &y& Elsevier]

Linear time-invariant systems can be implemented in O (N log ⁡ N) time, whereas the most general family of linear systems can be implemented as a vector-matrix product in O (N 2) time. However, there are time-variant systems that can be implemented in O (N log ⁡ N) time. In this paper, we introduce a particular family of such systems, which we refer to as the class of linearly time varying (LTV) systems. These systems interpolate between multiplicative systems and convolutive systems, and are characterized by their chirp-type eigenfunctions and their relationship to fractional Fourier domain filtering. We derive expressions for the linear transform kernel of LTV systems, and illustrate their use with examples. Recognizing LTV systems, or approximating linear systems with LTV systems when possible, can reduce the time of computation from O (N 2) to O (N log ⁡ N). [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a new parallel rotator for FFT and IFFT applied in multi-carrier radio communication systems. The proposed algorithm serves some primary purposes. First, it reduces the computational complexity, thus simplifying the hardware implementation. Second, it speeds up the convergence of the algorithm, resulting in reduced processing latency to meet the requirements of real-time services. Third, it improves rotational accuracy. In addition, the algorithm also significantly lessens the consumption of hardware and power resources. The phase rotator's hardware architecture is then proposed and implemented on the FPGA using the Vivado System Generator for DSP Tool (SYSGEN). The FPGA experimental results indicate that the proposed algorithm's computational complexity, processing delay, and hardware resource usage are significantly reduced when compared to some recent algorithms. • The proposed parallel rotator operates in three stages, namely pre-rotation, coarse rotation, and precise rotation. • The proposed phase rotation algorithm is non-iterative and has a parallel architecture. • The proposed parallel rotator consumes fewer hardware resources and operates at a higher frequency with lower latency. • The hardware architecture of the proposed phase rotator is designed, implemented, and tested on FPGA. [ABSTRACT FROM AUTHOR]

Sherman, P., On the Statistical Nature of Real Sinusoids Associated with Rotating Machinery, Digital Signal Processing12 (2002) 471–483This paper represents the first phase of an ongoing investigation into the nature of sinusoidal types of random processes associated with real world phenomena. While perfect sinusoids exist only in the mathematical sense, their use as an approximation model in relation to real world phenomena has been and continues to be widespread, often with much value. The motivation for this work is the belief that knowledge of their deviation from such a model can provide additional useful information. The focus of this paper is on sinusoids in relation to random processes associated with rotating machinery. The tools used include mathematical limit theorem results, standard signal processing tools including spectral estimation and Kalman filtering, and basic statistics. Some noteworthy results include the normality of amplitude and frequency probability distributions, the characterization of the same as stationary random processes, and the potential to improve condition monitoring of rotating machinery. [Copyright &y& Elsevier]

Kuruogˇlu, E. E., Nonlinear Least lp-Norm Filters for Nonlinear Autoregressive α-Stable Processes, Digital Signal Processing12 (2002) 119–142The α-stable distribution family has received great interest recently, due to its ability to successfully model impulsive data. α-stable distributions have found applications in areas such as radar signal processing, audio restoration, financial time series modeling, and image processing. Various works on linear parametric models with α-stable innovations have been reported in the literature. However, some recent work has demonstrated that linear models are not in general adequate to capture all characteristics of heavy-tailed data. Moreover, it is known that the optimal minimum dispersion estimator for α-stable data is not necessarily linear. Therefore, in this paper, we suggest a shift in the interest to nonlinear parametric models for problems involving α-stable distributions. In particular, we study a simple yet analytic nonlinear random process model namely polynomial autoregressive α-stable processes. Polynomial autoregression and Volterra filtering have been successful models for some biomedical and seismic signals reflecting their underlying nonlinear generation mechanisms. In this paper, we employ α-stable processes instead of classical Gaussian distribution as an innovation sequence and arrive at a model capable of describing asymmetric as well as impulsive characteristics. We provide a number of novel adaptive and block type algorithms for the estimation of model parameters of this class of nonlinear processes efficiently. Simulation results on synthetic data demonstrate clearly the superiority of the novel algorithms to classical techniques. The paper concludes with a discussion of the application areas of the techniques developed in the paper, including impulsive noise suppression, nonlinear system identification, target tracking, and nonlinear channel equalization. [Copyright &y& Elsevier]

In this paper, we propose direction-of-arrival (DOA) estimation techniques, respectively based on covariance matrix reconstruction and matrix completion, to achieve robust DOA estimation capability in nonuniform noise environments using coprime arrays. For the covariance matrix reconstruction-based approach, by exploring the diagonal structure of the covariance matrix of the noise, the covariance matrix of the received signal vector is reconstructed through averaging its diagonal elements. Moreover, in order to handle more sources than the number of sensors, the difference coarray of coprime arrays is utilized through the vectorization of the reconstructed covariance matrix. A compressive sensing (CS) based DOA estimator is then formulated to provide sparsity-based DOA estimation. For the matrix completion-based approach, we take the full advantage of the difference coarray lags and obtain the noise-free covariance matrix of the virtual uniform linear array by using the matrix completion technique to recover the removed diagonal elements and missing holes in the virtual array covariance matrix. Then CS-based and MUSIC-based DOA estimators are respectively designed to perform DOA estimation using the estimated noise-free covariance matrix. Simulation results verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Ongoing research addressing shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis square tiling for acute feature characterization aiding shape retrieval is contributed in this paper. The proposed descriptor, named Triangulated Feature Descriptor (TFD), performs feature characterization and abstraction by fusing hybrid geometrical concepts. The mechanism initially tiles the image into square regions that are later tessellated into right-angled triangles. The right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map representing an image. The attained feature maps are locally segmented to finally produce a shape histogram. Since, the intended descriptor is modeled based on intrinsic angular interactions between the local neighbors the resulting descriptor is highly invariant to diverse affine transformations. This characteristic is examined by performing rigorous experiments on MPEG-7, TARI-1000, Articulated and PHOS datasets. Competitive analysis reveals a consistent retrieval rate greater than 85% achieved by TFD with increased performance scores. [ABSTRACT FROM AUTHOR]

In this paper, a training algorithm is proposed to online (sequentially) learn the SVM based parameter. The proposed online learning method is used to construct discriminative classifier for visual object tracking application, where new examples are available in each successive frame of a video. The iterative method is based on maximizing the magnitude of sum of projection values of a positive example and a negative example which are closest to the hyperplane formed by parameter to be updated. In the proposed training framework, even if there is non-accurate labeling of the training examples (which are received online), it is possible to learn the parameter that ensures maximum value from the classification function. The learned parameter along with some examples which are nearest to hyperplane are used for the construction of object likelihood model. Using likelihood model, tracking instance most similar to the target is selected, where the target candidates are generated using particle filter framework. An object representation is also learned based on sparse DCT coefficients. This representation contains the basic structure of the target appearance. The proposed object tracking method performs better than state-of-the-art trackers in a number of challenging video sequences. When high dimension feature vectors are used, instead of simple raw pixels based feature vectors, to represent the training examples, the performance of the object tracking is better in a number of video sequences even without integrating sparse DCT coefficient based object representation as an additional model. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the detection performance of non-coherent radar for correlated gamma fluctuating targets in K distributed sea clutter. We present the radar signal models at first. Then we derive the mathematical expression of the radar detection probability. We verify the correctness of the theoretical derivations by Monte Carlo simulations. We further analyze the radar detection performances in various clutter and target circumstances. [ABSTRACT FROM AUTHOR]

Empirical mode decomposition (EMD) may generate undesirable intrinsic mode functions (IMFs) under low sampling rate, which can significantly affect the results of decomposition. In this paper, an improved EMD method using second generation wavelets interpolation is presented which can eliminate undesirable IMFs and reduce the scale mixing effectively under low sampling rate. Firstly, the original signal under low sampling rate is reconstructed by inverse process of second generation wavelets lifting algorithm. Secondly, the location algorithm of extrema using second generation wavelets is given to obtain the accurate position of extrema. Finally, five examples are demonstrated to justify the effectiveness. Numerical simulation and experimental results are attained to show the effectiveness of the proposed method in eliminating undesirable IMFs and reducing the scale mixing, thereby making the proposed improved EMD a promising method for improving the performance of EMD under low sampling rate. [ABSTRACT FROM AUTHOR]

In the present world, distributed signal processing plays a significant role in applications ranging from surveillance and tracking to exploration and monitoring. In this paper, an online distributed framework of spatio-temporal Wiener model is presented. A conventional Wiener model is extended to nonlinear distributed parameter systems (DPSs), which comprises of a linear time-invariant (LTI) system in series with a static nonlinear element. The standard Wiener model identification framework is reformulated as the minimization of multiple constrained optimization subtasks that get solved using alternating direction method of multipliers (ADMM) along with coordinate descent techniques. DPSs are significantly used in industrial processes e.g. thermal process, fluid process, etc. Almost all the real-time data contain non-linearity in them which is modeled using several methods: Wiener modeling is one of them. Adaptive as well as distributed implementation of such model is considered to take the advantages of both adaptive and distributed signal processing. The proposed method overcomes the limitations concerning fusion center (FC) and least-square (LS) based approaches. Unknown parameters of Wiener DPS are identified in an adaptive and distributed manner. To dignify the effectiveness of the proposed methodology, simulations on a catalytic rod (an example of a parabolic system) are illustrated. [ABSTRACT FROM AUTHOR]

In this paper, we present a new performance guarantee for Orthogonal Matching Pursuit (OMP) in the context of the Direction Of Arrival (DOA) estimation problem. For the first time, the effect of parameters such as sensor array configuration, as well as signal to noise ratio and dynamic range of the sources is thoroughly analyzed. In particular, we formulate a lower bound for the probability of detection and an upper bound for the estimation error. The proposed performance guarantee is further developed to include the estimation error as a user-defined parameter for the probability of detection. Numerical results show acceptable correlation between theoretical and empirical simulations. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of direction of arrival (DOA) estimation in the presence of sensor gain-phase errors. We derive an interesting conclusion that the Cramer–Rao Bound (CRB) on DOA and gain-phase error estimation accuracy is independent of sensor phase errors. Then, we propose a new method by using the Hadamard product of the signal eigenvectors and its conjugate. In addition, we derive a sufficient condition for unambiguous DOA estimation of the proposed method. We observe that the proposed method performs independently of phase errors, which is same as the CRB. Furthermore, it is robust to the correlated signals. Simulation results verify the above-mentioned observations and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Modern wireless technology calls for high throughput, which can be achieved by large spectral bandwidth, and/or high-order modulation. The in-phase and quadrature (IQ) imbalance is an important factor degrading the transmitter error vector magnitude (EVM), thus limiting the transmitter performance. In order to compensate for the IQ imbalance, a feedback path at the transmitter output is needed to estimate the mismatch. The analog-to-digital converter (ADC) in the feedback path can be power hungry when the signal bandwidth is large. In this paper, we propose a digital IQ imbalance estimator and compensator with 1-bit quantization in the feedback path. We show that the proposed algorithm is valid in terms of estimation convergence and accuracy. Numerical results show that the proposed algorithm achieves comparable estimation accuracy as that using the conventional least mean squares (LMS) method at a much lower cost. [ABSTRACT FROM AUTHOR]

Ground moving targets may be smeared in a synthetic aperture radar (SAR) image due to the range migration, Doppler ambiguity and azimuth spectrum split caused by target unknown motion parameters. To handle these problems, we propose a fast non-searching method for maneuvering target refocusing and motion parameters estimation in this paper. First, the azimuth-deramp is conducted, which is capable of eliminating the azimuth spectrum splitting. In the sequel, the second-order keystone transform (SOKT) based on the scaling principle is applied to further correct the residual range curvature. Then, the phase difference (PD) is performed to eliminate the range walk and estimate the motion parameters, which does not require the prior knowledge of Doppler ambiguity number. Finally, the moving target can be well focused with the estimated parameters. This method has high computational efficiency and improved parameter estimation accuracy. Both simulated and real data are processed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel method based on the entropy estimation of the observation space eigenvalues is proposed to estimate the number of independent sources impinging on a sensor array. In this method we do not need to know a priori information about the noise model and we can use it in any Gaussian or non-Gaussian model of observations and noise. Our analytical results show that the proposed algorithm is consistent and an approximation for probability of false alarm and an upper bound for probability of missed detection are derived analytically. The performance of the proposed algorithm is compared with the existing methods in the presence of Gaussian and non-Gaussian noise via the simulations. It is shown that this information theoretic method called EEE, has a better performance than those methods in the literature, especially in non-Gaussian noise environment. [ABSTRACT FROM AUTHOR]

In this paper, the problem of detecting transient signals of unknown waveforms and arrival times embedded in white Gaussian noise is addressed. The use of the cepstrum coefficients of the fourth order correlations of the transient signal for forming a detection statistic is demonstrated. It is considered both a batch and adaptive approach for the detection of the signal which is assumed to satisfy a linear constant coefficient difference equation. The adaptive approach is a least squares realization based on Q–R decomposition of the fourth order statistics matrix involved in the computation of the cepstrum coefficients. Their performance is compared in terms of probability of detection and probability of false alarm with the conventional energy detector by means of Monte-Carlo simulations and improved performance is demonstrated. It is shown that the adaptive approach allows for detection of short length transients which are of unknown arrival times using a single data record even before the whole amount of data becomes available. The Q–R decomposition offers good numerical properties when fourth order statistics are involved and in contrast to the energy detector the proposed receiver has false alarm rate independent of the noise if it is of zero mean and independent, identically, distributed (i.i.d). [ABSTRACT FROM AUTHOR]

Determining the frequencies of multiple resolvable exponentials is an important problem due to its application in diverse areas in science and engineering. In this paper, frequency estimation of two-dimensional (2-D) sinusoids is addressed. With the use of the periodogram in frequency domain, the required harmonics are first located coarsely. The characteristics of the 2-D spectrum is then analyzed, and the accurate estimates of the parameters are retrieved using an interpolation method iteratively. It is proved that at sufficiently high signal-to-noise ratio conditions, the harmonic estimates are asymptotically unbiased, and their variances are also analyzed. Furthermore, when only part of the data is observed, the proposed algorithm is tailored to get fast and accurate estimation results. Computer simulations are also included to compare the proposed approach with conventional 2-D harmonic retrieval schemes in terms of root mean square error performance and computational complexity. [ABSTRACT FROM AUTHOR]

This paper provides a review of the Teager–Kaiser (TK) energy operator and its extensions for signals and images processing. This class of operators possesses simplicity and good time-resolution and is very efficient in instantaneously estimating AM–FM signals and images. We point out the importance of the concept of energy from the point of view of the generation of the signal. More precisely, we emphasize the importance of analyzing signals from the point of view of the energy of the system needed to produce them. We show how this class of TK energy operators can be used to estimate useful features for signals and images analysis in time, space and frequency domains such as instantaneous frequency, second-order moment frequency, coherence function or spatial envelope and phase. We also show the importance of the higher derivative order of TK energy operator in terms of demodulation for both mono and multi-dimensional signals. Most of the developed tools around TK energy operators deal with real and complex-valued signals and some of them extended to multi-dimensional case. Due to their low complexity and their instantaneous-adapting nature, the class of TK energy operators offers valuable processing tools for time (space), frequency and time–frequency analysis. [ABSTRACT FROM AUTHOR]

In this paper, we proposed an effective and computationally efficient algorithm without iterations, named general sparse denoising with total variation regularization (GSDN-TV), for solving the convex optimization problem of combining the sparse regularization and total variation (TV) regularization. In the GSDN-TV, the original convex optimization problem is divided into two convex optimization subproblems. Each of the subproblems only contains one regularization and can be efficiently solved or has the closed-form solution. The final solution of the original problem can be obtained by solving the two subproblems one by one without iterations. By using the non-convex firm penalty function in the sparse regularization, the GSDN-TV is applied to the wavelet-TV denoising problem and achieves outstanding performances. [ABSTRACT FROM AUTHOR]

In this paper, the optimal channel switching problem is studied for average capacity maximization in the presence of multiple receivers in the communication system. First, the optimal channel switching problem is proposed for average capacity maximization of the communication between the transmitter and the secondary receiver while fulfilling the minimum average capacity requirement of the primary receiver and considering the average and peak power constraints. Then, an alternative equivalent optimization problem is provided and it is shown that the solution of this optimization problem satisfies the constraints with equality. Based on the alternative optimization problem, it is obtained that the optimal channel switching strategy employs at most three communication links in the presence of multiple available channels in the system. In addition, the optimal strategies are specified in terms of the number of channels employed by the transmitter to communicate with the primary and secondary receivers. Finally, numerical examples are provided in order to verify the theoretical investigations. [ABSTRACT FROM AUTHOR]

With the increasing demand for higher spectral efficiency in wireless communications, faster-than-Nyquist (FTN) signaling has been rediscovered to increase transmission rate without expanding signaling bandwidth. Most existing studies focus on low-complexity FTN receiver design by assuming perfect synchronization. In practice, however, phase noise (PHN) and carrier frequency offset (CFO) may degrade the performance of FTN detector significantly. In this paper, we develop iterative FTN detector in the presence of PHN and CFO in a factor graph framework. Wiener process is employed to model the time evolution of nonstationary channel phase. The colored noise imposed by sampling of FTN signaling is approximated by autoregressive model. Based on the factor graph constructed, messages are derived on the two subgraphs, i.e., PHN and CFO estimation subgraph and the FTN symbol detection subgraph. We propose two methods to update the messages between subgraphs, namely, Gaussian approximation via Kullback–Leibler divergence (KLD) minimization and the combined sum-product and variational message passing (SP-VMP), both of which enable low-complexity parametric message passing. The proposed SP-VMP algorithm can provide closed-form expressions for parameters updating. Moreover, conjugate gradient (CG) method is adopted to solve the maximum a posteriori probability (MAP) estimation of CFO with fast convergence speed. Simulation results show the superior performance of the proposed algorithm compared with the existing methods and verify the advantage of FTN signaling compared with the Nyquist counterpart. [ABSTRACT FROM AUTHOR]

The paper is addressed to 2D phase and amplitude estimation of complex-valued signals – that is, in particular, to estimation of modulo-2 π interferometric phase images from periodic and noisy observations. These degradation mechanisms make phase image estimation a challenging problem. A sparse nonlocal data-adaptive imaging formalized in complex domain is used for phase and amplitude image reconstruction. Following the procedure of patch-based technique, the image is partitioned into small overlapping square patches. Block Matching Three Dimensional (BM3D) technique is developed for forming complex domain sparse spectral representations of complex-valued data. High Order Singular Value Decomposition (HOSVD) applied to BM3D groups enables the design of the orthonormal complex domain 3D transforms which are data adaptive and different for each BM3Ds group. An iterative version of the complex domain BM3D is designed from variational formulation of the problem. The convergence of this algorithm is shown. The effectiveness of the new sparse coding based algorithms is illustrated in simulation experiments where they demonstrate the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Simultaneous sparse approximation is a generalization of the standard sparse approximation, for simultaneously representing a set of signals using a common sparsity model. Distributed compressive sensing (DCS) framework has utilized simultaneous sparse approximation for generalizing compressive sensing to multiple signals. DCS finds the sparse representation of multiple correlated signals from compressive measurements using the common + innovation signal model. However, DCS is limited for joint recovery of a large number of signals since it requires large memory and computational time. In this paper, we propose a new hierarchical algorithm to implement the joint sparse recovery part of DCS more efficiently. The proposed approach is based on partitioning the input set and hierarchically solving for the sparse common component across these partitions. The numerical evaluation of the proposed method shows the decrease in computational time over DCS with an increase in reconstruction error. The proposed algorithm is evaluated for two different applications. In the first application, the proposed method is applied to video background extraction problem, where the background corresponds to the common sparse activity across frames. In the second application, a common network structure is extracted from dynamic functional brain connectivity networks. [ABSTRACT FROM AUTHOR]

As an alternative method of empirical mode decomposition (EMD), the empirical Wavelet transform (EWT) method was proposed to realize the signal decomposition by constructing an adaptive filter bank. Though the EWT method has been demonstrated its effectiveness in some applications, it becomes invalid in analyzing some noisy and non-stationary signals due to its improper segmentation in the frequency domain. In this paper, an enhanced empirical wavelet transform method is proposed. This method takes advantage of the waveform in the frequency domain of a signal to eliminate drawbacks of the EWT method in the spectrum segmentation. It modifies the segmentation algorithm by adopting the envelope approach based on the order statistics filter (OSF) and applying criteria to pick out useful peaks. With these measures, the proposed method obtains a perfect segmentation in decomposing noisy and non-stationary signals. Furthermore, simulated and experimental signals are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Demosaicking is a process used to estimate missing color values from the subsampled color filter array (CFA) image to reduce the cost and volume of a digital still camera. However, by sampling theory, it is known that subsampling a signal causes overlaps of signals in the frequency domain, which is known as aliasing. Most current demosaicking processes cannot completely solve aliasing problem resulting in aliasing artifacts such as false colors and zipper effects. In this paper, we propose an algorithm to remove these aliasing artifacts in demosaicked color images. A luminance image with minimum aliasing is obtained from the CFA image by using a low-pass kernel with cutoff frequencies determined by an approximate model for the Fourier spectrum. An aliasing map is computed by analyzing subband signals of the CFA image based on the high correlation of the high-frequencies of the luminance and color channels. Then, a least squares of the luminance acquisition processes is used to design a cost function with the aliasing map to remove the aliasing artifacts. The experiments demonstrate that the proposed algorithm sufficiently removes aliasing artifacts and improves the quality of the color images. [ABSTRACT FROM AUTHOR]

The discrete cosine transform (DCT) has been successfully used for a wide range of applications in digital signal processing. While there are efficient algorithms for implementing the DCT, its use becomes difficult in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated. In this paper, a new two-dimensional sliding DCT (2-D SDCT) algorithm is proposed for fast implementation of the DCT on 2-D sliding windows. In the proposed algorithm, the DCT coefficients of the shifted window are computed by exploiting the recursive relationship between 2-D DCT outputs of three successive windows. The theoretical analysis shows that the computational requirement of the proposed 2-D SDCT algorithm is the lowest among existing 2-D DCT algorithms. Moreover, the proposed algorithm enables independent updating of each DCT coefficient. [ABSTRACT FROM AUTHOR]

Compared with the traditional frost space-time beamformer (STB) with a pre-steering delay processing (PSDP), the method without the PSDP is able to provide a more intuitive observation of the space-frequency response (SFR) for both the desired signal and interference in the beam pattern. In this paper, a new SFR constraint is proposed to guarantee the same time delay for all different frequency components in the desired signal by analyzing the desired signal process of the digital STB. Specifically, in order to make all frequencies in the desired bandwidth meet the SFR constraint, we propose a method to obtain the number of the sample frequency points of the new constraint. After that, the equivalent beamformer with lower complexity is acquired via transforming the SFR constraints into a part of the optimization objectives. On this basis, a robust wideband beamformer against the direction of arrival (DOA) mismatch of the desired signal is established. To be more concretely, in the proposed robust beamformer, the covariance matrix of the interference plus noise and the covariance matrix of the desired signal are reconstructed with the observed data containing the desired signal. Additionally, the reconstructed two matrices are used to estimate the actual desired signal steering vector by considering a maximization of the desired signal output power. Numerical results verify the effectiveness of those proposed algorithms. [ABSTRACT FROM AUTHOR]

In the theory of signal processing, signals are usually classified either by determining whether their time domain is discrete or continuous, or by determining whether they are periodic. However, no comprehensive definitions of multiplication and convolution exist that are consistent with the theories behind all classes, although some important theorems in signal processing involve multiplication and convolution. In order to unite the theories behind these classifications, we will consider tempered distributions. In this paper, we propose an approach to the multiplication and convolution of distributions that is appropriate to signal processing theory, and prove a well-known theorem regarding the impulse response of continuous linear time-invariant systems of tempered distributions in the context of this new approach. [ABSTRACT FROM AUTHOR]

To improve end-to-end throughput and to reduce signaling overhead in multi-hop relay networks, broadcast virtual multiple-input and multiple-output (MIMO) systems (BVMSs) have been introduced. Conventionally, this research has been done for a limited environment where each node is equipped with a single-antenna and also interference from other networks is not included for the numerical analysis. In this paper, we propose a new virtual MIMO broadcasting transceiver (VMBT) to overcome the limitation of conventional BVMS and to improve end-to-end throughput for BVMS-based multi-hop-relay networks while the signaling overhead effectively reduced. Toward this goal, proposed VMBT is designed based on the following contributions: analysis of the channel ellipse property, convergence proof of the iterative algorithm and utilization of the null and span of channel vectors. The simulation results show that the proposed VMBT achieves the highest end-to-end throughput compared with that of other conventional technologies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for the prediction of protein coding regions that is designed to detect novel genes that do not have known, close homologs. The proposed method uses a dynamic representation scheme to convert DNA sequences into a numerical form, and then it uses the nucleotide distribution variance to calculate the period-3 spectrum. The dynamic representation scheme assigns numerical pairs to the nucleotides to emphasize the effect of the nucleotides that have a stronger participation in the period-3 spectrum. The proposed method also uses the nucleotide distribution variance which has less computational cost than the Fourier transform to extract the period-3 spectrum. A post-processing of the period-3 spectrum signal is performed to smooth the signal, detect the period-3 spectrum peaks, and locate the boundaries of the protein-coding regions. The analysis of the receiver operating characteristic (ROC) curves shows that the proposed method outperforms other Digital Signal Processing (DSP)-based methods. The analysis of the false positive peaks shows that these regions have a similarity with regions that have functional patterns in other DNA sequences. The method also highlights and explores the capabilities of techniques that perform better than homology-based techniques for de novo protein prediction. We believe that this is an area of research that has been underemphasized and deserves additional attention. [ABSTRACT FROM AUTHOR]

This paper extends the recent signal analysis method based on the spectral analysis of the semi-classical Schrödinger operator to two dimensions. An algorithm based on the tensor product approach when writing the eigenfunctions of the semi-classical Schrödinger operator is proposed. The algorithm is described and the effect of some parameters on the convergence of this method are numerically studied. The performance of the algorithm is illustrated through some examples. [ABSTRACT FROM AUTHOR]

Wireless Body area networks (WBANs) consist of sensors that continuously monitor and transmit real time vital signals to a nearby coordinator and then to a remote terminal via the Internet. One of the most important signals for monitoring in WBANs is the electrocardiography (ECG) signal. The design of an accurate and energy efficient ECG telemonitoring system can be achieved by: i) reducing the amount of data that should be transmitted ii) minimizing the computational operations executed at any transmitter/receiver in a WBAN. To this end, compressed sensing (CS) approaches can offer a viable solution. In this paper, we propose two novel CS based ECG reconstruction algorithms that minimize the samples that are required to be transmitted for an accurate reconstruction, by exploiting the block structure of the ECG in the time domain (TD) and in an uncorrelated domain (UD). The proposed schemes require the solutions of second-order cone programming (SOCP) problems that are usually tackled by computational demanding interior point (IP) methods. To solve these problems efficiently, we develop a path-wise coordinate descent based scheme. The reconstruction accuracy is evaluated by the percentage root-mean-square difference (PRD) metric. A reconstructed signal is acceptable if and only if PRD < 9 % . Simulation studies carried out with real electrocardiographic (ECG) data, show that the proposed schemes, operating in both the TD and in the UD as compared to the conventional CS techniques, reduce the Compression Ratio (CR) by 20 % and 44 % respectively, offering at the same time significantly low computational complexity. [ABSTRACT FROM AUTHOR]