Search

In this paper, we consider the problem of sparse signal recovery using a learned dictionary in multiple measurement vectors (MMVs) case. Employing deep neural networks, we provide two new greedy algorithms to solve sparse MMV problems. In the first algorithm, we create a stacked vector of measurement matrix columns and a new measurement matrix, which can be assumed as the Kronecker product of the primary compressive sampling matrix and a unitary matrix. In order to reconstruct sparse vector corresponding to this new set of equations, a four-layer feed-forward neural network is applied. In the second algorithm, joint sparse structure of the sparse vectors is considered. Recurrent neural networks are employed to extract the joint sparsity structure. In addition, we utilize an over-complete dictionary obtained from an unsupervised learning procedure. Simulation results illustrate the benefit of using the proposed methods. Finally, the proposed algorithms are applied for pilot-based channel estimation in massive multiple-input multiple-output systems to improve the channel state information recovery performance. [ABSTRACT FROM AUTHOR]

Due to its low computational burden, the affine-projection-like (APL) adaptive filtering algorithm has been extensively studied for colored signal input. Recently, a robust APL algorithm was designed by adopting the M-estimate cost function in impulsive noise environment; however, its convergence rate is very slow for sparse system identification. This paper proposed a proportionate APL M-estimate (PAPLM) algorithm, which is derived by using the proportionate matrix to heighten the convergence rate. To maintain good steady-state performance of the PAPLM algorithm, a time-varying parameter PAPLM (TV-PAPLM) algorithm is proposed, which uses a modified exponential function to adjust the time-varying parameter according to the ratio of the mean square score function to the system noise variance. Moreover, the steady-state excess mean-square error performance of PAPLM algorithm is analyzed and obtained in detail. Simulation results reveal that the proposed PAPLM and TV-PAPLM algorithms achieve fast convergence rate and good steady-state performance in sparse system identification. [ABSTRACT FROM AUTHOR]

A compressive radar cross section (RCS) measurement method is presented in this paper. This method relies on the theory of compressive sensing (CS). We first show that the RCS data have sparse expansions in some proper basis. According to the theory of CS, the full RCS data can be recovered from the partial measured data by convex optimization algorithms. Comparisons of the compressive measurement method and the traditional measurement method are demonstrated by means of numerical simulations as well as by real data measured in the outdoor range. [ABSTRACT FROM AUTHOR]

We consider the problem of tensor (i.e., multidimensional array) inpainting in this paper. By using higher-order singular value decomposition, we propose an iterative algorithm that performs soft thresholding on entries of the core tensor and then reconstructs via the directional orthogonal matrices. An inpainted tensor is obtained at the end of the iteration. Simulations conducted over color images, video frames, and MR images validate that the proposed algorithm is competitive with state-of-the-art completion algorithms. The evaluation is made in terms of quality metrics and visual comparison. [ABSTRACT FROM AUTHOR]

This paper considers complex-valued mixing matrix estimation in underdetermined blind source separation. An effective estimation algorithm based on both single-source-point (SSP) detection and modified dynamic data field clustering is proposed. First, array-processing-based time-frequency SSP detection is applied to improve signal sparsity, therein utilizing the real and imaginary components of the observed signals in the time-frequency domain. The algorithm can be applied to the estimation of complex-valued mixing matrix based on L-shaped arrays and uniform circular arrays. Then, to overcome the limitation that the clustering performance of traditional algorithms is affected by noise, data cleansing detection is introduced to reselect the SSPs with high potential energy as representative objects to achieve preliminary data classification. Finally, a dynamic data field clustering algorithm is adopted to move and merge the representative objects until all column vectors of the mixing matrix are estimated. Simulation results show that the proposed method can effectively estimate complex-valued mixing matrices with high accuracy, especially in real-world noncooperative cases without prior knowledge. [ABSTRACT FROM AUTHOR]

This work is part of the Psypocket project which aims to conceive an embedded system able to recognize the stress state of an individual based on physiological and behavioural modifications. In this paper, one of the physiological data, the electrocardiographic (ECG) signal, is focused on. The QRS complex is the most significant segment in this signal. By detecting its position, the heart rate can be learnt. In this paper, a field-programmable gate array (FPGA) architecture for QRS complex detection is proposed. The detection algorithm adopts the integer Haar transform for ECG signal filtering and a maximum finding strategy to detect the location of R peak of the QRS complex. The ECG data are originally recorded by double-precision decimal with the sampling frequencies of 2000 Hz. For the FPGA implementation, they should be converted to integers with rounding operation. To find the best multiplying factor for rounding, the comparison is performed in MATLAB. Besides, to reduce the computation load in FPGA, the feasibility of the reduction in the sampling frequency is tested in MATLAB. The FPGA Cyclone EP3C5F256C6 is used as the target chip, and all the components of the system are implemented in VHSIC hardware description language. The testing results show that the proposed FPGA architecture achieves a high detection accuracy (98.41%) and a good design efficiency in terms of silicon consumption and operation speed. The proposed architecture will be adopted as a core unit to make a FPGA system for stress recognition. [ABSTRACT FROM AUTHOR]

This paper studies the modeling of multi-frequency signals based on measured data. With the use of the hierarchical identification principle and the iterative search, several iterative parameter estimation algorithms are derived for the signal models with the known frequencies and the unknown frequencies. For the multi-frequency signals, the hierarchical estimation algorithms are derived by means of parameter decomposition. Through the decomposition, the original optimization problem is transformed into the combination of the nonlinear optimization and the linear optimization problems. The simulation results show that the proposed hierarchical algorithms have better performance than the overall estimation algorithms without parameter decomposition. [ABSTRACT FROM AUTHOR]

In this paper, a variational Bayesian (VB)-based iterative algorithm for ARX models with random missing outputs is proposed. The distributions of missing outputs can be estimated in the VB-E step, and the distributions of unknown parameters can be estimated in the VB-M step by the estimated missing outputs and the available outputs. Compared with the expectation-maximization-based iterative algorithm, this algorithm computes the latent variable and the parameter distributions at each iteration. Therefore, it is more accurate. The simulation results demonstrate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In a large number of multiband/multistandard applications, it is necessary to design matching networks that are capable of concurrently synthesizing impedances at different frequencies. In order to obtain this functionality, this paper presents a method based on multiresonant networks that is capable of providing impedance matching for frequency-dependent complex loads to the reference impedance $$Z_o$$ , normally 50 $$\Omega $$ . The capacity of multiresonant networks to generate an open circuit and a short circuit condition is used to respectively remove and insert inductors/capacitors in the network, and change the impedance generated by the matching network. To evaluate the potential of this proposal, triband matching networks operating at 915 MHz, 1.8 and 2.4 GHz were designed and evaluated from post-layout simulation results. According to the results, the target impedances were generated with reflection coefficients smaller than 0.2 and insertion losses less than 1 dB, which is acceptable in most RF applications. These results corroborate the potential of the proposal for the design of triband networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of exponential stability and passivity analysis of a class of switched systems with interval time-varying delays and nonlinear perturbations. By constructing an improved Lyapunov-Krasovskii functional combining with novel refined Jensen-based inequalities, some improved sufficient conditions for exponential stability are proposed for a class of switching signals with average dwell time. Moreover, a new sufficient condition for passivity analysis of switched continuous-time systems with an interval time-varying delay is also derived. These conditions are delay dependent and are given in the form of linear matrix inequalities, which therefore can be efficiently solved by existing convex algorithms. Lastly, four examples are provided to demonstrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) is a multi-carrier modulation scheme that has been employed in many communication standards. The performance of OFDM is severely degraded by the presence of impulsive noise caused due to different nonlinear devices and power amplifiers. In this paper, we propose a novel automated framework to detect and remove impulse noise in OFDM system. The proposed method is based on sparse decomposition and $$l_1$$ -norm optimization algorithm of the received signal over an over-complete matrix composed of both sine and cosine waveforms and time-shifted impulse waveforms. By proper construction of the over-complete matrix, the impulse removal and symbol decoding have been performed simultaneously. Thus, it can reduce the computational complexity. Our method does not require any assumption about the location and magnitude of the impulse and does not demand pilot symbols or null subcarriers unlike other existing methods. Thus, the proposed method is blind in nature. The method is evaluated using different levels of impulse noise and signal-to-noise ratios varying from 0 to 20 dB. Preliminary evaluation results demonstrate the effectiveness of the sparse representation with the proposed dictionaries in effectively removing the impulse noise and improving the bit error rate under different noise levels. [ABSTRACT FROM AUTHOR]

This paper presents a novel mixing matrix estimation method based on a frame cluster analysis for application to the dynamic system of radar signals under an underdetermined blind source separation. The received signals are first processed using an adaptive denoising method. They are then divided into different appropriate length frames. Next, the frame type is determined and each frame is processed in accordance with its type. Short-time Fourier transform is used to transform the mixed signals from time domain to time–frequency domain. A transformation matrix is introduced to resolve the issue of the traditional clustering algorithm not being applicable within the complex field. After introducing the transformation matrix, an innovative single-source point detection algorithm and an estimation algorithm for the number of source signals are proposed. Finally, the mixing matrix is estimated via a fuzzy c-means clustering algorithm based on the characteristics of complex numbers. The simulation results show that the proposed algorithm improves the estimation accuracy of the mixing matrix considerably. Further, it has evident advantages for blind estimation of the mixing matrix for time-varying radar signals in the dynamic system. [ABSTRACT FROM AUTHOR]

This paper proposes a normalized subband spline adaptive filter (SAF-NSAF) algorithm to solve the problem that linear subband adaptive filtering cannot identify nonlinear systems. The weight update of the proposed algorithm is conducted using the principle of minimum disturbance. Since a delayless structure is used in the proposed algorithm, a delay is not introduced into the update process. The effectiveness of the proposed algorithm is verified by simulations. Also, the mean and mean square stability of the proposed algorithm are evaluated using the principle of conservation of energy. The simulation results demonstrate that the performance of the proposed algorithm outperforms other cited nonlinear algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes efficient fixed-point and floating-point implementations for radix-10 square root in Xilinx FPGAs devices. The method implements digit recurrence with restoring algorithm, which supports the three decimal floating-point (DFP) types specified in the IEEE 754-2008 standard. The technique used for restoring is optimal and novel. The designs use new techniques based on the efficient utilization of dedicated resources in the programmable devices. Implementations were made in Xilinx 7-series devices. For fixed-point square root, they are capable of operating up to 212 MHz for p=7, 197 MHz for p=16, and 190 MHz for p=34. As for DFP square root, the operation frequency obtained is 194 MHz for p=7, 183 MHz for p=16, and 174 MHz for p=34. The proposed architecture achieves better computation times than related works. [ABSTRACT FROM AUTHOR]

In this paper, by incorporating the neural network into an adaptive dynamic surface control (DSC) framework, a neural adaptive DSC algorithm is developed for a class of uncertain nonlinear system to ensure the asymptotic output tracking. Neural network is used to approximate the unknown nonlinear term in the system such that the requirements for known nonlinear term in control laws design procedure are released. In order to eliminate the boundary layer effects, which are caused by the linear filters at each step in the DSC procedure, the nonlinear filters with the compensation term are designed skillfully. The proposed neural adaptive DSC algorithm not only avoids the inherent problem of "explosion of complexity" in the backstepping procedure, but also has its own advantages: (1) releasing the requirements for known nonlinear term in control laws design procedure; (2) holding the asymptotic output tracking performance. Some simulations are shown to demonstrate the effectiveness and advantages of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of robust adaptive beamforming (RAB) for a linear array in the presence of gain-phase errors. By setting a few calibrated auxiliary elements on one side of a linear array, we propose a novel gain error and phase error estimation algorithm to calibrate gain-phase errors. According to the proposed estimation algorithm, the unknown gain error and phase error are jointly estimated by calculating the eigenvector corresponding to the minimum eigenvalue of the available transitional matrices. Furthermore, we calibrate the received data and compensate for the nonwhite noise caused by the calibration process. Then, the interference-plus-noise covariance matrix (INCM) is reconstructed based on the calibrated steering vector. By incorporating the reconstructed INCM with the minimum variance distortionless response principle, a novel robust adaptive beamformer for a linear array with gain-phase errors is designed. The proposed beamformer can obviously improve RAB performance in a linear array with gain-phase errors. The robustness and superiority of the designed beamformer are demonstrated in simulations. [ABSTRACT FROM AUTHOR]

This paper addresses the node-variant graph filter on the joint time-vertex domain. An efficient algorithm is proposed to design the filter that can provide a good approximation to desirable linear operators for the time-varying signals on graphs. As a case study, the application of the designed filter on the signal denoise is addressed, where the filter is designed to approximate an inverse filtering operator. Numerical experiments conducted on the synthetic and real-world datasets demonstrate that the proposed filter is superior to the polynomial counterpart. [ABSTRACT FROM AUTHOR]

Some stochastic gradient (SG) algorithms for Hammerstein systems with piecewise linearity are developed in this paper. Due to the complexity of the nonlinear structure, the key term separation is used to transfer the nonlinear model into a regression model, and then, some SG algorithms are proposed for this model. Since the SG algorithm has slow convergence rate, a forgetting factor SG algorithm and an Aitken SG algorithm are provided. Compared with the forgetting factor SG algorithm, the Aitken SG algorithm has smaller variance of estimation error, which means the Aitken SG algorithm is more effective. Two simulation examples are provided to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In the recent past, a new set of digital signal processing algorithms are developed called short word length (SWL) DSP systems to mitigate the multiplier complexity that is an inherent part in most DSP functions. In SWL algorithms, the critical element is sigma-delta modulation (SDM). In this paper, we present the design, implementation, and hardware synthesis of the FIR filter and adaptive algorithms with the conventional and proposed multiplier schemes. Two proposed short word length adaptive algorithms namely Wiener and Steepest-Descent are compared with their counterpart LMS algorithm using conventional and proposed multiplier schemes. The hardware synthesize of these algorithms is done using Xilinx Spartan-6 and Vertex-7 FPGA and comparison is done based on area-performance-power. The overall results show that the sigma-delta modulation based adaptive DSP algorithm outperforms and is an efficient approach for mobile communication. [ABSTRACT FROM AUTHOR]

In this paper, the improved wavelet transform domain least mean squares (IWTDLMS) adaptive algorithm is established. The IWTDLMS algorithm has a faster convergence speed than the conventional WTDLMS for colored input signals. Since the performances of WTDLMS and IWTDLMS are degraded in impulsive noise interference, the IWTDLMS sign algorithm (IWTDLMS-SA) is proposed. In comparison with IWTDLMS, the IWTDLMS-SA has lower computational complexity. In order to improve the performance of IWTDLMS-SA, the variable step-size IWTDLMS-SA (VSS-IWTDLMS-SA) is introduced. The VSS-IWTDLMS-SA is derived by minimizing the ℓ 1 -norm of the a posteriori error vector. To increase the tracking ability of the VSS-IWTDLMS-SA, the modified VSS-IWTDLMS-SA (MVSS-IWTDLMS-SA)is presented. The simulation results demonstrate that the proposed algorithms have a faster convergence rate and lower misadjustment than the conventional WTDLMS. The robustness feature of the IWTDLMS-SA, VSS-IWTDLMS-SA, and MVSS-IWTDLMS-SA against impulsive noises is also verified through several experiments in a system identification setup. [ABSTRACT FROM AUTHOR]

In this paper, Benford's law (BF) is analyzed and its feasibility is presented to solve the complexity issue of high-efficiency video coding (HEVC). Secondly, the intra modes selected for the current block are modeled using BF's curve to identify the most important intra modes. Finally, the intra mode computation process is terminated when the probability of these intra modes does not follow BF's curve. This termination results in an early mode decision algorithm that reduces the complexity of HEVC. Experimental results on HEVC's dataset show that the proposed algorithm reduces the complexity of the HEVC by 32.77% and the increase in BD-BR (Bjontegaard delta bit rate) is only 1.62%. [ABSTRACT FROM AUTHOR]

The feature least-mean-square (F-LMS) algorithm has already been introduced to exploit hidden sparsity in lowpass and highpass systems. In this paper, by proposing the extended F-LMS (EF-LMS) algorithm, we boosted the F-LMS algorithm to exploit hidden sparsity in more general systems, those which are neither lowpass nor highpass. To this end, by means of the so-called feature matrix, we reveal the hidden sparsity in coefficients and utilize the l 1 -norm to exploit the exposed sparsity. As a result, the EF-LMS algorithm will improve the convergence rate and the steady-state mean-squared error (MSE) as compared to the traditional least-mean-square algorithm. Moreover, in this work, we analyze the convergence behavior of the coefficient vector and the steady-state MSE performance of the EF-LMS algorithm. Through synthetic and real-world experiments, it has been seen that the EF-LMS algorithm can improve the convergence rate and the steady-state MSE whenever the hidden sparsity is revealed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new image blind deblurring model, based on a novel low-rank prior. As the low-rank prior, we employ the weighted Schatten p-norm minimization (WSNM), which can represent both the sparsity and self-similarity of the image structure more accurately. In addition, the L0-regularized gradient prior is introduced into our model, to extract significant edges quickly and effectively. Moreover, the WSNM prior can effectively eliminate harmful details and maintain dominant edges, to generate sharper intermediate images, which is beneficial for blur kernel estimation. To optimize the model, an efficient optimization algorithm is developed by combining the half-quadratic splitting strategy with the generalized soft-thresholding algorithm. Extensive experiments have demonstrated the validity of the WSNM prior. Our flexible low-rank prior enables the proposed algorithm to achieve excellent results in various special scenarios, such as the deblurring of text, face, saturated, and noise-containing images. In addition, our method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the proposed algorithm is robust and performs favorably against state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is presented to analyze the amplitude modulated and frequency modulated (AM–FM) multicomponent signals using a combination of the variational mode decomposition (VMD) and the discrete energy separation algorithm (DESA). In the presented method, firstly, a multicomponent signal is decomposed using VMD method applied in an iterative way. In order to separate the monocomponent signals from multicomponent signal, a suitable convergence criterion is developed based on the values of estimated center frequencies ( CF ¯ ) and standard deviations ( σ CF ) of the decomposed components. Further, the estimation of amplitude envelope and the instantaneous frequency functions of monocomponent AM–FM signals has been carried out by employing DESA. Moreover, the proposed method is also applied on the synthetic AM–FM signal and speech signals to evaluate its performance. Furthermore, its performance is also compared with the Fourier–Bessel series expansion-based DESA, empirical wavelet transform-based DESA, and iterative eigenvalue decomposition-based DESA methods. The performance of the proposed method is compared with the other methods in terms of mean square error between actual and estimated amplitude envelopes ( MSE AE ), mean square error between actual and estimated instantaneous frequencies ( MSE IF ) for synthetic signal. The COSH distance measure is used as a performance measure for speech signals. It is found that the proposed method gives better results in terms of performance measures in several cases. [ABSTRACT FROM AUTHOR]

This paper proposes a dedicated hardware design approach focused on the adoption of state-of-the-art approximate adders (AAs) for the design of CMOS (complementary metal–oxide–semiconductor) Canny edge detection hardware accelerators. The proposed method leverages state-of-the-art AAs in the compute-intensive Gaussian and Gradient filter steps of the Canny edge detection algorithm. The key objectives of our accelerator architecture are: (1) to provide real-time Canny edge operation by proposing an energy-efficient ASIC (application specific integrated circuit) architecture and (2) to further reduce energy consumption when adopting the proposed design-time approach for approximate arithmetic operations. The proposed accelerator architecture considers two methods for the magnitude computation: (1) the square root operator and (2) the absolute operator. All proposed architectures herein developed were described in VHDL and synthesized in a 45 nm digital CMOS ASIC design. Results show that the baseline architecture takes only 0.42 ms to process an 8-bit 512 × 512 pixels image at a maximum VLSI operating frequency of 631 MHz. When considering all the approximate architecture versions and the methods for magnitude computation, the maximum energy reduction achieved is 44.3% when compared to the baseline architecture in an iso-performance analysis. This significant energy reduction is achieved when an average F measure quality metric equal to 0.79 is obtained. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a productive numerical technique to deal with a class of time partial ideal AI control issues. The classical fuzzy inference methods cannot work to their full potential in such circumstances, because the given knowledge does not cover the entire problem domain. In addition, the requirements of fuzzy systems may change over time. The use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. The experimental result indicates that evolved bat algorithm with our proposed fitness function presents a 93.77% success rate in average for finding the feasible solutions. The contribution of this study is that near outcomes likewise confirm that the partial administrator for a Mittag–Leffler circuit in the Caputo sense improves the execution of the AI controlled framework as far as the transient reaction, in contrast with the other fragmentary and whole number subordinate administrators. [ABSTRACT FROM AUTHOR]

In this paper, a neurodynamic algorithm with finite-time convergence to solve L 1 -minimization problem is proposed for sparse signal reconstruction which is based on projection neural network (PNN). Compared with the existing PNN, the proposed algorithm is combined with the sliding mode technique in control theory. Under certain conditions, the stability of the proposed algorithm in the sense of Lyapunov is analyzed and discussed, and then the finite-time convergence of the proposed algorithm is proved and the setting time bound is given. Finally, simulation results on a numerical example and a contrast experiment show the effectiveness and superiority of our proposed neurodynamic algorithm. [ABSTRACT FROM AUTHOR]

Partial-update (PU) algorithms offer reduced computational complexity to adaptive second-order Volterra filters (SOV) in nonlinear systems while retaining acceptable performance. In this paper, a new selective partial-update technique for the normalized LMS (NLMS) SOV algorithm is proposed, which requires lesser number of comparison operations per iteration than existing methods while providing close performance to the standard M-Max NLMS-SOV algorithm. Convergence properties of the proposed algorithm are enhanced by making the algorithm step-size time varying based on the natural logarithm function. Simulation experiments compare the proposed algorithm with existing PU and variable step-size NLMS-SOV algorithms, which illustrate the advantageous properties of the new algorithm. The proposed algorithm achieves both lower steady-state misalignment and faster convergence speed when compared with the fixed step-size full-update NLMS-SOV algorithm. Simulations also show that comparison operations overhead of the proposed algorithm is reduced significantly compared to that of the standard M-Max NLMS-SOV algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel low complex configuration methodology by deploying a directed minimal spanning tree (DMST) algorithm for designing digital multiplierless finite impulse response filter. This method utilizes the shift inclusive differential coefficient and common sub-expression elimination (CSE) technique to minimize the multiple constant multiplications using steepest decent approach. The attractive feature of this work lays on the detailed comparison of the cost, reconstruction error, pass-band and stop-band error by exploiting various evolutionary techniques such as cuckoo search algorithm, particle swarm optimization, artificial bee intelligence, hybrid swarm optimization and flower pollination algorithm; followed by employing hybrid SID and DMST on these techniques. For this purpose, a mean squared error-based objective function is optimized in the pass-band and stop-band region of the filter. Further, a detailed comparison of hybrid SID and DMST with other existing CSE algorithms is also illustrated. It has been observed that up to 80% reduction in number of adders can be accomplished using the proposed DMST-based approach for multiplierless filter design. [ABSTRACT FROM AUTHOR]

Subband adaptive filtering algorithms can increase the convergence rate of system identification tasks when the input signal is colored. Recently, a new normalized subband adaptive filtering algorithm with sparse subfilters (NSAF-SF) has been proposed, whose main advantage is the lower computational complexity when compared to state-of-the-art subband approaches, while maintaining similar convergence performance. In this paper, the first- and second-order stochastic analyses of the NSAF-SF algorithm are presented in order to provide predictions about its transient and steady-state performances. A relationship between the adaptive subband coefficients and the ideal fullband transfer function is derived, and the algorithm is proven to produce an asymptotically unbiased solution. In addition, a closed-form expression is obtained for the steady-state mean square deviation (MSD) of the subfilter coefficients. Although the proposed analyses use conventional assumptions of statistical independence, they do not assume a specific stochastic characteristic for the input signal (e.g., Gaussianity or whiteness). Transient and steady-state theoretical predictions of the MSD are confirmed by simulations. [ABSTRACT FROM AUTHOR]

In this paper, we would suggest a sparse adaptive filtering technique which is robust against Gaussian and non-Gaussian noises. To this goal, a linear combination of the least mean square and the least mean fourth loss functions has been considered as the fidelity term. Moreover, in order to promote the sparsity property of the underlying vector, we have added different sparsity-inducing penalty terms. To optimize the resultant cost function, the quasi-Newton scheme has been adopted which accelerates the convergence of the algorithm. The convergence of the proposed method has been proved analytically. Furthermore, the efficiency of the suggested scheme has been evaluated through extensive simulation scenarios which confirm the superiority of the proposed algorithm over the other state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements which belong to some interval with given censoring limits, is considered. Two improvements of the standard TKF process are proposed, in order to estimate the hidden state vectors. Firstly, the exact covariance matrix of the censored measurements is calculated by taking into account the censoring limits. Secondly, the probability of a latent (normally distributed) measurement to belong in or out of the uncensored region is calculated by taking into account the Kalman filter residual. The designed algorithm is tested using both synthetic and real data sets. The real data set includes human skeleton joints' coordinates captured by the Microsoft Kinect II sensor. In order to cope with certain real-life situations that cause problems in human skeleton tracking, such as (self)-occlusions, closely interacting persons, etc., adaptive censoring limits are used in the proposed TKF process. Experiments show that the proposed method outperforms other filtering processes in minimizing the overall root-mean-square error for synthetic and real data sets. [ABSTRACT FROM AUTHOR]

This paper presents a novel spike detection algorithm in nonstationary signals using a time–frequency (t–f) approach. The proposed algorithm exploits the direction of signal energy in the t–f domain to detect spikes in the presence of high-frequency nonstationary signals even at low signal-to-noise ratio. The performance of the proposed approach is evaluated using synthetic nonstationary signals, synthesized signals mimicking electroencephalogram (EEG) signals, manually selected segments of speech signals, and manually selected segments of real EEG signals. The statistical measures, such as hit rate and precision, are used to demonstrate that the proposed algorithm performs better than other widely used algorithms, such as the smoothed nonlinear energy detector. [ABSTRACT FROM AUTHOR]

In this paper, we employ the geometry of uniform circular array to achieve classification and localization of mixed near-field and far-field sources. Considering that the eigendecomposition of the covariance matrix requires high computational cost, we develop the propagator method to obtain the noise subspace and reduce complexity. Firstly, since the direction parameters of far-field sources at centrosymmetry sensors hold a conjugate structure while the covariance matrix of near-field sources holds a Hermitian structure, we exploit the covariance differencing approach to extract the pure near-field sources from mixed sources. Then, we improve the ESPRIT-like method and one-dimensional MUSIC method to determine the 2-D direction-of-arrival (DOA) and range of near-field sources, respectively. Finally, by calculating the noise power of mixed sources, we utilize the oblique projection approach to extract the pure far-field sources and exploit the 2-D MUSIC method to determine the 2-D DOA of far-field sources. Simulations demonstrate that the proposed algorithm can avoid the pseudo-peaks in the 2-D DOA spatial spectrum of far-field sources and provide the satisfactory performance of mixed source localization. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for unsupervised segmentation and labeling of acoustically homogeneous segments from the speech signals. The virtual labels, thus obtained, are used to build unsupervised acoustic models in the absence of manual transcriptions. We refer to this approach as unsupervised speech signal-to-symbol transformation. This approach mainly involves three steps: (i) segmenting the speech signal into acoustically homogeneous regions, (ii) assigning consistent labels to the acoustic segments with similar characteristics and (iii) iterative modeling of the acoustic segments sharing the same label. This work focuses on improving initial segmentation and acoustic segment labeling. A new kernel-Gram matrix-based approach is proposed for segmentation. The number of segments is automatically determined using this approach, and performance comparable to the state-of-the-art algorithms is achieved. The segment labeling is formulated in a graph clustering framework. Graph clustering methods require extensive computational resources for large datasets. A new graph growing-based strategy is proposed to make the algorithm scalable. A two-stage iterative modeling is used to refine the segment boundaries and segment labels alternately. The proposed method achieves highest normalized mutual information and purity on TIMIT dataset. Quality assessment of the virtual labels is performed by building a language identification (LID) system for Indian languages. A bigram language model is built using these virtual phones. The LID system built using these virtual labels and corresponding language model performs very close to the system trained using manual labels and an i-vector-based LID system. The fusion of unsupervised LID system scores from our approach and the i-vector approach outperforms the LID system built under the supervision of manual labels by a relative margin of 31.19% demonstrating the effectiveness of unsupervised LID systems that can be at par with supervised systems by using virtual labels. [ABSTRACT FROM AUTHOR]

For a class of uncertain differential spatially interconnected systems in the particular case of active electrical ladder circuits, the problem of iterative learning control with stability and robust performance specifications in finite frequency ranges is investigated in this paper. Firstly, the dynamics are converted to an equivalent differential linear repetitive process. Then, based on the Kalman–Yakubovich–Popov Lemma, a control law design algorithm is presented in the form of the corresponding linear matrix inequalities. Since the system parameters are norm-bounded uncertain, an extension to robust control law is also discussed. The resulting dynamics satisfy the robust performance specifications, and the error monotonically converges in finite frequency ranges with the robust iterative learning control law. Finally, a control simulation of an active electrical ladder circuit is presented to illustrate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for image denoising inspired by the bidimensional empirical mode decomposition algorithm and the diffusion-based filtering. In the bidimensional empirical mode decomposition technique, the noisy image is decomposed into its respective intrinsic mode functions and the high-frequency and the low-frequency noises are removed with the help of the proposed diffusion filtering method using its parameters such as connectivity, conductance function, gradient threshold and the number of iterations. The image is reconstructed with the help of these denoised intrinsic mode functions. Performance of the proposed technique is evaluated in terms of the peak signal-to-noise ratio, the structural similarity index, the mean square error and is then compared with the already existing techniques on the image denoising. From the experimentation and results obtained, it is clear that the bidimensional empirical mode decomposition method using the diffusion filtering algorithm is efficient for the denoising of the images both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

In orthogonal frequency-division multiple access (OFDMA), carrier frequency offset (CFO) destroys the orthogonality between subcarriers due to Doppler effect of the channel and also causes inter-carrier interference (ICI) as well as multiple access interference (MAI). In this paper, CFO tracking algorithm for OFDMA system is presented to overcome the effect of ICI as well as MAI. The proposed algorithm employs normalized least-mean-square (NLMS) algorithm which tracks the residual carrier frequency offset in OFDMA system. The proposed NLMS-based tracking algorithm uses average of all residual carrier frequency offset (RCFO) for improving the mean square error (MSE) performance. The proposed NLMS-based frequency tracking along with frequency offset compensator improves the error performance by reducing the effect of ICI and MAI. Simulation results indicate that the proposed algorithm achieves better tracking performance in the presence of frequency offset. From the results, it is observed that the MSE of the proposed estimator is improved about 3.8 dB at SNR = 20 dB with the frequency offset. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to introduce a generalized recursive identification algorithm compensated by orthogonal weighted kernel. The performance index of the novel proposed algorithm considers information about both the estimation error and first time derivative of estimation error. The estimation process is implemented in a recursive sliding window scheme. In addition, linear combinations of sine and cosine basis functions, which lead to Fourier orthogonal series, are employed as a kernel to approximate the time-varying parameters as continuous functions in each sliding window. Properties of the proposed algorithm are extended to both least squares estimator and instrumental variable estimator; thus, it is applicable to systems with correlated and uncorrelated noise. Simulation results demonstrate the efficiency of the proposed algorithm for accurate and online tracking of unknown parameters' trend. [ABSTRACT FROM AUTHOR]

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

This paper proposes a performance assessment-based system identification of different practically useful open-loop and closed-loop Wiener systems using an evolutionary computational algorithm named as brain storm optimization (BSO) algorithm. Different performance measures of the estimation process in practical scenario, i.e., accuracy; precision; consistency; and computational time, are measured with a properly selected fitness function which is the output mean square error (MSE) between the desired and the estimated outputs. Bias and variance of the output MSE have been found negligible for each plant model to show the accuracy and consistency limits, and the corresponding statistical test results have been shown to establish the consistency of the BSO-based identification approach. Efficient identification of each plant under a noisy environment ensures the robustness and the stability of the proposed evolutionary-based identification approach with BSO. The BSO-based optimum MSE values, corresponding estimated parameter values, computational times and the other statistical information are all compared and are found to be superior to those of the other approaches reported earlier. [ABSTRACT FROM AUTHOR]

Bingham proposed a complex fast independent component analysis (c-FastICA) algorithm to approximate the nengentropy of circular sources using nonlinear functions. Novey proposed extending the work of Bingham using information from a pseudo-covariance matrix for noncircular sources, particularly for sub-Gaussian noncircular signals such as binary phase-shift keying signals. Based on this work, in the present paper we propose a new reference-based contrast function by introducing reference signals into the negentropy, upon which an efficient optimization FastICA algorithm is derived for noncircular sources. This new approach is similar to Novey's nc-FastICA algorithm, but differs in that it is much more efficient in terms of the computational speed, which is significantly notable with a large number of samples. In this study, the local stability of our reference-based negentropy is analyzed and the derivation of our new algorithm is described in detail. Simulations conducted to demonstrate the performance and effectiveness of our method are also described. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of the direction of departure (DOD) and the direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar with array gain-phase errors. In this paper, we propose a propagator method (PM)-like algorithm for joint angle and array gain-phase errors estimation in MIMO radar. The proposed method not only yields automatically paired estimates of the angles and gain-phase errors but also has much better gain-phase errors estimation performance than the estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm; this has higher computational cost than the proposed algorithm. Furthermore, the proposed algorithm has angle estimation performance very close to ESPRIT-like algorithm. We also derive the Cramér-Rao bound (CRB) for MIMO radar with array gain-phase errors. Simulation results present the usefulness of our approach. [ABSTRACT FROM AUTHOR]

The improved Direct Digital Synthesizer (DDS) using the Hybrid Wave Pipelining (HWP) technique and COordinate Rotation DIgital Computer (CORDIC) algorithm for Software Defined Radio (SDR) is presented in this paper. In order to achieve high throughput, the hybrid wave pipelining technique is adopted. The HWP can be used to speed up the circuits without insertion of storage elements. The CORDIC algorithm is used for phase-to-amplitude conversion and utilized for dynamic transformation rather than Read Only Memory (ROM) static addressing. The frequency resolution and phase resolution are achieved as 0.023 Hz and 0.088 degree, respectively, at the maximum operating frequency of 199.288 MHz for the proposed DDS architecture. The spectral purity of the proposed design has been improved to 114 dBc with a throughput of 94 %. This paper is focused on the design and implementation of DDS using hybrid wave pipelining with CORDIC approach to target on Xilinx Spartan 3 (XC3S400-5PQ208) Field Programmable Gate Array (FPGA) with a speed grade of −5. The proposed DDS design reduces the gate count from 49.4 % to 18.2 % as compared to the conventional pipelined Read Only Memory Look Up Table (ROMLUT) DDS method. The throughput of the proposed method has been improved from 78 % to 94 % and 55 % of total power reduction as compared with conventional DDS. The performance of the improved DDS architecture is compared with several existing DDS architectures and it is found that the present design is outperforming and can be used for software defined radios. [ABSTRACT FROM AUTHOR]

In this paper, the problem of robust H control for a class of uncertain neutral stochastic systems with mixed delays is investigated. The parameter uncertainties are assumed to be norm-bounded. A delay-dependent sufficient condition is derived in terms of the nonlinear matrix inequality by constructing proper Lyapunov-Krasovskii functional, using matrix inequality techniques and introducing free weighting matrices. The new result obtained in this paper can be tested numerically by using the so-called cone complementarity linearization (CCL) algorithm. Two examples provided in the literature show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel scalable and runtime dynamically reconfigurable FFT architecture for different wireless standards. With only 8 butterfly units, a reconfigurable FFT architecture for three different FFT points is realized using mixed radix-2/2/2 FFT algorithm in a modified Single-path Delay Feedback (SDF) pipelined architecture. Via a proper data flow reconfiguration it can support 64, 128 and 256. It can even be extended up to 8192-point transforms and uses only 13 butterfly units to realize 8192 points. This paper describes the implementation method of 256 and 128 point FFT, which is reconfigured partially from 64 point FFT. The whole system is implemented on a Xilinx XC2VP30 FPGA device. The implementation design addresses area efficiency and flexibility allowing the insertion of the partial modules dynamically to realize various FFT sizes. To verify the efficacy of this dynamic partial reconfigurable FFT design method, a conventional multiplexer based reconfigurable architecture was designed and tested on the same platform. Tested FPGA results for the Dynamic Partial Reconfigurable (DPR) method show the configuration time improvement and good area efficiency as compared to the reconfigurable architecture using conventional multiplexer techniques. [ABSTRACT FROM AUTHOR]

Sliding-Window Fast Fourier Transform (SWFFT) is a very important and widely used time-frequency representation of a signal. In the paper, we mainly focus on the problem of how to implement non-recursive SWFFT in parallel programming. To avoid repeated calculations, non-recursive SWFFT algorithms always save the calculated results and use for later calculations. So the current calculations need the results from the earlier calculations, and this leads to the main obstacle of implementing SWFFT in parallel programming. By assuming that all the data have been present at the outset, a new parallel algorithm for non-recursive SWFFT is proposed in the paper. In our algorithm, 3D data structure is utilized to represent the computation of SWFFT while the calculations are carried out along level axis instead of time axis. Since the current calculations do not anymore depend on the results from the earlier calculations, our algorithm can be implemented by parallel programming easily. Finally, the algorithm is programmed in C++ based on OpenMP API and is evaluated on a 4-processor desktop computer. Threads up to four are created and each of them is performed by one processor core. A memory-shared parallel programming model is devised to achieve data accessing and results exchanging between threads. Compared with employing one thread, our algorithm reduces the computational time of a signal of length L=10 (the window length N=10) to 32% by employing four threads. [ABSTRACT FROM AUTHOR]

The invention of cognitive radio concept is aimed to overcome the spectral scarcity issues of emerging radio systems by exploiting under-utilization of licensed spectrum. As the cognitive users (secondary users) are only allowed to use a licensed spectrum in the absence of its rightful owner, the ability to accurately sense the presence of the rightful owners (primary users) is highly essential. The traditional way of having individual secondary users perform their own spectrum sensing is vulnerable to the presence of noise and shadowing of propagation channel. Cooperative spectrum sensing emerges as an attractive alternative that exploits the inherent geospatial diversity of multiple cognitive radios to enhance the robustness of sensing accuracy. Two specific issues in cooperative spectrum sensing are discussed in this paper. The first issue is on dynamic detection of primary user's bands. A dynamic band clustering (DBC) algorithm that uses K-means clustering technique is proposed in this paper. The proposed algorithm reduces the number of erroneous narrow subbands resulting from spurious noise. This in turn minimizes the number of subbands to be detected and hence the overall sensing time. The second issue is on reducing the overheads required to facilitate fusion center operation. A novel entropy-based maximal ratio combining for decision-fusion center is also proposed in this paper. Based on extensive simulation studies, the proposed fusion technique is shown to be comparable to conventional information-fusion techniques. The performance offered by the proposed technique is achieved with significant reduction in the bandwidth overheads. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of robust l− l guaranteed cost filtering for uncertain discrete-time switched systems with both mode-dependent time-varying delays and norm-bounded parameter uncertainties. The objective of this paper is to design a stable guaranteed cost filter which guarantees that the error system is asymptotically stable and the cost function value is not more than a specified upper bound for all admissible uncertainties. By resorting to a descriptor system approach, delay-dependent sufficient conditions of guaranteed cost filtering are presented in terms of linear matrix inequalities (LMIs). A numerical example is provided to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new family of algorithms for underdetermined blind identification based on the second generating function (SGF). For the real case, the proposed algorithm first takes advantage of the partial derivatives of the SGF of the observation signals to construct a tensor and then reconstructs this tensor by grouping the parallel factors. The reconstructed tensor, which expands the length of several dimensions of the original tensor, transforms the underdetermined case into an overdertermined or determined case when (M 2 + M) / 2 ≥ N , where M is the number of sensors and N is the number of sources. Thus, obtaining the estimation of the mixing matrix by decomposing the reconstructed tensor results in the performance improvement. The analysis of the uniqueness of tensor decomposition and the complex analysis show that the proposed algorithm relaxes the limitation of the maximal number of sources and owns lower computational complexity order compared with the conventional ALESCAF algorithm. We also extend the proposed algorithm to the complex case by complex-to-real transformation. Simulation results verify the effectiveness of the proposed algorithms and show that the proposed algorithms are preferred when the signal-to-noise ratio is higher than 5 dB. [ABSTRACT FROM AUTHOR]