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This work describes the log-MAP (BCJR) algorithm implementation of a close to capacity dirty paper coding CODEC. The CODEC consists of eight deep pipeline processors. It decodes blocks of 975 bits in 26.9 ms using less than 9.7% of low-cost FPGA (and no DSP blocks). Two pipelines, for alpha and beta, calculate the values of gamma (of the BCJR) to reduce the storage requirements. The final log-likelihood ratio (LLR) is calculated together with alpha, reusing intermediate results. The number of bits used by the different signals of the processor is easily configurable. It was set to six bits to the channel measure signals and eight bits to log of probability signals like alpha, beta, and others. The CODEC clock was 100 MHz. The achieved bit rate is 36.2 Kbps per CODEC, but multiple CODECs can be fit into a single chip. The CODEC is 3.49 dB from the channel capacity. [ABSTRACT FROM AUTHOR]

Under the condition of low signal-to-noise ratio, for the problem of insufficient speech feature extraction and speech enhancement effect of the traditional neural network, this paper is based on empirical mode decomposition (EMD), temporal convolutional network (TCN), and gated convolution recurrent neural network (GCRN), while combining with feature fusion module (FFM), the adaptive mean median-empirical mode decomposition-multilayer gated feature fusion module convolutional recurrent neural networks (ME-MGFCRNs) for speech enhancement modeling. The network model uses a split-frequency learning strategy to learn low-frequency features and high-frequency features, i.e., the TCN and MGFCRN networks are used to obtain low-frequency and high-frequency features, and FFM processes the two sets of features to achieve speech enhancement in the form of feature mapping. The model proposed in this paper performs ablation and comparison experiments on the dataset to evaluate the enhancement effect of speech using PESQ, FwSegSNR, and STOI metrics. The research shows that under different noise environments and SNR conditions, the model proposed in this paper improves compared with other baseline models, especially under the low SNR condition of − 5 dB, FwSegSNR and PESQ improve by more than 0.86 dB and 0.02 compared with other baseline models. [ABSTRACT FROM AUTHOR]

Constrained least mean square (CLMS) algorithm is the most popular constrained adaptive filtering algorithm due to its simple structure and easy implementation. However, its convergence slows down when the input signal is colored. To address this issue, this paper firstly introduces the normalized subband adaptive filter (NSAF) into the constrained filtering problem and derives a constrained NSAF (CNSAF) algorithm using the Lagrange multiplier method. Benefiting from the good decorrelation capability of the NSAF, the proposed CNSAF algorithm significantly improves the convergence performance of the CLMS algorithm under colored inputs. Then, the mean and mean-square stability of the CNSAF algorithm is analyzed, and the theoretical models to characterize the transient and steady-state mean square deviation (MSD) behaviors of the CNSAF algorithm are derived utilizing the Kronecker product property and vectorization method. Further to extend the CNSAF algorithm to the problem of sparse system identification, a sparse version of the CNSAF algorithm (S-CNSAF) is derived. Finally, the validity of the derived theoretical MSD prediction models and the superiority of the proposed algorithms are confirmed by extensive computer simulations on system identification with colored inputs. [ABSTRACT FROM AUTHOR]

The controlled autoregressive autoregressive moving average (CARARMA) models are of popularity to describe the evolution characteristics of dynamical systems. To overcome the identification obstacle resulting from colored noises, this paper studies the identification of the CARARMA models by forming an intermediate correlated noise model. In order to realize the real-time prediction function of the models, the on-line identification scheme is developed by constructing the dynamical objective functions based on the real-time sampled observations. Firstly, a rolling optimization cost function is built based on the observation at a single sampling instant to catch the modal information at a single time point and a generalized extended stochastic gradient (GESG) algorithm is proposed through the stochastic gradient optimization. Secondly, a rolling window cost function is built in accordance with the dynamical batch observations within data window by extending the proposed GESG algorithm and the multi-innovation generalized extended stochastic gradient algorithm is derived. Thirdly, from the perspective of theoretical analysis, the convergence proof of the proposed algorithm is provided based on the stochastic martingale convergence theory. Finally, the simulation analysis and comparison studies are provided to show the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present an underdetermined estimation algorithm for two-dimensional (2-D) direction of arrival (DOA) based on three-parallel co-prime arrays. By exploiting the correlation among the received signals in both the time and spatial domains, a conjugate augmented spatial–temporal virtual array is constructed, which significantly increases the degrees of freedom (DOF) and expands the array aperture, effectively filling the holes within the physical array. By vectorizing the covariance matrix obtained from the spatial–temporal virtual array, we can transform the 2-D DOA estimation problem into a one-dimensional (1-D) sparse recovery problem. Thereafter, the excellent gridless methods: sparse and parametric approach (SPA) and the matrix pencil and pairing (MAPP), are adopted to efficiently resolve the 1-D sparse recovery problem, enabling the automatic pairing of the 2-D angles. And simulation results are provided to validate the enhanced accuracy of the proposed algorithm in comparison with other existing methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel algorithm for optimizing the coefficients of the digital filters used in incremental delta-sigma analog-to-digital converters (IDSC). This algorithm is modified from constrained linear least squares (LS) to improve the signal-to-noise-and-distortion ratio (SNDR) of IDSC and minimize the oversampling rate (OSR) of the modulator, which enhances system speed and reduces power consumption. In the case of the same SNDR, the first-order IDSC with the proposed filter can reduce the OSR of the modulator by 50%, at least compared to the IDSC with the conventional filter. The second-order IDSC with the proposed filter can reach a higher SNDR of 106 dB with an OSR of 64. Considering the nonlinearity of the integrator, the SNDR of IDSC with the proposed filter is also 10 dB greater than that of the conventional filters and 3dB greater than that of the IDSC using the near-optimal algorithm filter. The experimental results indicate that the proposed filter possesses an excellent figure of merit of 0.028 pJ / conv . [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new algorithm for pattern synthesis of large linear arrays. In our algorithm, the large linear array is divided into multiple disjoint nonuniformly-spaced subarrays and is only weighted at subarray level. Unlike the existing methods, the subarray synthesis is implemented in two stages. Firstly, the optimization of subarray partition and subarray weights are performed by using the K-means approach. Based on the determined subarray partition and subarray weights, the subarray element positions are further optimized by the iterative convex programming method. Compared with the state-of-the-art methods, the proposed algorithm can synthesize lower peak sidelobe level and yield smaller pattern matching error. Numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article focuses on the fixed-time stability of a class of nonlinear systems with uncertain disturbances and time-varying delays. Different from other papers, the second-order sliding mode control algorithm (SOSMCA) is developed in this article to study the fixed-time stability of nonlinear systems for the first time. Additionally, the novel sliding mode surface (SMC) and second-order SMC are presented. And the stability and reachability of the sliding-mode dynamics under a faster settling time are demonstrated. Finally, the applicability and validity of the obtained SOSMCA are demonstrated by a simulation example with the F-404 aircraft engine model. [ABSTRACT FROM AUTHOR]

Machine learning-based direction-of-arrival (DOA) estimation methods can have a good predictive ability even in complex scenarios. However, their estimation performance in the face of unknown data is poor because they rely on the generalization ability of the algorithm itself. Therefore, this paper proposes a DOA estimation method based on the Light Gradient Boosting Machine (LightGBM) algorithm. Using the histogram algorithm, gradient-based one-sided sampling and exclusive feature bundling measures, the LightGBM algorithm can reduce the time to find the best segmentation point, reduce the amount of data and the number of features in the dataset, thus reducing the model training time, and achieve high prediction accuracy. Applying the LightGBM algorithm to the DOA estimation problem and using a large dataset for training can improve the estimation accuracy while reducing the training cost. Simulation and real experimental results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time and fixed-time bipartite consensus problem of multiple Euler–Lagrange systems (MELSs) under directed interaction topologies, considering external disturbances. Two novel distributed hierarchical control algorithms are developed to address the challenging problems. Specifically, a new distributed estimator is designed to estimate the states of agents in finite/fixed-time. Moreover, based on the obtained estimations, the finite/fixed-time controller is presented to accomplish the bipartite consensus of the MELSs in the local control layer. By utilizing Lyapunov stability and the nearest neighbor-interactions theory, sufficient conditions for achieving the designed performance of the MELSs are derived. Simulation examples are provided to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The performance of a narrowband active noise control (NANC) system can be significantly degraded due to the frequency mismatch (FM). In this paper, the statistical performance of a typical FxLMS-based NANC system in the presence of FM is analyzed in detail. Difference equations governing the system dynamics and closed-form steady-state mean-square error expressions are derived and discussed. The stochastic analysis results reveal that the FM introduces small troublesome sinusoids into the NANC system. The controller has to track these sinusoids to minimize the residual noise, which leads to a serious performance deterioration. As a by-product, the optimal step sizes that minimize the effect of a relatively small FM are also derived. The findings significantly enrich our understanding of the stochastic behavior of the FxLMS algorithm in the presence of FM and also provide some useful information for NANC system design. Extensive simulations are conducted to confirm the validity of the analytical findings. [ABSTRACT FROM AUTHOR]

High-accuracy positioning plays an important role in future 5G-Advanced cellular networks. However, it is quite challenging to utilize 5G signals to estimate positioning parameters accurately under multipath environments. To establish a high-precision real-time location system (RTLS), which is suitable for indoor scenarios, advanced parameter estimation techniques with low computational complexity should be developed. This paper proposes a modified subspace-based time-of-arrival (TOA) estimation algorithm for 5G signals with ultra-high accuracy and reduced complexity under multipath environments. A novel smoothing scheme termed as grouped subcarrier smoothing (GSS) is proposed to reduce the dimension of the covariance matrix involved in typical subspace-based methods. Then, the computational complexity of the corresponding eigenspace calculation is reduced. The TOA estimation performance is enhanced by constructing multiple snapshots of the estimated channel frequency response vector in the frequency domain. Finally, the super-resolution TOA estimate can be obtained using only one orthogonal frequency division multiplexing (OFDM) symbol. Simulation results show that the proposed method achieves centimeter-level TOA estimation accuracy under multipath channels when the signal-to-noise ratio (SNR) is high. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS), as a digital signal processing approach, focuses on estimating the underlying source signals from their linear mixtures without any prior information about the source signals and mixing matrix. Conventional methods for the BSS, however, are incapable of separating the complex-valued source signals. By leveraging the negative conjugate gradient to minimize the least mean square error reconstruction (LMSER) principle in complex domain, this paper proposes a collection of least-squares algorithms for complex-valued BSS (CBSS), including least-mean square (LMS)-type algorithms and recursive least-squares (RLS)-type algorithms. We demonstrate the availability of the proposed algorithms in both circular and non-circular source signals separation. Especially, the RLS algorithm for the CBSS without prewhitening is superior in cross-talking criterion to the others, as verified by computer simulations on artificial source signals. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is presented for the nonparametric variable step-size normalized least-mean-square (NP-VSS-NLMS) algorithm. This algorithm has demonstrated potential in practical applications and hence a deeper understanding of its behavior becomes crucial. In this context, model expressions are obtained for characterizing the algorithm behavior in the transient phase as well as in the steady state, considering a system identification problem and Gaussian input data. Such expressions reveal interesting algorithm characteristics that are useful for establishing design guidelines and for the advancement of more refined algorithms. Simulation results for various operating scenarios ratified both the model's accuracy and the algorithm's superior performance relative to other recent and relevant algorithms from the literature. [ABSTRACT FROM AUTHOR]

A novel multi-combined-step-size normalized subband adaptive filtering algorithm is proposed. Different from the traditional combined-step-size method, by employing a variable mixing parameter on each subband, our designed algorithm is able to combine a large step size with a small one more effectively for each subband. The subband mixing parameters are derived from the variance of the noise-free a priori subband error signals. In this design, a noniterative shrinkage strategy is also utilized to estimate the noise-free a priori subband error signals. Moreover, the mean-square and steady-state performances of the proposed algorithm are studied. Simulation results illustrate that the proposed algorithm outperforms other algorithms mentioned in this paper in terms of tracking capability, convergence speed and steady-state error. [ABSTRACT FROM AUTHOR]

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the signal reconstruction of M-channel oversampled graph filter banks (MOSGFBs) on sparse graphs. The analysis filter bank of MOSGFBs can be in polynomial or node-variant (NV) form. Given the analysis filter bank and sampling matrix, the synthesis procedure can be formulated into a quadratic optimization problem. To solve this problem, a novel distributed conjugate gradient (DCG) algorithm is proposed, in which the step size and increment involved in the conjugate gradient iteration are evaluated in a distributed manner. The local step sizes and increments are first calculated using data from neighboring nodes, and then patched to participate in the iteration. The DCG algorithm involves the exchange of information between neighboring nodes and executes the synthesis computation locally; therefore, the synthesis procedure has a low implementation complexity and is suitable for processing large-scale datasets. Numerical examples conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multimedia data have increased dramatically today, making the distinction between desirable information and other types of information extremely important. Speech/music discrimination is a field of audio analytics that aims to detect and classify speech and music segments in an audio file. This paper proposes a novel feature extraction method called Long-Term Multi-band Frequency-Domain Mean-Crossing Rate (FDMCR). The proposed feature computes the average frequency-domain mean-crossing rate along the frequency axis for each of the perceptual Mel-scaled frequency bands of the signal power spectrum. In this paper, the class-separation capability of this feature is first measured by well-known divergence criteria such as Maximum Fisher Discriminant Ratio (MFDR), Bhattacharyya divergence, and Jeffreys/Symmetric Kullback–Leibler (SKL) divergence. The proposed feature is then applied to the speech/music discrimination (SMD) process on two well-known speech-music datasets—GTZAN and S &S (Scheirer and Slaney). The results obtained on the two datasets using conventional classifiers, including k-NN, GMM, and SVM, as well as deep learning-based classification methods, including CNN, LSTM, and BiLSTM, show that the proposed feature outperforms other features in speech/music discrimination. [ABSTRACT FROM AUTHOR]

Image processing algorithms are essential for clarifying the image and improving the ability to recognize distinct characteristics of the image. The field of digital image processing is widespread in several research and technology applications. In many of these applications, the existence of impulsive noise in the obtained images is one of the most frequent problems. The median filter is a strong method to remove the impulsive noise; it effectively eliminates salt and pepper noise from the image. The main target of this paper is to investigate efficient median filter units to be connected to a general-purpose processor (GPP) for FPGA-based embedded systems. The paper exposes three novel techniques, two of them specially for median filtering techniques and the third one is used to get the maximum number of any 9 elements array. The proposed algorithms are inspired by the Median Of Median (MOM) algorithm. The first two techniques are tested for filtering 3 × 3 image windows and optimized for producing the expected result in high accuracy, short time, and reduced number of comparisons. The last technique is tested for a 9 elements array for extracting the maximum number in same high efficiency manner. Furthermore, the three proposed techniques are implemented leveraging the advantage of the parallel processing and the FPGA flexible resources to satisfy the real-time processing constraints. A comparison between the first two proposed filtering units and their counterparts in the literature is included. The comparison reveals the superiority of the first technique in terms of accuracy with fewer comparators than previously published techniques. Besides, the paper illustrates how the concept beyond the proposed techniques can be used to perform the maximum pooling for convolution neural networks. [ABSTRACT FROM AUTHOR]

Bright-dark components and edge details are the most important complementary information between infrared and visible images. To extract and fuse them efficiently, a novel non-subsampled morphological fusion algorithm is proposed in this paper. The algorithm uses non-subsampled pyramid (NSP) as the spatial-frequency splitter to decompose the source image to get a series of high-frequency detail images and one low-frequency background image. Then, a dual-channel multi-scale top–bottom hat (MTBH) decomposition is constructed to extract the bright-dark details from the low-frequency background. In addition, to extract the edge details with different directions from high-frequency images, a dual-channel multidirectional inner-outer edge (MIOE) decomposition is constructed. Through these decompositions, the bright-dark information and edge details present in the source images can be effectively extracted. Then, based on the distinct roles of the extracted information, the decomposed images are fused using diverse fusion strategies. Subsequently, the fused image is reconstructed using the appropriate inverse transforms corresponding to each decomposition. The experimental results demonstrate that the fusion images generated by this algorithm exhibit richer details and higher image contrast compared to those produced by state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In order to improve tracking accuracy of time-varying sparse signals, a sparse state Kalman filter algorithm based on Kalman gain matrix is proposed in this paper. Under the constraint of sparse state, minimizing the mean square error and solving the optimization problem by using the symmetrical alternating direction multiplier method (symmetrical ADMM), while keeping the update expression of state estimation unchanged, a Kalman gain matrix is obtained which can make the state estimation sparse. In addition, this paper also discusses two different sparse constraints which are L1 norm constraint and cardinality function constraint. The proposed algorithm can be implemented under the framework of conventional Kalman filter algorithm, no need to introduce additional frameworks. Two groups of dynamic signal models, a slow change of signal and a random walk of nonzero elements, are simulated. Simulation results show that the proposed algorithm can improve the tracking accuracy of conventional Kalman filter algorithm for time-varying sparse signal. [ABSTRACT FROM AUTHOR]

Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms. [ABSTRACT FROM AUTHOR]

Wideband linear frequency modulation (LFM) signals are widely used in radar, sonar, mobile, and similar applications. One of the common problems of algorithms for estimating the direction of arrival (DOA) of LFM signals is that the number of snapshots must be large for good estimation. Therefore, they are not suitable for real-time and low-power applications. In this paper, we proposed a method of estimating DOA based on the sparse iterative covariance (SPICE) algorithm, which has features such as low computational complexity for the uniform linear array (ULA) using the Fourier transform (FFT). We first developed the dechirp process for linear frequency modulation signals using the Fourier transform. Then we modified the SPICE algorithm for linear arrays. Finally, we have obtained the calculation of the DOA estimation for a few snapshots with low computational complexity and high resolution. Compared to other methods, the simulation results of the proposed LSPICE algorithm show an increase in estimation accuracy, higher resolution, more acceptable accuracy in low SNRs, less error in high SNRs, favorable response with low snapshots, and lower computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, an unambiguous two-dimensional (2D) direction of arrival (DOA) estimation algorithm based on a large-space L-shaped array is proposed. The proposed L-shaped array is composed of two large-space linear arrays. Each linear array consists of two uniform linear arrays with internal element spacing being larger than half wavelength of incident signal. Firstly, an unambiguous modified estimation of signal parameter via rotational invariance techniques (ESPRIT) algorithm is proposed to estimate the elevation angles. Then, using the estimated elevation angles, automatically matched azimuth angles can be estimated. On account of the adjustable element spacing, the proposed array is robust to mutual coupling effect. Moreover, simulation results can prove that the proposed algorithm has higher estimation accuracy than many similar 2D DOA estimation algorithms based on L-shaped array. [ABSTRACT FROM AUTHOR]

The multipath effect error (MEE) is typically not taken into account by the RTKLIB localization method, and this may lead to poor positioning accuracy. This paper proposes an expectation maximization (EM) algorithm for GPS positioning based on Volterra series, and the pseudoranges contaminated by MEE are considered as missing data. Firstly, the Volterra series is introduced to linearize the pseudorange equation. Then, the EM algorithm is used to iteratively update the user location and missing data. Compared with the RTKLIB method, the proposed algorithm has more accurate positioning accuracy. The simulation example shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

To solve the noise reduction failure of the filter-x least mean squares (FxLMS) algorithm in the impulsive noise environment, the filter-x tanh LMS (FXtanhLMS) algorithm is proposed in this paper. The algorithm enhances the robustness by introducing an adjustable nonlinear hyperbolic tangent function. The hyperbolic tangent function is used to compress the residual signal. It can solve the problem that the impulsive noise does not have a bounded second-order moment. Compared to the fixed compression function, the automatically adjustable compression function can balance the convergence speed and the robustness of the algorithm under different intensities of impulsive noises. It allows the algorithm to have a slow change in the adaptive steady stage. In addition, the normalized filtered-x tanh LMS (NFXtanhLMS) algorithm is proposed to improve the performance in heavy spike impulsive noise by combining the FXtanhLMS algorithm with the normalized step-size. The proposed algorithms do not require complex threshold estimation compared to existing algorithms. Simulation results show that faster convergence, minor residual errors, and better system tracking can be achieved in both SaS synthesis noise and punching noise. [ABSTRACT FROM AUTHOR]

In this paper, a novel pitch detection algorithm (PDA) is proposed. Actually, pitch detection is a classical problem that has been investigated since the very beginning of speech processing. However, the novelty of the proposed method consists in establishing an empirical relationship between fundamental frequency ( f 0 ) and instantaneous frequency ( f i ), which serves as a basis to develop the proposed PDA. Even though f 0 and f i are defined as attributes of two different transforms, i.e., the Fourier transform and the Hilbert transform, respectively, the relationship proposed in this paper shows some interaction between both of them, at least empirically. The first step of this work consists in validating the proposed relationship on a large set of speech signals. Then, it is leveraged to develop an algorithm capable to (a) detect voiced/unvoiced parts of speech and (b) extract f 0 contour from f i values in the voiced parts. For evaluation purposes, the yielding f 0 contour is compared to some well-rated state-of-the-art PDA's. The main findings show that the quality of pitch detection obtained by the proposed technique is as satisfactory as some of top PDA's, either in clean or in simulated noisy speech. In addition, one of the main advantages consists in bypassing the traditional short-time analysis required to assume local stationarity in speech signal. [ABSTRACT FROM AUTHOR]

The dictionary grid mismatch problem in sparse processing is an important factor affecting parameter reconstruction. The existing grid correction algorithms are limited by the initial grid division and the accurate estimation of the support set vector. Furthermore, these methods fail if multiple scattering points fall into the same grid interval. In this paper, a constrained inhomogeneous grid optimization sparse processing algorithm is proposed. The proposed method includes two sub-processes, namely initial grid optimization and the support set vector correction. The optimization of the initial grid is to use the idea of the coordinate descent method to iteratively generate an updated grid vector, which is used for the inhomogeneous fission of the grid around the target from coarse to dense. Moreover, under the constraint of dictionary atom maximum correlation, multiple scattering points are separated in different grids, which improves the search accuracy of support set atoms. The correction process of the support set vector is to use the first-order Taylor expansion of the dictionary grid to linearly approximate the real parameters of the target, to achieve adaptive correction of dictionary mismatched atoms. According to the simulation experiment of the frequency agile radar scenario, the proposed algorithm can achieve higher range-Doppler parameter joint estimation accuracy in the multi-scattering point scenario in comparison with the conventional sparse recovery algorithm and grid point correction algorithm. [ABSTRACT FROM AUTHOR]

The major purpose of this paper is to explore an adaptive recursive notch digital filter by employing the normal state-space description of a second-order notch filter with single frequency and applying the gradient-descent method. Another is to analyze the stability and frequency-estimation bias for the gradient-descent iterative algorithm. Finally, a numerical example is presented to demonstrate the validity and effectiveness of the adaptive recursive notch filter and frequency-estimation bias analyses. [ABSTRACT FROM AUTHOR]

This paper considers the identification problem of the linear continuous time-delay systems. By using the multi-frequency responses, a stochastic gradient gradient-based iterative (SG-GI) algorithm is derived. The proposed algorithm can estimate the unknown parameters and the unknown time delays simultaneously. To improve the parameter estimation accuracy of the SG-GI algorithm, a multi-innovation stochastic gradient gradient-based iterative (MISG-GI) algorithm is derived by using the multi-innovation identification theory. In addition, a forgetting factor is introduced to increase the parameter estimation accuracy. The resulting algorithm is called the multi-innovation forgetting gradient gradient-based iterative (MIFG-GI) algorithm. The effectiveness of the proposed strategies is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) target localization by using hybrid time difference of arrival (TDOA) and angle of arrival (AOA) measurements from multiple sensors has been an active research area for several decades due to its extensive applications in various fields. For this nonlinear estimation problem, the pseudolinear system of equations constructed by using the measurements generally acts as the basis of numerous localization algorithms. In this paper, we aim to improve the performance of 3D TDOA–AOA localization by introducing the constrained total least squares (CTLS) framework wherein the inherent characteristics of the pseudolinear equations can be properly taken into consideration. On the basis of the total least squares model, the CTLS model for 3D TDOA–AOA localization is established by imposing the inherent characteristics of the pseudolinear equations as additional constraints. Then, the multi-constraint optimization problem in CTLS model is solved by using an iterative algorithm based on successive projections. Extensive numerical simulations are accomplished for evaluating the performance of the proposed CTLS algorithm. The results show that the proposed algorithm gives moderate accuracy enhancement with acceptable computational cost, and more importantly, it is more robust to large measurement noise than the compared algorithms. [ABSTRACT FROM AUTHOR]

Currently, the covariance matrix (CM) or pseudo CM of the received signal is processed to improve the degrees of freedom (DOFs) and estimation accuracy. This is where the second-order cumulant (SOC) in the two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithm based on sparse arrays comes into play. This paper proposes a 2-D DOA estimation approach for non-circular (NC) signals based on fourth-order cumulant (FOC), which fully exploits its inherent benefits in virtual array expansion and denoising white Gaussian noise in comparison with SOC. The proposed algorithm, on the other hand, discards the original 2-D spectral peak search theory and uses the properties of the NC signal and parallel sparse array (PSA) to place the 2-D angle information in Toeplitz matrices for estimation, greatly reducing the computational complexity of processing the FOC without compromising estimation accuracy. Simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy while utilizing fewer physical sensors and a smaller array aperture. [ABSTRACT FROM AUTHOR]

In recent years, researchers have considered quantum steganography and its various methods with the development and progress of research in computation theory and quantum signals processing. The destructive use of quantum steganography methods to establish illegal covert communications is rising, so it is essential to introduce ways to detect hidden data in a quantum medium. Accordingly, this paper presents a frequency-based universal audio steganalysis approach to detecting quantum steganography. First, based on the quantum Fourier transform, the characteristic of the quantum spectrum centroid (QSC) was computed, and its circuit network was implemented to extract feature vectors. The proposed method classifies quantum audio signals using a quantum machine learning approach called a quantum ensemble of quantum classifiers. This approach was implemented within the framework of the Deutsch–Jozsa algorithm, which uses the superposition property to create an ensemble of classifiers evaluated in parallel, significantly increasing the computational speed. The accuracy weight of the classifiers is adjusted based on the classifiers' performance in training data classification; finally, the measurement of the first n qubits of the Deutsch–Jozsa algorithm predicts whether the quantum audio signals belong to the stego or clean class. The idea stems from the classic ensemble methods that try to build more robust models by combining different classifiers. The results show that the proposed frequency domain steganalysis method with 95% accuracy performs better than the previous methods in the time domain. [ABSTRACT FROM AUTHOR]

Single-window discrete Gabor transform with an adaptive window width selection algorithm (AWWS-DGT) and multi-window discrete Gabor transform (M-DGT) are the current mainstream analysis and processing methods for multi-component signals. However, the time–frequency accuracy in the time–frequency plane obtained by the two algorithms is not very high, and not all components of the signals that contain complex and very different time–frequency components can be displayed accurately. The linear combination window algorithm based on the ℓ 1 and ℓ 2 norm constraints of the transform coefficients can accurately display all the components of the signal, but the algorithm has poor anti-noise performance. In this paper, an algorithm for finding the best linear combination window under the sparse solution constraint of discrete Gabor transform coefficients is proposed. First, the optimal window number adaptive selection method proposed in this paper is used to select a set of window functions with different window widths, and the goal is to obtain the sparse solution of the transform coefficient. Through the gradient descent method, the best linear combination coefficient is obtained. Second, according to the best linear combination coefficient obtained, the selected window is combined into an analysis window function. Finally, the influence of the parameters on the combined window is further studied, and the results show that the number of windows has a great influence on the result and that the influence of the regularization parameter is negligible. Experiments on two simulated non-stationary signals and real electrocardiogram signals show that the proposed algorithm can display all components of the signals, the time–frequency accuracy is improved by 3% to 10%, and the anti-noise performance is more than 20% higher than that of the existing linear combination window algorithm. These advantages are very important for the analysis and processing of practical signals. [ABSTRACT FROM AUTHOR]

The tunable Q-factor-based wavelet transform (TQWT) is a novel method employed for the speech enhancement (SE) task. However, in TQWT, the controlling parameters Q-factor and the level of decomposition (J) are kept constant for different noise conditions which deteriorates the overall performance of SE. Generally, the performance of SE is calculated in terms of quality and intelligibility. However, it has been reported that these two evaluation parameters do not always correlate with each other because of the distortions introduced by the SE algorithms. These two important issues are addressed in this paper, and satisfactory solutions are provided by employing a multi-objective formulation to find the optimal values of the Q and J of the TQWT algorithm at different noise levels. In addition, to correctly estimate the appropriate values of Q and J from the unknown noisy speech, a low complexity functional link artificial neural network-based model is developed in this paper. To assess the performance of the proposed hybrid approach, subjective and objective evaluation tests are carried out using three standard noisy speech data sets. The results of the study are computed with six recently reported SE methods. It is demonstrated that in both the subjective and objective evaluation tests, the proposed hybrid approach outperforms the other six SE methods. [ABSTRACT FROM AUTHOR]

This paper presents a modified minimal realization technique to reduce single input single output (SISO) systems from higher-order SISO systems. The reduction process is based on restructuring the Hankel matrix, which consists of two major elements, i.e., Time Moments and Markov parameters. The system transformation is executed to reduce the order of the system by maintaining the desired system properties. The modified Hankel Matrix is proposed to obtain an expected reduce order model, i.e., kth order reduced model by selecting k × k order square matrix and using Silverman's algorithm. This paper presents a simple solution of model order reduction with the advantages of minimizing the steady-state error, fast convergence of steady-state behavior, better approximation in terms of rise time, peak time, and settling time at higher frequencies. Three different cases have been taken from the literature to validate the proposed technique with the comparisons of performance in terms of a quality check through performance indices and response matching between original and reduced-order models. [ABSTRACT FROM AUTHOR]

Conventional denoising algorithms have poor effect when dealing with nonlinear or non-stationary signals, and it is also difficult to select appropriate threshold parameters for denoising. This paper proposes a mixed-mode decomposition denoising algorithm based on variance estimation, which combines empirical mode decomposition (EMD) and variational mode decomposition (VMD). The algorithm performs a single EMD decomposition on an original signal, calculates an energy sequence of the first-order intrinsic mode functions, and estimates a noise variance in the original signal according to the noise energy attenuation characteristics, and the estimated variance value serves as the threshold for VMD denoising completion. Nonlinear signals and actual speech signals are selected to carry out experimental comparisons of various denoising algorithms. The results show that the mixed-mode decomposition denoising algorithm is not constrained by the selection of threshold parameters, and its denoising performance is better than that of conventional mode decomposition threshold denoising algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a double fractional order LMS algorithm (DFOLMS) based on fractional order difference and fractional order gradient, in which a variable initial value strategy is introduced to ensure the convergence accuracy of the algorithm. Through a model approximation, the DFOLMS is transformed into two fractional order difference models to analyze its convergence and steady-state properties indirectly. It is shown that the DFOLMS has different convergence characteristics in different difference intervals; meanwhile, a larger difference order α and gradient order β would lead to a faster convergence speed but a larger steady-state noise. Finally, the effectiveness and superiority of the proposed DFOLMS are demonstrated by simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, a new wirelength-driven placement model for UltraScale heterogeneous FPGAs have been developed and presented. The proposed algorithm applies a novel approach of utilizing the architectural information of the UltraScale heterogeneous FPGA to guide the placement from the initial stage of the proposed technique. The initial placement is followed by a cluster selection and net selection processes. The novelty of the proposed placement method lies in the fixed block-based cluster formation, segregation table and cell mapping to different placement sites. The segregation table and cell mapping methods are proposed to achieve a good global placement solution by consider a rough legalization and area congestion estimation. A new legalization procedure has been introduced to reduce legalization cost by employing delayed packing and cell mapping to BELs. A detailed placement phase is explored to refine the placement to achieve congestion free legal placement of cells with minimized wirelength. Experimental results have shown that the proposed algorithm achieves better results than the state-of-the-art FPGAs placement algorithms in terms of wirelength without using any expensive optimization framework. [ABSTRACT FROM AUTHOR]

In this paper, the estimation problem is considered for both sample delay and coefficients of ARMAX model. An extended recursive least squares algorithm is derived by minimizing a quadratic cost function. However, the solution of the optimization problem returns a real value for the sample delay. To overcome this difficulty, the rounding properties are used to transform the integer nonlinear problem into a real optimization problem. In addition, consistency of the estimates with their convergence rates are established under the persistent excitation condition. Finally, experimental results on semi-batch reactor are presented to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The traditional identification algorithms are generally based on the quadratic criterion functions and perform well under the interference of Gaussian noise. However, their performances become worse when non-Gaussian impulsive noise is encountered in practical situations. By introducing the maximum correntropy criterion and utilizing its robustness against outliers, this paper develops a robust recursive algorithm for the linear parameter-varying system with the non-Gaussian noise. The algorithm is not only simple and easy to implement, but also can resist the negative influence of the non-Gaussian noise. Moreover, a maximum correntropy criterion-based robust hierarchical algorithm is presented to increase the calculative efficiency. Three simulation examples are provided to demonstrate the validity of the proposed algorithms. [ABSTRACT FROM AUTHOR]

To make the zero attractor sign subband adaptive filter (ZA-SSAF) algorithm more suitable for sparse systems, where the impulse response is sparse and disturbed with impulse interference, this paper proposes an improved sign subband adaptive filtering algorithm that takes advantage of the splendid robustness of the arctangent function against impulse interference. Based on the ZA-SSAF algorithm, this algorithm introduces a proportionate coefficient matrix composed of a nonlinear function (the arctangent function) to assign different step sizes for the tap coefficients that need to be updated. The step size of the algorithm is updated in proportion to the magnitude of the weight coefficient in the adaptive process according to the relationship of the arctangent function, which greatly shortens the calculation convergence time and improves the overall convergence performance. The simulation results show that the proposed algorithm takes into account the trade-off between a faster convergence rate and a lower steady-state error and is superior to the traditional sign subband algorithm and zero attractor sign subband adaptive filtering algorithm in terms of the convergence rate and robustness against impulse noise. [ABSTRACT FROM AUTHOR]

The normalized least mean M-estimate (NLMM) algorithm exhibits good robustness in impulsive noises, but it suffers from the trade-off between convergence rate and steady-state misadjustment. In response to this problem, this paper proposes a switching step-sizes NLMM (SSS-NLMM) algorithm, which selects the optimal step-size at each iteration by comparing the mean square deviation (MSD) values of the algorithm with different step-sizes. Furthermore, to improve the convergence of the algorithm in the transition stage, we embed a feedback mechanism on the minimum MSD. Simulations in various environments have verified that the proposed SSS-NLMM algorithm has better performance than its counterparts in terms of convergence rate and steady-state misadjustment. [ABSTRACT FROM AUTHOR]

The generalized variable step-size diffusion continuous mixed p-norm (GVSS-DCMPN) algorithm is proposed in this paper, which is derived based on the improved continuous mixed p-norm (CMPN) strategy. In detail, a linear function is designed for the CMPN strategy. The proposed GVSS-DCMPN algorithm capable of exploiting various error norms to obtain performance improvement in non-Gaussian noise environment can be viewed as the generalization of the traditional p-norm-based algorithms in the sense of continuous errors. In particular, the GVSS-DCMPN algorithm transforms into the VSS-DCMPN algorithm when the slope of the linear function is set to 0. The computational complexity as well as the mean convergence is analyzed in the paper. Simulation results over the diffusion network show that the proposed algorithm achieves performance gain over some existing diffusion algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. [ABSTRACT FROM AUTHOR]

In this paper, we propose a noble algorithm for mixing matrix estimation in Underdetermined blind source separation. A concept of confidence measure and that of being able to be single source points at all the points in time frequency plane are introduced in the proposed algorithm. At first, we can detect the single source points from real parts and imaginary parts of time frequency coefficients of mixture signals and calculate principal vectors and its confidence measures through principal component analysis at the single source points using this algorithm. Finally, mixing matrix is obtained by clustering principal vectors according to its confidence measure. Experimental results show that the proposed algorithm is very suitable for actual situation of UBSS. [ABSTRACT FROM AUTHOR]

The time–frequency analysis (TFA) method is an effective tool to separate and extract main components for non-stationary signals such as vibration signals and seismic signals, which are time-varying and affected by high noise. However, suffering from the Heisenberg uncertainty principle and cross terms of time–frequency result, conventional TFA methods usually produce vague time–frequency representations (TFRs). As a branch of the TFA method, current redistributive compressive transforms enable to generate clear TFR. However, these techniques are limited to a singular type of signal, which is not applicable to deal with complicated signals in production. In order to enhance the applicability and the time–frequency (TF) aggregation capability, this paper proposes a promoted TFA method 2D-FTSST2 based on the synchrosqueezing transform combining two-dimensional information of time and frequency domains. For an accurate IF estimate, we also define a time redistribution operator, which can describe strong time and frequency-varying signals. This algorithm not only provides a high-resolution decomposition of multicomponent signals but also enables to extract main features in noisy environments. Experiments on simulated signals and real data confirm the validity and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

One of the main challenges in using adaptive filtering algorithms is efficiently emulating a system subject to noisy disturbances. This can be facilitated in applications where the system response to impulse is sparse, which allows for acceleration of the convergence rate if appropriate strategies are used. As a result, methods that impose norm constraints on the estimates are widely used. However, in the case of non-stationary plants to be identified, there is a gap in terms of theoretical performance guarantees of these algorithms. This paper proposes a novel stochastic model capable of predicting the performance of the ℓ0\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\ell _0$$\end{document}-LMS algorithm in identifying a plant subjected to a first-order Markovian disturbance. Therefore, a tracking analysis is carried out, including both the average performance of the adaptive coefficients and second-order statistics of these coefficients. The theoretical model offers an analytical equation that predicts the asymptotic mean squared deviation in terms of the variance of the Markovian disturbance. Further, for most simulated scenarios, the theoretical model’s error in mean squared deviation remains below 1 dB, even when the learning step varies across several orders of magnitude. It was possible to observe that the theoretical model can accurately predict the steady-state regime for a wide range of learning step values and calculate an optimal value for this parameter. The findings are confirmed through extensive simulations. [ABSTRACT FROM AUTHOR]