In this paper, we propose a new MIMO communication system in a time-varying multipath environment, using a Khatri-Rao space-time (KRST) coding combined with a multiple Khatri-Rao product of symbol matrices (MKRSM). It is shown the signals received at the receiver form a tensor which satisfies a (M+2)-order nested PARAFAC model, where (M−1) denotes the number of symbol matrices considered for MKRSM

In this work, we have analyzed the pitch robustness of the recently reported power normalized cepstral coefficient (PNCC) feature for noise robust children's speech recognition. The PNCC feature is intended to suppress various types of common additive noise present in speech data. The PNCC feature is noted to be susceptible to pitch variations. In order to normalize the pitch effect on PNCC, a pitch

With the advances in electronic countermeasures (ECMS), especially the emergence and development of active jammers, there is an urgent demand for anti-jamming techniques. In this paper, we proposed a cognitive active anti-jamming method based on frequency diverse array (FDA) radar phase center. For the uniform linear FDA (ULFDA) radar, we derive the closed form of phase center, based on which the regulation

Combining segmentation, classification, and time-fractional diffusion filtering, an excellent smoothing method of peak-preserving is proposed. First, the signal is divided into equal-length segments. Second, these segments are classified according to the similarity. Third, similar segments in the same class are stacked into a two-dimensional array, and then they are filtered by the two-dimensional

The non-linear frequency modulation (NLFM) waveform is one of the existing waveforms that can be used in high range resolution radar applications. However, a high sampling frequency and consequently an expensive ADC are required. To overcome this drawback while taking advantage of the features of the NLFM waveform, we suggest approximating the wideband NLFM waveform by a piecewise linear waveform and

Several emerging application scenarios for the multi-hop wireless networking have been introduced in 5G and beyond networks, to provide ultra-low latency and much higher data rate to the end users. In this work, an integrated performance evaluation of a decode-and-forward (DF) multi-hop wireless communication system is undertaken over the non-linear generalized α−κ−μ and α−κ−μ-Extreme fading models

The least squares based affine projection algorithm (APA) is sensitive to outlier/impulsive noise in the desired data. A novel robust APA over distributed networks scenario is proposed in this manuscript, which is based on the rank based robust estimator named Wilcoxon norm. The proposed diffusion Wilcoxon affine projection algorithm (DWx-APA) based on pseudo least squares formulation is robust against

This paper addresses the multichannel adaptive detection of signal with unknown amplitude in the presence of coloured Gaussian interference. The interference is modelled as an autoregressive (AR) process with regard to its spatial and temporal correlation. We propose a new parametric detector in which the ability to reject fictitious targets is considered by including a mismatched signal in the null

This paper considers jointly designing the digital beamformer (DBF) and analog beamformer (ABF) for multi-user downlink hybrid beamforming systems in order to enhance communication performance and reduce information leakage. Our goal is maximizing the minimum signal-to-interference-plus-noise ratio (SINR) while depressing the maximum sidelobe level (SLL) of all users. Since the problem is difficult

This paper proposes the beta binomial autoregressive moving average model (BBARMA) for modeling quantized amplitude data and bounded count data. The BBARMA model estimates the conditional mean of a beta binomial distributed variable observed over the time by a dynamic structure including: (i) autoregressive and moving average terms; (ii) a set of regressors; and (iii) a link function. Besides introducing

The detrended fluctuation analysis (DFA) and its variants such as the detrended moving average (DMA) are widely used to estimate the Hurst exponent. These methods are very popular as they do not require advanced skills in the field of signal processing and statistics while providing accurate results. As a consequence, a great deal of interest has been paid to compare them and to better understand their

An enhanced intelligent fault diagnosis method is proposed based on pairwise graph regularized deep belief network (PG-DBN) model. In this novel framework, two different graph constraints are imposed on hidden layer of the Restricted Boltzmann Machine (RBM). The first graph constraint defines the representation of preserving the feature manifold structure in same class of the data and the second graph

Estimation of haplotype sequences from DNA sequencing samples is a challenging task whose mathematical formulation leads to an NP-hard problem. Also, accuracy of the estimates plays an essential role in providing the required information for personalized medicine. In order to fully incorporate the available quality of measurements with higher accuracy into the estimates, in this paper, we propose a

To mitigate the leakage signal that obstructs the target information extraction in miniatured frequency modulated continuous wave (FMCW) radar system, an adaptive leakage signal cancellation algorithm is proposed in this paper. Without any help of the additional auxiliary path, all the processing for the intermediate frequency (IF) signal can be conducted in the digital domain. The basic principle

In this paper, we consider the problem of two-dimensional (2-D) direction of arrival (DOA) estimation for L-shaped monostatic MIMO radar with two-level nested linear array (NLA). To obtain more degrees of freedom (DOFs) than traditional nested L-shaped (NLs) MIMO radar, we propose the extensional NLs (ENLs) MIMO radar by extending all of the sensor positions in transmitter array with an identical magnification

This paper focuses on the weak maneuvering target detection problem in the alpha-stable noise (ASN) environment. A novel coherent integration framework is proposed, where the range migration (RM) is first removed via approximate linear methods to make the noise distribution character remain unchanged, and then the ASN suppression, Doppler frequency migration (DFM) compensation, and coherent integration

As the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems is dramatically increased, linear detection methods are able to achieve the near optimal performance at the expense of performing the complicated matrix inversion of a high dimensional matrix, which makes hardware implementation infeasible. Meanwhile, the straightforward application of the hard symbol-to-bit

We develop a data-driven approach for signal denoising that utilizes variational mode decomposition (VMD) algorithm and Cramer Von Misses (CVM) statistic. In comparison with the classical empirical mode decomposition (EMD), VMD enjoys superior mathematical and theoretical framework that makes it robust to noise and mode mixing. These desirable properties of VMD materialize in segregation of a major

In this paper, a one-step method is proposed for the classification and estimation of the parameters in the scenario of mixed near-field and far-field stationary sources. By constructing several temporal-spatial cumulant matrices, utilizing the stationary property of sources, and introducing a mechanism for controlling the rank, a parameters estimation matrix is formed. This matrix contains electric

Compared with the traditional subspace methods, the application of compressed sensing (CS) to the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar can achieve robustness to coherent targets and high localization accuracy with a limited number of snapshots. However, the performance of grid-based CS reconstruction methods degrades

Different transmit antenna elements in a frequency diverse array (FDA) radar transmit different frequencies. This makes the transmitted beam to continuously change its orientation, and enables an FDA to scan the space around itself in a simple manner. Furthermore, an FDA can find the range as well as the direction of each target simultaneously. The achievable resolution of direction estimation depends

Perceptual video coding (PVC) can eliminate visual redundancy and effectively improve the compression performance of ultra-high definition video. The conventional PVC schemes, involving the perceptual characteristics related to the video features, do not account for interference in the human visual system caused by external environment changes. Therefore, this paper presents a PVC scheme based on a

Signal scaling is a fundamental operation of practical importance in which a signal is made wider or narrower along the coordinate direction(s). Scaling, also referred to as magnification or zooming, is complicated for signals of a discrete variable since it cannot be accomplished simply by moving the signal values to new coordinate points. Most practical approaches to discrete scaling involve interpolation

Facial expression recognition has recently received an increasing attention due to its great potentiality in real world applications. Conventional facial expression recognition is often conducted on the assumption that training data and testing data are obtained from the same dataset. However, in reality, the data are often collected from different devices or environments, which will severely degrade

This paper addresses the direction of arrival (DOA) estimation via an acoustic vector sensor (AVS) array in the presence of non-orthogonal factors. To mitigate the estimation bias, which is caused by non-orthogonal factors, a novel DOA estimator is proposed by combining the iterative sparse maximum likelihood-based and maximum a posteriori (ISML-MAP) approaches. First, the two non-orthogonal AVS array

End effects of Local Mean Decomposition (LMD) are regarded as a typical problem leading to a distorted decomposed waveform and interfere with the extraction of characteristics. This paper proposes a novel self-adaptive point extension approach based on a Bidirectional Long Short-Term Memory (Bi-LSTM) regression network to eliminate this problem. This approach divides the existing samples into two parts

In large scale information fusion networks the statistical dependencies between nodes are rarely known. Failing to account for this detail in the design of distributed estimation schemes may lead to statistical inconsistencies and may even cause some estimators to diverge at various nodes within the network. Among the approaches that have been proposed to address this problem, covariance intersection

One of the most interesting problems in denoising of images includes despeckling of multiplicative noise. This paper proposes a novel statistical processor in the framework of Non-sub Sampled Shearlet Transform (NSST) to reduce the effect of the multiplicative noise on images given preserving of structural and visual quality of image. First, we indicate that NSST coefficients of logarithmically transformed

Recently, deep-learning-based blind picture quality measurement (BPQM) metrics have gained significant attention. However, training a robust deep BPQM metric remains a difficult and challenging task because of the limited number of subject-rated training samples. State-of-the-art full-reference (FR) picture quality measurement (PQM) metrics are in good agreement with human subjective quality scores

Conventionally adaptive detection for frequency diverse array (FDA) radar requires extra training data to estimate the range-dependent interference covariance matrix (ICM). In this paper, we propose an adaptive moving target detection approach for FDA radar without training data, where the ICM is directly estimated by applying a structured generalized likelihood ratio test (SGLRT) to the target-present

With the development of the generative model, image synthesis has become a research hotspot. This paper presents a novel Cross-modal Feature Alignment based Hybrid Attentional Generative Adversarial Networks (CFA-HAGAN) for text-to-image synthesis. It mainly consists of two steps, text-image encoding and text-to-image synthesis. Text-image encoding learns a Cross-modal Feature Alignment Model (CFAM)

Linear zero forcing (ZF) precoding can obtain near-optimal performance as the number of base station (BS) antennas increases, but involve complex matrix inversion calculations whose computational complexity is cubic with respect to the number of user equips (UEs) in massive multiple-input multiple-output (MIMO) systems. In this paper, a low-complexity modified successive over-relaxation-based ZF (MSOR-ZF)

A categorical study on iris localization, its challenges and techniques is carried out here. This survey comprehends the historical evolution and present state-of-the-art in iris localization. Most research publications include their primary contribution in iris localization based on tools like Hough Transform, Integro-differential Operator, Active Contour and Deep Learning etc. Majority of the techniques

This article presents new expressions for the bit error probability (BEP) of the M-ary quadrature amplitude modulation (M-QAM) and rectangular QAM (R-QAM) scheme in a frequency non-selective channel model with cascaded double fading, characterized in the links by the distributions η-μ, κ-μ or α-μ. In this paper, the probability density function (PDF) of the signal-to-noise ratio (SNR) of the double

Signal parameter estimation like direction of arrival (DOA), polarization and Doppler frequency for conformal arrays with a reasonable computational effort is cumbersome, because conformal arrays have complex steering vectors without Vandermonde structure but involves distinct antenna patterns. To this end, we propose a fast estimation algorithm which enjoys computational benefit stemming from processing

Model-based adaptive signal decomposition algorithm is an important research topic in the field of signal analysis. The amplitude modulated and frequency modulated (AM-FM) model is the most common model in the signal. In this paper, an adaptive signal decomposition algorithm based on the complex AM-FM model is proposed. This algorithm first obtains the analytical expression of the analyzed signal;

Due to their nice properties, unimodular sequences possessing low autocorrelation sidelobes have already been employed as transmit waveforms in numerous radar systems. It could also be desired to have a transmit waveform which avoids certain frequency bands allocated for specific communication applications. Hence, adaptation and specific design of the waveform spectrum might also be essential. Accordingly

Recently, the kernel based constrained adaptive filtering algorithm has attracted a lot of attentions because of its robustness and superiority over traditional methods. As two classic kernel based algorithms, both constrained maximum correntropy criterion (CMCC) and constrained minimum error entropy (CMEE) have shown their superiorities in the case of non-Gaussian noise. However, both algorithms use

The anisotropic total variation (TV) denoising model suppresses noise for two-dimensional signals that are vertically and horizontally piecewise constant. However, two-dimensional signals may have sparse derivatives in other directions. We propose a modification of the classical anisotropic two-dimensional TV regularizer from a spectral point of view. In the frequency domain, the TV regularizer can

In machine-type communications (MTC), a large number of devices are connected to an access point (AP), but only a few devices are active at a time. This sparse activity of devices makes compressed sensing (CS) technique a possible solution for joint activity detection and channel estimation problem in MTC. In this paper, we improve the performance of greedy CS based detection in MTC, by exploiting

Orthogonal frequency division multiplexing (OFDM) systems based on discrete fractional Fourier transform (DFRFT) chirp basis replacing the complex exponential basis of discrete Fourier transform (DFT) is extensively studied in the literature. Authors of this paper have recently proposed fractional Fourier domain equalization (FFDE) for an OFDM system with a discrete cosine transform (DCT) multicarrier

Compressed Sensing theory breaks through the limitation of the Nyquist sampling law and provides theoretical support for accelerating the imaging process of MRI while reconstructing high-quality images. It can use less sampling data through the reconstructed algorithm to restore the original signal. Reconstruction time and reconstruction algorithms play important roles in compressed sensing. However

In practical application, the time when the maneuvering targets enter and leave the radar coverage observation is usually unknown, which will seriously deteriorate the performance of coherent integration and target detection. To solve this problem, we propose a method based on the symmetric autocorrelation function and the proposed window Lv's transform (SAF-WLVT) for coherent integration and motion

Direction-of-arrival (DOA) estimation is a fundamental problem in many applications of array signal processing. In practice, it is more important to track the DOAs of moving target signals. In this paper, we study the problem of tracking the DOAs of multiple signals in the frequency diversity array (FDA)-MIMO radar system. Aiming at FDA signals, we propose an efficient algorithm for DOA tracking, which

3D-high efficiency video coding (3D-HEVC) is the latest extension of HEVC standard. Through adopting novel coding tools, 3D-HEVC could improve the compression efficiency of multi-view texture video. However, with the great bitrate savings, 3D-HEVC brings the extreme complexity. To reduce the encoding time of texture video, a fast 3D-HEVC method based on just noticeable difference (JND) model is proposed

In this work, we address the problem of tracking multiple acoustic sources by using an array of microphones in reverberant and noisy environments. Generally, the time difference of arrival (TDOA) measurements are obtained by the peak extraction of the Generalized Cross Correlation (GCC) function, and then used to update source states in the framework of target tracking. In adverse environments, due

The noise generated by some rotating machines usually presents a mixture of tonal and chaotic signals. The hybrid functional link artificial neural network (HFLANN) consisting of sinusoidal noise canceller (SNC), narrowband ANC (NANC) and FLANN subsystems was a great success for this kind of noise. However, it has no ability to overcome nonlinear acoustic path and frequency mismatch, and its convergence

We present a deep network-based blind image quality assessment (BIQA) using two-side pseudo reference (TSPR) images. Different from a traditional reference with the perfect quality, two opposite images are introduced as pseudo references, which are generated via a restoration model and a degradation model, respectively. The bilateral distance (error) maps between the TSPR images and the pristine distorted

Distributed multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) configuration, which is composed of multiple distributed dual-function transmitters, multiple radar receivers and multiple communication receivers, are capable of performing target state estimation and information transferring tasks simultaneously. In this article, the authors put forward a low probability of

In the direction estimation of noncircular signals, the sensor array can be virtually extended by adding its complex conjugated version. Exploiting the centro-Hermitian of the extended signal vector without specific requirements of array geometry, we present a computationally efficient method for direction estimation based on the multiple signal classification (MUSIC) principle. When the MUSIC-like

This paper investigates the problem of separating galaxy spectra resulting from the slitless spectroscopy system, which can e.g. be used in spatial missions like the WISP survey and the Euclid project. We first derive a physical mixing model linking observed data to source spectra, then simplify it to obtain two different approximate but realistic models. The first simplified model being entirely defined

Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end

The multiple signal classification (MUSIC) algorithm is widely used in direction of arrival (DOA) estimation. Conventional MUSIC-like algorithms suffer from the heavy computational burden imposed by the two-dimensional (2-D) angle search and an exhaustive spectral search. We propose a two-stage unitary spherical harmonics MUSIC (TSU-SHMUSIC) that converts 2-D MUSIC into two new one-dimensional (1-D)

Target detection is a challenging problem in the complex detection environment of high-frequency surface wave radar (HFSWR). Due to the existence of various clutter and interferences, the detection environment in HFSWR is highly non-homogeneous with sharp/smooth clutter edges and multiple interfering targets. In this paper, we propose an effective detector for the non-homogeneous Weibull clutter in

Currently, sparse Bayesian learning (SBL) has been introduced to solve direction of arrival (DOA) estimation in different situations. In the line of DOA estimation under impulsive noise, existing SBL-based methods need large computation which will restrict their practicabilities. To address this problem, we propose an efficient method based on a real-valued SBL for non-circular signals in this paper