The least mean square (LMS) algorithm is optimal for combating Gaussian noises owing to the used minimum mean square error (MSE) criterion in its loss function. However, the MSE criterion is not efficient for non-Gaussian noises. To this end, a new robust minimum kernel risk sensitive mean p-power loss (MKRSP) algorithm is proposed to provide robustness against impulsive noises and improve filtering

Most of the speech processing applications use triangular filters spaced in mel-scale for feature extraction. In this paper, we propose a new data-driven filter design method which optimizes filter parameters from a given speech data. First, we introduce a frame-selection based approach for developing speech-signal-based frequency warping scale. Then, we propose a new method for computing the filter

The classical distributed sparse regression based on least square error is sensitive to the outliers in the desired data. In this manuscript, we consider the rank based estimator named minimum Wilcoxon norm for developing robust non-parametric sparse regression over distributed adaptive networks. The convergence analysis of the proposed algorithm is analyzed using asymptotic linearity of rank test

The heartbeat interval (HBI) signal (RR-time series), and electrocardiogram (ECG) derived respiration (EDR) signal quantify the information about the cardiopulmonary activity, and monitoring these two signals simultaneously will provide more information for the sleep apnea detection. This paper proposes a novel approach to detect sleep apnea using both HBI and EDR signals. The approach consists of

Model order and frequency estimation of multiple 2-D complex sinusoids in additive white Gaussian noise are addressed. Frequency estimation follows the coarse-fine search strategy. Coarse estimates, obtained by locating maxima of the 2-D discrete Fourier transform, are refined in a two-stage procedure. In both stages, frequency refinement is based on three-point periodogram maximization. In order to

With the advent of biomedical imaging systems and the rapid advancements in light microscopy, multi-focus image fusion has provided significant attention in creating a fused image with improved depth-of-field (DOF). The fused image plays an imperative role in different computer vision based applications. Firstly, the proposed algorithm decomposes the source multi-focus images into base layer (BL) and

In this paper, we consider a three-dimensional parameter space, which is the time-frequency-chirprate (TFCR), to characterize the time-varying features of multi-component non-stationary signals. By performing reassignment on the frequency-chirprate plane, a highly concentrated TFCR representation, named as the frequency-chirprate reassignment method (FCRM), is proposed. FCRM can provide the instantaneous

To locate mixed far-field (FF) and near-field (NF) sources, most existing methods require two or three steps to first find the direction-of-arrival (DOA) and then retrieve the range parameters based on the DOA estimates. Hence, the range estimation performance highly relies on the DOA estimation accuracy in these methods. On the other hand, the MUSIC-like or on-grid sparse methods require discretization

In the graph signal processing literature, most methods were developed based on the assumption of Gaussian noise since it can lead to computationally efficient and mathematically tractable solutions. Unfortunately, the Gaussian distribution cannot capture the sharp spikes and tail heaviness of the signal noise in various natural phenomena. The α-stable distribution is a generalization of the Gaussian

A statistical analysis of the least squares matching methods for single-tone frequency estimation is carried out in this paper. Based on asymptotic derivations, an accurate frequency variance expression is provided for different least squares matching methods with variable parameters. Two representative least squares matching methods are involved to show the validity of the variance expression, however

In this paper, we investigate the performance of various nature-inspired optimization algorithms in the design and analysis of non-uniformly spaced arrays. These algorithms include covariance matrix adaptation evolution strategy (CMA-ES), evolutionary programming (EP), particle swarm optimization (PSO), genetic algorithm (GA), and fireworks algorithm (FWA). First, we compare the performance of these

In modern radar system, constant false alarm rate (CFAR) detection is a key technique for automatic target detection in unknown and nonstationary environment. Recently, a novel Bayesian methodology has been introduced for the design of CFAR detector. This method uses Bayesian predictive inference approach to produce a predictive density of the cell under test (CUT) conditioned on the reference samples

Voice Onset Time (VOT) has been used by researchers as an acoustic measure in order to gain some understanding about the impact of different motor speech disorders in speech production. However, VOT values are usually obtained manually, which is expensive and time consuming. In this paper we proposed a method for the automatic detection of VOT based on pre-trained Recurrent Neural Networks with Gated

Fourier decomposition method (FDM) is an adaptive data-driven time-frequency analysis tool developed recently for nonlinear and non-stationary time series. This method intrinsically decomposes any signal into a small number of band-limited zero-mean orthogonal functions called as Fourier intrinsic band functions (FIBFs) via zero-phase ideal band-pass filters, and can represent the signal by these functions

In this paper an audio coding scheme based on the empirical mode decomposition in association with a psychoacoustic model is presented. The principle of the method consists in breaking down adaptively the audio signal into intrinsic oscillatory components, called Intrinsic Mode Functions (IMFs), that are fully described by their local extrema. These extrema are encoded. The coding is carried out frame

Antenna array-based joint radar-communication (JRC) system employs the beamforming strategy to steer the transmit signals in different directions such that different beamforming weight vectors are exploited which perform the corresponding radar-communication objectives. Such functionality has been provisioned in the spectrally congested environments where both systems are destined to coexist. The transmit

Discrete dynamical systems are widely used in a variety of scientific and engineering applications. Modeling these systems involves performing statistical analysis of the system output to estimate the parameters of a model so it can behave statistically similar to the original system. These models can be used for simulation, performance analysis, fault detection, among other applications. This work

This paper discusses a content based remote sensing image retrieval technique using multi-scale, patch-based local ternary pattern and fisher vector encoding. The technique downsamples an image in three scales, each downsampled image utilizes local ternary pattern to obtain upper and lower texture images, and divides them into dense patches to build a final histogram representation. This representation

Correlation is an index indicating the similarity of different data set, and a correct calculation of correlation or correlation coefficient is very important in science and engineering. However, in many real word applications, the signal will be contaminated by structured impulsive noise, then the generally used correlation or correlation coefficient will fail to correctly express the statistical

The movement of the background caused by the movement of a sensor platform has always been a major challenge in space-based infrared weak and small target tracking. In addition to clutter, false alarms, noise, and other disturbances, the most serious source of interference in space-based infrared weak target tracking comes from the stars. This kind of interference always exists and is large enough

This paper proposes a new approach to quality factor identification for a JPEG images. This approach is based on a spatial domain model of the variance of 8×8 blocks of JPEG images. The variance model follows a Gamma distribution, which is characterized by two parameters. These parameters are considered as a unique fingerprint for determining the value of the quality factor. To demonstrate the benefits

In this paper we consider a wireless receiver model that employs the macro diversity (MD) technique, consisting of a selection combining (SC) receiver fed by two micro diversity (mD) SC branches, which is used to simultaneously reduce the effects of Nakagami-m short term fading, Gamma long term fading, and co-channel interference on the system performances. The interfering signal is also affected by

This paper studies the problem of estimating a parameter vector from measurements having a multivariate chi-squared distribution. Maximum likelihood estimation in this setting is unfeasible because the multivariate chi-squared distribution has no closed form expression. The typical approach to go around this consists in considering a sub-optimal solution by replacing the chi-squared distribution with

This paper studies adaptive detection of distributed targets in interference and noise. The signal and interference are assumed to lie in two known subspaces which are partially related; this denotes the cases where the signal and interference cannot be completely separated in spatial, temporal and frequency domains. Since impartible, the signal and interference are recast as one with the aid of singular

In this paper, we propose a deep neural network (DNN) ensemble for reducing artificial noise in speech bandwidth extension (BWE). The proposed DNN ensemble consists of three DNN models; one is a classification model, and the other two are regression models. When estimating sub-band energies of the high-frequency region using sequential DNNs in a frequency domain, the over-estimation of sub-band energies

In the computer vision, object detection has always been considered one of the most challenging issues because it requires classifying and locating objects in the same scene. Many object detection approaches were recently proposed based on deep convolutional neural networks (DCNNs), which have been demonstrated to achieve outstanding object detection performance compared to other approaches. However

This paper deals with adaptive detection of distributed targets in the presence of signal mismatch in compound Gaussian clutter. The texture is assumed random to match the non-Gaussian environment. To increase the capability of rejecting mismatched signals, a fictitious subspace signal is introduced under the null hypothesis. Under the above assumptions, we propose two adaptive selective detectors

Scheduling of the sensor nodes has attracted the interest of the research and scientific community by its ability to prolong the lifetime of the wireless sensor networks (WSNs). Full coverage of the target/region and connectivity between the scheduled sensor nodes is an important issue while scheduling a smaller set of sensor nodes only. In this paper, an NSGA-II with modified dominance has been proposed

Over the last decade remote photoplethysmography (rPPG) algorithms have been developed extensively. As a result, pulse rate can now be accurately estimated by video data for still subjects. However, in realistic conditions both the accuracy of these algorithms and benchmark datasets are far from perfect. In this paper we propose a new rPPG method which enables heart rate detection by video from a standard

In this paper, we mainly study the distributed estimation problem under attacks, which is mainly used to estimate an unknown parameter. To solve this problem, a correction-based secure diffusion least mean square (CS-dLMS) algorithm, which is a hybrid algorithm that composes of a non-cooperative LMS (nc-LMS) algorithm and a correction-based diffusion least-mean squares (C-dLMS) algorithm, is proposed

We introduce efficient online learning algorithms based on the Long Short Term Memory (LSTM) networks that employ the covariance information. In particular, we introduce the covariance of the present and one-time step past input vectors into the gating structure of the LSTM networks. Additionally, we include the covariance of the output vector, and we learn their weight matrices to improve the learning

Savitzky-Golay (SG) filters are finite impulse response (FIR) realizations of least-squares polynomial regression and they are widely used for filtering (e.g. smoothing, interpolating, predicting, differentiating) and processing (e.g. detecting and classifying) non-stationary signals in non-Gaussian noise. For such inputs, the Wiener filter is biased and the Kalman filter is sub-optimal. Sequentially-correlated

For bearing fault signal classification, the performance of the affinity propagation (AP) method is limited by its way of measuring similarity. In this paper, we have proposed an improved version for the AP method, which is termed as IMFSE-AP. The key point of the proposed IMFSE-AP method is the improvement on the way of measuring signal similarity. Instead of the Euclidean distance, a novel method

Recently, large scale video content retrieval has gained more attention due to a large amount of user-generated images and video content available over the internet. Hashing is one of the effective techniques that encode the high dimensional features vectors into compact binary codes. The aim of hashing is to generate the short binary codes and map the similar hash code values to retrieve similar video

We propose a blind image deblurring model based on the group sparse representation (GSR) prior. The motivation for this work is that adjacent similar patches of images and blur kernels favor clear images and ground-truth kernels over blurred images and poor kernels. Therefore, we impose GSR constraints on similar patches based on images and kernels to ensure the sparsity of the intermediate latent

The problem of direct position determination (DPD) with single moving array in the presence of deterministic sensor gain and phase errors is addressed. To reduce the loss in localization accuracy due to these errors, a calibration emitter with known position is introduced. The Cramér-Rao lower bound (CRLB) for estimating these errors using this calibration emitter are first derived in two situations

Sparse array motion can efficiently expand the numbers of achievable degrees of freedom (DOFs) and consecutive lags, improving direction-of-arrival (DOA) estimation. Sparse arrays on a moving platform benefit from motion translation that introduces new sensor positions, which collectively with the original positions can increase the number of spatial autocorrelation lags and lead to full array augmentability

Digital beamforming (DBF) arrays with a large number of small antennas are extensively employed in millimeter-wave (mmWave) sensing systems. Sparse arrays have been posed as an attractive solution to mmWave systems due to their capability of striking the best compromise between performance and complexity. We propose two iterative antenna selection strategies, referred to as deterministic selection

We consider sparse array beamformer design achieving maximum signal-to interference plus noise ratio (MaxSINR). Both array configuration and weights are attuned to the changing sensing environment. This is accomplished by simultaneously switching among antenna positions and adjusting the corresponding weights. The sparse array optimization design requires estimating the data autocorrelations at all

Optimal sensor selection for source parameter estimation in energy harvesting Internet of Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement

As an important component of the economic market, the stock market has been concerned by many researchers. How to get the trend of the stock market and predict the stock price is a problem that many researchers are studying. In previous works, the prediction methods are mainly focused on statistical models and traditional neural network models which are relatively popular in recent years. Deep learning

Multichannel multicomponent signals can be decomposed into individual signal components by exploiting the eigendecomposition of the corresponding autocorrelation matrix. Recently, we have shown that such decomposition is possible even in the particularly challenging case of non-stationary components with significantly overlapped supports in their time, frequency, and joint time-frequency domains. Each

In this study, a novel multi-objective speech enhancement algorithm is proposed. First, we construct a deep learning architecture based on a stacked and temporal convolutional neural network (STCNN). Second, the main log-power spectra (LPS) features are input into a stacked convolutional neural network (SCNN) to extract advanced abstract features. Third, an improved power function compression Mel-frequency

Compressive sensing (CS) has been widely used in wireless sensor networks (WSNs). In WSNs, the sensors are battery-powered and hence their communication and processing powers are limited. One of the dominant features of the CS is its complex recovery phase. Thus, great care should be taken into account when designing the CS recovery algorithm for WSNs. In this paper, we propose a distributed and cooperative

This paper studies the statistical behavior of a switched variable step size Normalized Least Mean Square adaptive filter. The purpose of the switching is to obtain a Normalized Least Mean Square adaptive filter combination with fast convergence and small steady-state mean-square weight deviation (MSD). Previous works studied variable step-size Least Mean Square (LMS) and Normalized Mean Square (NLMS)

In digital histopathological image analysis, two conflicting objectives are often pursued: closeness to the original tissue and high classification performance. The former objective tries to recover images (stains) that are as close as possible to the ones obtained by staining the tissue with a single dye. The latter objective requires images that allow the extraction of better features for an improved

Graph signal reconstruction is a fundamental topic in graph signal processing. Recently, graph signal reconstruction in three typical sampling schemes has been studied in the literature. In this paper, we study the reconstruction of bandlimited graph signals in a general measurement scheme, which includes the three existing sampling scheme as its special cases. Theorems for perfect reconstruction are

Applications employing Global Navigation Satellite Systems (GNSS) to provide accurate positioning are subject to drastic degradation not only due to electromagnetic interference, but also due to multipath components caused by reflections and refractions in the environment. Typically, the higher the correlation between the line-of-sight (LOS) component and the remaining multipath components, the more

In this paper, nonstationary sea clutter time sequence, and corresponding amplitude sequence and phase sequence are modeled as statistically similar fractional-order Brownian motion (fBm) processes at different time scales. Their Hurst exponents form an all-dimensional description of fractal characteristics of sea clutter time sequence. Radar returns with targets and sea clutter exhibit salient differences

The detection and tracking of multiple weak targets with time-varying and unknown number has become a hot spot and challenging problem in marine radar application. This paper integrates δ-generalized labeled multi-Bernoulli (δ-GLMB) density and labeled multi-Bernoulli (LMB) density, which are two important special cases of labeled random finite set, to propose an effective method for multiple weak

Autoregressive (AR) spectrum has the advantage of high frequency resolution over the Fourier spectrum in sea clutter analysis. In recent years, the multi-scale Hurst exponents are widely used in describing the AR spectrum of sea clutter due to their abundant clues about the local roughness of the sea clutter in different scale intervals. In this paper, a method based on whole-scale Hurst exponent of

A low complexity ground-penetrating radar (GPR) clutter removal method based on lattice filter (LF) decomposition is proposed. The GPR image is decomposed into several levels by LF where each level contains approximation and directional subbands. Due to its horizontal structure, the clutter is contained in a horizontal subband, while vertical and diagonal subbands represent the target information.

A dual-function radar-communications (DFRC) system based on circulating code array is designed in this paper. The communication symbols are firstly embedded into the continuous phase modulation (CPM) term and then attached to the transmitted base waveform. The particular configuration of the circulating code array provides a possibility to recover the spectrum of the transmitted basic waveform. According

This paper derives an identification algorithm for Hammerstein nonlinear systems with dynamic disturbances and measurement noise. The dynamic disturbance is viewed as a time-varying sequence to be estimated and its model structure and excitation signal are not considered. By extending the parameter and information vector, an extended recursive least squares algorithm is proposed first time to identify

Although many saliency models for images had been proposed, the saliency model for stereoscopic omnidirectional image (SOI) is still a very important but not yet deeply studied topic. In addition, one of main disadvantages of the existing saliency models is difficult to simulate the characteristics of a wide field of view (FOV) of SOIs. To solve the problems, this paper proposes a novel cube map projection

The emerging cognitive radio technology resolves the spectrum scarcity problem by exploiting the unutilised licenced spectrum on an opportunistic basis. Meanwhile, future cognitive devices require advanced sensing techniques for rapid and active identification of spectrum holes over a wideband. Incorporation of prior information from the geolocation database enhances the sensing performance, reduces