In recent years, distributed beamforming has attracted a lot of attention. Since each node has its own processing power, one significant advantage is the capability of distributed computing. In general, almost all distributed beamforming approaches are solving certain multi-block optimization problems. However, additional conditions are usually required to ensure convergence. In this paper, a new distributed

In this paper, a novel and complete derivation of the range doppler algorithm (RDA) formulation is developed for stepped frequency continuous wave (SFCW) synthetic aperture radar (SAR) usage. As the RDA has been originally developed for linear frequency modulated (LFM) pulse excited SAR data, the detailed mathematical derivation of SFCW based RDA formulation is presented with similarities, differences

This work studies the near-field localization problem using a symmetry uniform linear array (ULA). To decouple the range and direction of arrival (DOA), by exploiting the symmetry property of the array, two spatial correlation sequences are constructed, where each sequence only corresponds to one parameter, i.e, DOA or range. After decoupling, an attractive property is that the resulting sequences

This paper focuses on the error probability performance of LoRa, a long-range low-power wireless communication technology suited for the Internet of Things (IoT). We propose accurate approximations for the bit error probability of the uncoded LoRa chirp modulation in additive white Gaussian noise (AWGN) channel. Our approach, which is based on a smart modification of a union bound on the error probability

Existing stereoscopic 3D (S3D) salient object detection (SOD) networks typically employ a two-branch architecture, in which the RGB and depth channels are learned independently. Conventional methods based on conventional neural networks generally fuse the two branches by combining their deep representations at a later stage with only one path, which can be inefficient and insufficient for retaining

Object proposal quality assessment without ground truth as reference is a challenging task. Some existing methods measure the quality with hand-crafted metrics for subjective metrics, such as objectness and foreground confidence. Recently, deep learning is adopted for direct assessment for quantifiable metric, such as Intersection over Union (IoU). However, we find that IoU, the commonly used quality

This paper presents analytical superdirectivity solutions for circular arrays with acoustic particle velocity sensors (APVSs) based on the eigenbeam decomposition and synthesis theory. The main findings are summarized as follows. (1) The optimal beampattern, the global directivity factor, and the total error sensitivity function of circular APVS arrays, like those of circular arrays with pressure sensors

Downlink multiuser network coding (NC) automatic repeat request (ARQ) systems can provide significant throughput improvements over basic ARQ systems. In this paper, we consider the case of two-unicast flows and analyze their performance in terms of the effective capacity (EC) under retransmission schemes. We extend the derived EC of NC-ARQ for asymmetric channels where users have different data rates

Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting

This work deals with the problem of fast direction-of-arrival (DOA) estimation. A low complexity iterative off-grid method is proposed, which employs the approximate message passing with unitary transformation based sparse Bayesian learning (SBL) to obtain initial estimates of the signals and their corresponding DOAs, and then refines the estimates iteratively using the Jacobi or Gauss-Seidel iteration

In a low angle scenario, the radar performance is significantly degraded because of the multipath. Time reversal is an efficient technique to improve the target detectability by focusing the energy from different paths. For multiple targets and rich multipath scattering case, the number of echoes is large and the distance estimation is complex. In this paper, a distance estimation method of multiple

In this paper, a robust watermarking algorithm in integer wavelet domain using downsampling is proposed. The innovations of this paper can be summarized as follows. First, after downsampling both the host image and watermark, each sub-watermark is embedded into the corresponding sub-host image. Second, four sub-watermarks are encrypted by pixel scrambling and fractional random transform, and the right

In most traditional multi-focus image fusion algorithms, the focus measures used to detect the focused areas of multiple images are unstable and sensitive to noise. Although many modified methods implement more sophisticated strategies to cope with this problem, the complexity of these methods turns out to be a problem for mobile devices. In order to cope with these problems, a novel multi-focus image

Mainlobe jamming will reduce the capabilities of modern radar significantly. Conventional independent component analysis (ICA)-based methods will suffer from the ineffectiveness when the angle of the target is same as that of the jammer. In this paper, exploring polarization characteristics, we propose an approach based on ICA for polarimetric multi-channel radar to suppress mainlobe interference.

Since the introduction of finite rate of innovation (FRI) signals in 2002, various deterministic and stochastic techniques have been proposed to estimate the innovative parameters of FRI signal, e.g. time instants tk and weights ck belonging to a linear combination of finite number of Diracs, from its noisy samples regardless of sampling kernel's type. Having analyzed the Bayesian methods dedicated

The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions. A large amount of current CNN-based Video Super-Resolution methods are designed and trained to handle a specific degradation operator (e.g., bicubic downsampling) and are not robust to mismatch between training and testing degradation

This paper investigates a class of weighted-type fractional Fourier transform (WFRFT), which is mainly used in signal processing and image encryption. To date, studies have primarily focused on the application of WFRFT, and few studies have examined its properties in detail. We propose a new reformulation of WFRFT, whose properties can be readily proven. Multiweighted-type fractional Fourier transform

When training samples contain interference target signals (i.e., outliers), the performance of space–time adaptive processing (STAP) will be degraded. To solve this problem, a cyclic training sample selection and cancellation (CTSSC) algorithm based on sample matrix inversion (SMI) statistic is proposed. This algorithm consists of three steps. Firstly, a cyclic process is carried out to detect outliers

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

In this paper, a novel cross-range scaling method for inverse synthetic aperture radar imaging is proposed to estimate rotation angular velocity of the target by fully integrating the energy of all the target scatterers. Firstly, the phase difference is utilized to integrate the scatterers located at a same range cell into one point. Then, the improved axis rotation technique is adopted to transform

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