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

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

Time-bandwidth product (TBP), peak side-lobe level (PSL), integrated side-lobe level (ISL), and relative main-lobe width are considered important performance parameters in pulse compression (PC) radar for the selection of transmitting waveforms. In radar system, linear frequency modulated (LFM) waveforms are popular for transmission. LFM waveforms suffer from the range side-lobes problem which affects

This paper investigates the robust cognitive joint design of transmit waveform and receive filter to improve the system performance of airborne multiple-input multiple-output (MIMO) radar. Considering the target Doppler frequency and spatial cone angle uncertainties are present, we formulate the averaged signal-to-clutter-plus-noise ratio (SCNR) as the optimization goal. Specifically, the averaged

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

During the past decade, the dictionary learning has been a hot topic in sparse representation. With theoretical guarantees, a low-coherence dictionary is demonstrated to optimize the sparsity and improve the accuracy of the performance of signal reconstruction. Two strategies have been investigated to learn incoherent dictionaries: (i) by adding a decorrelation step after the dictionary updating (e

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

In this study, a new method combined with two-dimensional Riesz Transform (RT) in feature extraction stage and Multi-Objective Grey Wolf Optimizer (MOGWO) with k-Nearest Neighbor (KNN) algorithm in the feature selection stage is introduced to classify Power Quality (PQ) disturbances. After determining the most suitable feature group, classification models are created by using machine learning approaches

In this paper, we investigate effect of different Time-Frequency (TF) analysis of micro-Doppler signature for robust and efficient automatic target classification in next-generation radar systems. Indeed, we provide a novel mathematical baseband model for the non-stationary reflected signal of the targets and explore how Short Time Fourier Transform (STFT) and General Linear Chirplet Transform (GLCT)

Active authentication refers to a new mode of identity verification in which biometric indicators are continuously tested to provide real-time or near real-time monitoring of an authorized access to a service or use of a device. This is in contrast to the conventional authentication systems where a single test in form of a verification token such as a password is performed. In active voice authentication

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

Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time-frequency representations. Hence, we overview recent advances dealing with time-frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners

The spectrum of the convolution of two continuous functions can be determined as the continuous Fourier transform of the cross-correlation function. The same can be said about the spectrum of the convolution of two infinite discrete sequences, which can be determined as the discrete time Fourier transform of the cross-correlation function of the two sequences. In current digital signal processing,