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This paper focuses on a low-complexity one-dimensional (1D) direction-of-arrival (DOA) algorithm with an arbitrary cross-linear array. This algorithm is highly accurate without the performance error usually ca...
In this paper, a vision-based patient identification recognition system based on image content analysis and support vector machine is proposed for medical information system, especially in dermatology. This pr...
This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of co...
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate o...
Thanks to massive antenna arrays in millimeter wave communications, a much higher resolution can be achieved for the estimation of angle of arrival. By taking full advantage of beamforming capability and high ...
This paper discusses the fitting of linear state space models to given multivariate time series in the presence of constraints imposed on the four main parameter matrices of these models. Constraints arise par...
This paper develops a data collection and processing framework that achieves individual users’ data privacy and the operator’s information accuracy simultaneously. Data privacy is enhanced by adding noise and ...
Recently, compressed sensing (CS) has aroused much attention for that sparse signals can be retrieved from a small set of linear samples. Algorithms for CS reconstruction can be roughly classified into two cat...
Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary i...
This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stat...
The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi en...
The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties. The Rényi information div...
Neural networks based on the attentional encoder-decoder model have good capability in abstractive text summarization. However, these models are hard to be controlled in the process of generation, which leads ...
Image deconvolution consists in restoring a blurred and noisy image knowing its point spread function (PSF). This inverse problem is ill-posed and needs prior information to obtain a satisfactory solution. Bay...
This paper deals with cell identifier (ID) estimation for narrowband-Internet of things (NB-IoT) system. It is suggested to transform the usual maximum likelihood (ML) estimator expression in order to highligh...
The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In o...
A new variable step-size strategy for the least mean square (LMS) algorithm is presented for distributed estimation in adaptive networks using the diffusion scheme. This approach utilizes the ratio of filtered...
In millimeter wave (mmWave) massive MIMO (multiple-input multiple-output) systems, it is difficult to apply conventional digital precoding techniques due to hardware constraints. Fortunately, the hybrid precod...
A measurement matrix and sensing dictionary are the basic tools for signal compression sampling and reconstruction, respectively, which are important aspects in the field of compression sensing. Previous studi...
We present a novel approach to non-rigid object tracking in this paper by deriving an adaptive data-driven kernel. In contrast with conventional kernel-based trackers which suffer from the constancy of kernel ...
Global navigation satellite systems (GNSS) are being the target of various jamming, spoofing, and meaconing attacks. This paper proposes a new statistical test for the presence of multiple spoofers based on ra...
The brain dynamics in the electroencephalogram (EEG) data are often challenging to interpret, specially when the signal is a combination of desired brain dynamics and noise. Thus, in an EEG signal, anything ot...
Root tracking is a powerful technique that provides insight into the mechanisms of various time-varying processes. The poles and the zeros of a signal-generating system determine the spectral characteristics o...
Continuous miniaturization of circuitry has open the door for various novel application scenarios of millimeter-sized wireless agents such as for the exploration of difficult-to-access fluid environments. In t...
The existence of walls surrounding targets leads to multi-path return at the radar receiver, which provides additional information about the target, and thus can be exploited to strengthen the quality of throu...
An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage...
In this paper, we present a non-convex ℓ2/ℓq(0<q<1)-analysis method to recover a general signal that can be expressed as a block-sparse coefficient vector in a coherent tight frame, and a sufficient condition is ...
In this paper, we propose a target detection and localization method on distributed monopulse arrays for tracking radar. An optimized mainlobe jamming (MLJ) cancellation filter was designed by maximizing the p...
Signal design is an important component for good performance of radar systems. Here, the problem of determining a good radar signal with the objective of minimizing autocorrelation sidelobes is addressed, and ...
This work proposes an analog implementation of gradient-based algorithm for compressive sensing signal reconstruction. Compressive sensing has appeared as a promising technique for efficient acquisition and re...
Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables peopl...
We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted ...
Traditional subspace methods which are based on the spatial time-frequency distribution (STFD) matrix have been investigated for direction-of-arrival (DOA) estimation of linear frequency modulation (LFM) signa...
In this paper, we introduce the q-ratio block constrained minimal singular values (BCMSV) as a new measure of measurement matrix in compressive sensing of block sparse/compressive signals and present an algorithm...
Pursuing better imaging quality and miniaturizing imaging devices are two trends in the current development of ultrasound imaging. While the first one leads to more complex and expensive imaging equipment, poo...
Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsis...
The person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired r...
The complex correntropy is a recently defined similarity measure that extends the advantages of conventional correntropy to complex-valued data. As in the real-valued case, the maximum complex correntropy crit...
The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee ac...
This paper reports the investigation on the micromotion characteristics of chaff dipoles and a chaff cloud. Micromotion models of chaff dipoles and steady-state chaff clouds are discussed. Fourier transform me...
In this paper, online non-negative discriminative dictionary learning for tracking is proposed, which combines the advantages of the global dictionary learning model and the class-specific dictionary learning ...
This paper presents a cost-effective two-dimensional (2-D) inverse discrete cosine transform (IDCT) for supporting multiple standards of MPEG 1/2/4, H.264, VC-1, and HEVC. The proposed approach employs a time ...
A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target reg...
K-singular value decomposition (K-SVD) is a frequently used dictionary learning (DL) algorithm that iteratively works between sparse coding and dictionary updating. The sparse coding process generates sparse c...
This paper proposes a learning policy to improve the energy efficiency (EE) of heterogeneous cellular networks. The combination of active and inactive base stations (BS) that allows for maximizing EE is identi...
An efficient robust fusion estimation (RFE) for distributed fusion system without knowledge of the cross-covariances of sensor estimation errors is suggested. With the hypothesis that the object lying in the i...
We propose a new array configuration composed of multi-scale scalar arrays and a single triangular spatially spread electromagnetic-vector-sensor (SS-EMVS) for high-accuracy two-dimensional (2D) direction-of-a...
Frequency estimation of a tonal signal in passive sonar systems is crucial to the identification of the marine object. In the conventional techniques, a basis mismatch error caused by the discretization of the...
We present sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics. The GLR statistics allow for an unknown ...
In recent years, deep convolutional neural networks (CNNs) have achieved great success in visual tracking. To learn discriminative representations, most of existing methods utilize information of image region ...
Learn more about the impact the EURASIP Journal on Advances in Signal Processing has worldwide
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