EURASIP Journal on Advances in Signal Processing

http://link.springer.com/journal/13634

List of Papers (Total 3,501)

A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics

In this paper, we propose a novel machine-learning method for anomaly detection applicable to data with periodic characteristics where randomly varying period lengths are explicitly allowed. A multi-dimensional time series analysis is conducted by training a data-adapted classifier consisting of deep convolutional neural networks performing phase classification. The entire...

An algebraic method for moving source localization using TDOA, FDOA, and differential Doppler rate measurements with receiver location errors

To weaken the effect of receiver location error on localization accuracy and make the localization model closer to the practical scenario, this paper considers the receiver location errors, usually neglected in prior studies into the measurement model, and proposes an algebraic method for locating a moving source using time difference of arrival (TDOA), frequency difference of...

Multi-resolution auditory cepstral coefficient and adaptive mask for speech enhancement with deep neural network

The performance of the existing speech enhancement algorithms is not ideal in low signal-to-noise ratio (SNR) non-stationary noise environments. In order to resolve this problem, a novel speech enhancement algorithm based on multi-feature and adaptive mask with deep learning is presented in this paper. First, we construct a new feature called multi-resolution auditory cepstral...

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is...

Signal-to-noise ratio estimation for M-QAM signals in η−μand κ−μfading channels

In this paper, signal-to-noise ratio (SNR) estimation is carried out by the method of moments (MOM) for fading channels modeled by probability distributions η−μ and κ−μ, considering M-ary quadrature amplitude modulation (M-QAM) with constellation energy normalized to one. New expressions are presented for the SNR estimation and for the mean, variance, and normalized mean square...

Optimized implementation of digital signal processing applications with gapless data acquisition

This paper presents novel models and design optimization methods for gapless deep waveform applications, where continuous streams of data must be processed reliably without dropping any samples. The approaches developed in this paper involve unified dataflow-based modeling of the interfaces and signal processing functionality of gapless deep waveform analysis. Bottleneck actors...

Joint channel and phase noise estimation for mmWave full-duplex communication systems

Full-duplex (FD) communication at millimeter-wave (mmWave) frequencies suffers from a strong self-interference (SI) signal, which can only be partially canceled using conventional RF cancelation techniques. This is because current digital SI cancellation techniques, designed for microwave frequencies, ignore the rapid phase noise (PN) variation at mmWave frequencies, which can...

Unsupervised joint deconvolution and segmentation method for textured images: a Bayesian approach and an advanced sampling algorithm

The paper tackles the problem of joint deconvolution and segmentation of textured images. The images are composed of regions containing a patch of texture that belongs to a set of K possible classes. Each class is described by a Gaussian random field with parametric power spectral density whose parameters are unknown. The class labels are modelled by a Potts field driven by a...

Support system for classification of beat-to-beat arrhythmia based on variability and morphology of electrocardiogram

BackgroundSeveral authors use the R-R interval, which is the temporal difference between the largest waves (R waves) of the electrocardiogram (ECG), to propose a support system for the diagnosis of arrhythmias. However, R-R interval analysis does not measure ECG waveform deformations such as P wave deformations for atrial fibrillation.ObjectiveIn this study, we propose an...

A bottom-up summarization algorithm for videos in the wild

Video summarization aims to provide a compact video representation while preserving the essential activities of the original video. Most existing video summarization approaches relay on identifying important frames and optimizing target energy by a global optimum solution. But global optimum may fail to express continuous action or realistically validate how human beings perceive...

A multisource fusion framework driven by user-defined knowledge for egocentric activity recognition

Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its widespread applicability to human systems, including the evaluation of dietary and physical activity and the monitoring of patients and older adults. In this paper, we present a knowledge-driven multisource fusion...

Recurrently exploiting co-saliency of target for part-based visual tracking

Visual tracking in condition of occlusion has been a challenging task over years. Recently, part-based algorithms have made great progress in handling occlusion. However, the existing part-based methods neglect different importance between central parts and marginal parts. Besides, scale variation remains a difficulty for part-based tracking. In this paper, we propose a novel...

Reverberation suppression using non-negative matrix factorization to detect low-Doppler target with continuous wave active sonar

In active sonar systems, the detection of echo from targets can deteriorate due to reverberation. Detection becomes more difficult if targets have low-Doppler frequency and are located near the reverberation band, especially in an environment with low signal-to-reverberation ratio. In this paper, we propose an algorithm for the reverberation suppression of continuous wave signals...

Spectral information of EEG signals with respect to epilepsy classification

BackgroundThe spectral information of the EEG signal with respect to epilepsy is examined in this study.MethodIn order to assess the impact of the alternative definitions of the frequency sub-bands that are analysed, a number of spectral thresholds are defined and the respective frequency sub-band combinations are generated. For each of these frequency sub-band combination, the...

A multiple model high-resolution head-related impulse response database for aided and unaided ears

Head-related impulse responses (HRIRs) allow for the creation of virtual acoustic scenes. Since in ideal conditions the human auditory system can localize sounds with a very high degree of accuracy, it is useful to have an HRIR database with high spatial resolution, such that realistic-sounding scenes can be created. In this article, we present an HRIR database with 12722...

Two variants of the IIR spline adaptive filter for combating impulsive noise

It has been pointed out that the nonlinear spline adaptive filter (SAF) is appealing for modeling nonlinear systems with good performance and low computational burden. This paper proposes a normalized least M-estimate adaptive filtering algorithm based on infinite impulse respomse (IIR) spline adaptive filter (IIR-SAF-NLMM). By using a robust M-estimator as the cost function, the...

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Segmentation and denoising of signals often rely on the polynomial model which assumes that every segment is a polynomial of a certain degree and that the segments are modeled independently of each other. Segment borders (breakpoints) correspond to positions in the signal where the model changes its polynomial representation. Several signal denoising methods successfully combine...

Convolutional neural networks for radar HRRP target recognition and rejection

Robust and efficient feature extraction is critical for high-resolution range profile (HRRP)-based radar automatic target recognition (RATR). In order to explore the correlation between range cells and extract the structured discriminative features in HRRP, in this paper, we take advantage of the attractive properties of convolutional neural networks (CNNs) to address HRRP RATR...

Energy-efficient multi-cell resource allocation in cognitive radio-enabled 5G systems

In this paper, we propose an energy-efficient resource allocation (RA) algorithm in cognitive radio-enabled 5th generation (5G) systems, where the scenario including one primary system and multiple secondary cells is considered. Because of the high spectrum leakage of traditional orthogonal frequency division multiplexing (OFDM), alternative modulation schemes regarded as the...

Distributed stochastic gradient descent for link prediction in signed social networks

This paper considers the link prediction problem defined over a signed social network, where the relationship between any two network users can be either positive (friends) or negative (foes). Given a portion of the relationships, the goal of link prediction is to identify the rest unknown ones. This task resorts to completing the adjacency matrix of the signed social network...

Wideband signal detection for cognitive radio applications with limited resources

Wideband signals are expected to be used to achieve the required quality of service (QoS) in the next generation of wireless communications, civil and military radar, and many wireless sensor network (WSN) scenarios. Wideband signal detection has been identified as one of the most challenging problems in the proliferation of the cognitive radio technology. Moreover in many...

FROST—Fast row-stochastic optimization with uncoordinated step-sizes

In this paper, we discuss distributed optimization over directed graphs, where doubly stochastic weights cannot be constructed. Most of the existing algorithms overcome this issue by applying push-sum consensus, which utilizes column-stochastic weights. The formulation of column-stochastic weights requires each agent to know (at least) its out-degree, which may be impractical in...

Direction finding of bistatic MIMO radar based on quantum-inspired grey wolf optimization in the impulse noise

A novel direction-finding method is proposed for bistatic multiple-input-multiple-output (MIMO) radar in the impulse noise in this paper. The method has the capacity to suppress the impulse noise by means of infinite norm normalization and can obtain better performance for direction finding via the weighted signal subspace fitting algorithm. To solve the objective function of...

Hybrid ADMM: a unifying and fast approach to decentralized optimization

The present work introduces the hybrid consensus alternating direction method of multipliers (H-CADMM), a novel framework for optimization over networks which unifies existing distributed optimization approaches, including the centralized and the decentralized consensus ADMM. H-CADMM provides a flexible tool that leverages the underlying graph topology in order to achieve a...