Showing papers in "Digital Signal Processing in 2015"

TL;DR: It is shown that the higher concentration of the synchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and SWT do not appear to provide any significant advantages over the original WFT and WT apart from a more visually appealing pictures.

TL;DR: Comparative results indicate that the new ( t, f ) features give better performance as compared to time-only or frequency-only features for the detection of abnormalities in newborn EEG signals.

TL;DR: The results demonstrate clearly that the proposed methodology is immune to noise and capable of estimating the optimal boundaries to isolate the frequencies from noise and estimate the main frequencies with high accuracy, especially the closely-spaced frequencies.

TL;DR: Two recursive least squares parameter estimation algorithms are proposed by using the data filtering technique and the Auxiliary model identification idea to identify the parameters of the system models and the noise models interactively and can generate more accurate parameter estimates than the auxiliary model based recursion least squares algorithms.

TL;DR: An underdamped step-varying second-order SR (USSSR) method is proposed to further improve the output signal-to-noise ratio (SNR) and has three distinct merits as good anti-noises capability in detecting weak signal being submerged in heavy background noise.

TL;DR: Performance of the proposed personal authentication system is found to be better than the various existing systems.

TL;DR: The utility of Bayesian model testing in the domain sciences is demonstrated by presenting four specific practical examples considered within the context of signal processing in the areas of signal detection, sensor characterization, scientific model selection and molecular force characterization.

TL;DR: A novel method of Bayesian gait recognition using Microsoft (MS) Kinect image and depth sensors and skeleton tracking in three-dimensional space and digital signal processing methods and Bayesian probability classification algorithms are used for gait feature analysis to recognize individuals suspected of having Parkinson's disease.

TL;DR: An HVCS construction method with minimum auxiliary black pixels (ABPs) distributed homogeneously, which is realized via embedding secret image into meaningful shares in the halftoned processing of the cover images by error diffusion and obtains admirable visual quality and some preferable advantages compared with related meaningful VC schemes (VCSs).

TL;DR: The results show that the proposed scheme offers a good sensitivity to image content alterations and is robust to the common content-preserving operations, and especially to large angle rotation operations.

TL;DR: This paper provides a review of the recent literature on Bayesian Blind Image Deconvolution methods and focuses on VB inference and the use of SG and SMG models with coverage of recent advances in sampling methods.

TL;DR: This article reviews the basics of AIS as well as provides a comprehensive survey of the state-of-the-art of the topic.

TL;DR: The results of the experiments that were conducted and the comparisons with the previous robust lossless data hiding methods prove the effectiveness of the proposed algorithm, which provides complete reversibility after watermark extraction.

TL;DR: A novel fuzzy clustering algorithm for image segmentation, in which the Mahalanobis distance is utilized to define the dissimilarity measure, and a new regularization term is added to the objective function of the proposed algorithm, reflecting the covariance of the cluster.

TL;DR: The proposed query by saliency content retrieval (QSCR) image retrieval method represents a bottom-up approach considering the human attention, both locally at the level of image regions as well as globally, for the entire image.

TL;DR: A modified S-transform is proposed with several parameters to control the width of a hybrid Gaussian window and a constrained optimization problem is proposed based on an energy concentration measure as objective function and inequalities constraints to define the bounds of the Gaussianwindow.

TL;DR: A fast and efficient algorithm for image inpainting based on the local Allen-Cahn equation that is computationally efficient and unconditionally stable with respect to time step size is presented.

TL;DR: Compared to the state-of-the-art impulse noise removal methods, UWMF provides superior performance, without requiring a fine-tuning for its parameters, in terms of both objective measurements and subjective assessments.

TL;DR: Fast Universal Self-tuned Sampler (FUSS) as mentioned in this paper is a self-tuning MCMC algorithm which produces virtually independent samples from these univariate target densities.

TL;DR: It is shown that multiple measurement vector methods and block sparsity techniques play a fundamental role in improving signal local frequency representations, leading to what is referred to as sparsity-aware quadratic time-frequency distributions.

TL;DR: This work extends the previous analysis using the MPEG CDVS Test Model framework to consider additional detectors and the use of color descriptors to achieve interoperability between different implementations of clients and servers.

TL;DR: In this article, a cell-like P system with the nested structure of three layers is designed to evolve the objects in membranes based on the special membrane structure and communication mechanism of objects.

TL;DR: The technique, exploiting the maximum entropy method (MEM), predicts the entropy of the next future time interval of the time series under investigation by a least square minimization approach to assess the predictability of financial markets.

TL;DR: A novel time-frequency analysis method called OLMD is presented, which can weaken the mode mixing problem in traditional LMD and has better fault identification capacity, which is effective in rotating machinery fault diagnosis.

TL;DR: A data filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein nonlinear controlled autoregressive moving average systems by adopting the key-term separation principle and the data filtering technique.

TL;DR: A new adaptive filtering algorithm and its kernelized version are developed, and a theoretical value of the steady-state excess mean square error (EMSE) is derived, suggesting that the new algorithms with proper choice of the smoothing factor may perform quite well.

TL;DR: The experimental results using raw radar target data and simulated clutter data show that the new detector achieves better detection performance for range-spread maneuvering targets than the existing detectors.

TL;DR: Theoretical analysis and extensive experiments are conducted to validate the correctness, security, and performance of the proposed scheme and it is shown that the client can fulfill the task of selective image encryption securely and save much overhead at the same time.

TL;DR: Experimental results demonstrate that the proposed method outperforms the existing methods for a range of image sizes, meanwhile maintaining low computational cost.

TL;DR: The impacts of frequency increment errors on frequency diverse array (FDA) multiple-input and multiple-output (MIMO) radar in adaptive beamforming and target localization are investigated and the subspace-based localization performance of the FDA MIMO radar is analyzed.