Showing papers in "Digital Signal Processing in 2017"

TL;DR: A new expression for the Cramer-Rao bound is derived that is valid for nested arrays, coprime arrays, and MRAs such that the new expressions remain valid for D = O ( N 2 ) , the precise detail depending on the specific array geometry.

TL;DR: In this paper, a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection is proposed for the joint problem of online tracking and detection of the current modality.

TL;DR: The designed three-band filter banks and multi-layer perceptron neural network (MLPNN) are further used together to implement a signal classifier that provides classification accuracy better than the recently reported results for epileptic seizure EEG signal classification.

TL;DR: A novel thresholding algorithm based on 3D Otsu and multi-scale image representation is proposed for medical image segmentation and experimental results indicate that the proposed algorithm is superior to the other multilevel thresholding algorithms consistently.

TL;DR: The analysis indicates that the parameter estimates given by the proposed algorithms converge to their true values under the persistent excitation conditions.

TL;DR: The goal of this paper is to thoroughly review existing techniques for protection and verification of digital image integrity and discuss relevant developments both in active protection and in passive forensic analysis techniques.

TL;DR: This method takes advantage of the waveform in the frequency domain of a signal to eliminate drawbacks of the EWT method in the spectrum segmentation and obtains a perfect segmentation in decomposing noisy and non-stationary signals.

TL;DR: This paper overviews recent advances dealing with time-frequency processing of sparse signals acquired using compressive sensing approaches based on the polynomial Fouriertransform and the short-time Fourier transform.

TL;DR: In the proposed scheme, the DOAs of the uncorrelated targets are first estimated using subspace-based methods, whereas those of the coherent targets are resolved using Bayesian compressive sensing, achieving improved DOA estimation accuracy with a flexible coprime array configuration.

TL;DR: This is the first software with the characteristics described above, and it is considered that it will help other researchers to contribute to the state-of-the-art in pathological speech assessment from different perspectives, e.g., from the clinical point of view for interpretation, and from the computer science point of views enabling the test of different measures and pattern recognition techniques.

TL;DR: The results show that a very good classification rate is achieved at a low SNR of 0 dB, which shows the potential of the deep learning model for the application of modulation classification in AWGN and flat-fading channel.

TL;DR: A new iris segmentation method is developed and tested on UBIRIS and accurately localizes the iris by a model designed on the basis of the Histograms of Oriented Gradients descriptor and Support Vector Machine (SVM), namely HOG-SVM.

TL;DR: The results show that the proposed method allows better identification of signal components compared to standard STFT, multi-resolution STFT and Adaptive Optimal-Kernel Time Frequency Representation, although the method is not computationally efficient in its present form.

TL;DR: It is demonstrated through numerical simulations that the mutual coupling, if unaccounted for, can, in general, lead to performance degradation, with the MRA faring better against mutual coupling than the other two non-uniform structures for both antenna types.

TL;DR: A PARAFAC decomposition-based algorithm is developed for joint direction-of-departure and direction- of-arrival estimation in the presence of unknown mutual coupling for bistatic multiple-input multiple-output radar, which achieves more accurate angle estimation performance than the existing methods.

TL;DR: The measurement differencing approach is adopted to define a new measurement such that the noise in the new measurement is not time-correlated any longer, and the innovation-based and the consensus-based distributed filters are proposed for each sensor by using its neighboring information.

TL;DR: This paper deals with the problem of extracting information from non-stationary signals in the form of features that can be used for effective decision-making in both data analysis and machine learning for automatic classification systems, and forms a TF/TS feature set including two complementary categories: signal related features and image features.

TL;DR: Experimental results demonstrate the effectiveness of proposed work over prior work on compression of encrypted images and obtain the compression performance comparable to state of art work on compressor of unencrypted images i.e. JPEG standard.

TL;DR: This paper constructs a novel coarray named as the difference and sum (diff–sum) coarray by exploiting an improved Conjugate Augmented MUSIC (CAM) estimator, which utilizes both the temporal information and the spatial information.

TL;DR: Numerical experiments and real radar sea clutter data are given to confirm the superiority of the proposed algorithms in terms of the computational complexity and the detection performance.

TL;DR: A new quantizedkernel adaptive filter called quantized kernel maximum correntropy (QKMC) is developed, which is robust to large outliers or impulsive noises, and a sufficient condition for guaranteeing convergence is obtained.

TL;DR: An algorithm which adaptively selects diffusion coefficient using the residual local power and the amount of the gradient magnitude to simultaneously preserve edges, textures, and fine details is proposed.

TL;DR: A method is introduced for automatic determination of the number of sub-histograms and density based histogram division leading to appropriate output with no need for parameter setting and demonstrates not only clearer details along with a boost in contrast, but also noticeably more natural appearance in the images.

TL;DR: A modified anisotropic diffusion model is proposed, which considers a local probability value of gray-level and an adaptive threshold parameter in diffusion coefficient function to adjust the implication of low edge gradient of the feature space from the noisy image.

TL;DR: Simulation results examining the quality of the uniform linear array approximation afforded by this application synthetic aperture processing are shown.

TL;DR: The purpose of this problem is to design a recursive state estimator such that, for all admissible coupling strength uncertainties and linearized errors of nonlinear functions, the estimation error is mean square bounded and the variance of the estimationerror is not more than the prescribed upper bound.

TL;DR: The proposed filter bank outperforms other existing methods for the classification of seizure and seizure-free EEG signals and the sum of the time variance and frequency variance is used to formulate a positive definite matrix to measure the time–frequency joint localization of a bandlimited function from its samples.

TL;DR: The deception jamming suppression issue is formulated as a problem of detecting the presence of target signal while rejecting potential deceptive jamming under the multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) as the transmit array.

TL;DR: The improved performance of MTFDs is demonstrated using several applications including source localization based on direction of arrival (DOA) estimation and automated component separation of non-stationary sources, with focus on blind source separation.

TL;DR: This paper addresses the problem of direction of arrival (DOA) estimation by exploiting the sparsity enforced recovery technique for co-prime arrays, which can increase the degrees of freedom, and develops an iterative approach to gradually amend the offset vector to achieve final DOA estimation.