Showing papers in "Signal Processing in 2019"

TL;DR: A four-wing hyperchaotic system is used to provide pseudo-random chaotic sequences, SHA 384 hash function of the plain image and external parameters are combined to compute its initial values, and one-time-pad encryption policy makes the proposed encryption effectively resist plaintext attacks.

TL;DR: The proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis and can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved.

TL;DR: Security and performance analysis indicates that the proposed scheme is highly resistant to various cryptanalytic attacks, is statistically superior and more secure than previously proposed chaos-based image ciphers.

TL;DR: Attention mechanism is introduced to assist the deep network to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge.

TL;DR: Various existing color (RGB mode) image encryption schemes have been examined comprehensively based on the application domains in addition to summarizing over 50 studies in this field, most of which being published in the last year.

TL;DR: Experimental results show that the proposed scheme for color medical image copyright protection based on a chaotic system and quaternion polar harmonic transforms (QPHTs) is robust to geometric attacks and common attacks and that it has better performance than similar lossless watermarking schemes.

TL;DR: This work proposes a RDH scheme based on the images texture to reduce invalid shifting of pixels in histogram shifting and demonstrates that the proposed method has higher capacity and better stego-image quality than some existing RDH schemes.

TL;DR: Rigorous analysis and comparison with some of the existing S-box and image encryption methods reveal that the proposed techniques are capable of generating cryptographically strong S-boxes, PRN with high entropy and optimal resistance against modern image cryptanalysis.

TL;DR: A visually secure encryption scheme is designed by using the parallel compressive sensing counter mode and embedding technique and Logistic-Tent system and 3-D Cat map are introduced to construct the measurement matrices and to disturb the order of the embedded information, respectively.

TL;DR: An enhancement to a tent map’s chaotic properties by hybridizing it with a deterministic finite state machine and proposing a new image encryption scheme based on TM-DFSM, capable of performing both confusion and diffusion operations in one pass while also having a flexible key space.

TL;DR: In this paper, the authors provide an overview of low-rank tensor completion for estimating the missing components of visual data, e.g., color images and videos, and demonstrate the performance comparison when different methods are applied to color image and video processing.

TL;DR: The experimental results and simulation analysis prove that the proposed cryptosystem is capable of reducing data for transmission and has good security performance under various attacks, especially for the shear and noise attacks.

TL;DR: With this extensive review, researchers in image processing will be able to ascertain which of these denoising methods will be best applicable to their research needs and the application domain where such methods are contemplated for implementation.

TL;DR: The proposed adaptive filtering process, called SpcShrink, is able to discriminate wavelet coefficients that significantly represent the signal of interest and shows superior performance when compared with competing algorithms.

TL;DR: Uppermost cryptanalytic properties that are proven by statistical/numeric tests show good performance and reliability of proposed algorithm for image encryption tasks while unlike any other chaotic image encryption system, the algorithm uses a string input for secret key.

TL;DR: The clustering algorithm, i.e. Fuzzy C-means (FCM) clustering, is introduced for the construction of multiple histograms and demonstrates the superior performance of the proposed scheme over other state-of-the-art ones.

TL;DR: A novel transferred deep learning waveform recognition method which makes use of multi-scale convolution and temporal dependency characteristics to improve the recognition performance and significantly outperforms the state-of-the-art methods.

TL;DR: This paper investigates the distributed fusion estimation problem for networked systems whose multisensor measured outputs involve uncertainties modelled by random parameter matrices and finds that local least-squares linear estimators are obtained at the individual local processors, without requiring the signal evolution model.

TL;DR: A conditional generative model for radar high resolution range profile (HRRP) target recognition to learn the discriminative representations and sufficiently encode the observed feature variability by taking the multi-layer perception (MLP) as the sufficient statistics of posterior approximation distribution, thus offering the potential to improve the overall recognition performance.

TL;DR: A Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension.

TL;DR: Experimental results on various test images including benchmark grayscale images and medical ultrasound images demonstrate that the proposed method achieves better performance compared to some state-of-the-art denoising approaches.

TL;DR: A novel decoupled approach of 2-D ANM via semi-definite programming (SDP) is developed, which introduces a new matrix-form atom set to naturally decouple the joint observations in both dimensions without loss of optimality.

TL;DR: Comparisons of SAF-SFT with several typical algorithms with respect to computational cost, detection probability and parameter estimation ability show that the SAF- SFT could strike a balance between computational cost and detection probability as well as the estimation performance.

TL;DR: A dynamic linear event-triggered scheme is proposed to limit the quantity of the transmitted data in the communication channel and a networked fault detection filter is designed as a positive Markov jump system to detect the fault signal.

TL;DR: Experimental results prove the proposed zero-watermarking algorithm can resist common image processing attacks and geometric attacks effectively and thus can be well applied to the copyright protection of three images.

TL;DR: Simulation results verify the superiority of the proposed power allocation algorithm in terms of achieving the effective overall performance of MTT while taking into account the differences targets priorities.

TL;DR: This paper extends the maximum correntropy Kalman filter to a distributed algorithm by introducing a new gain matrix, and analyzes the corresponding mean error and mean square error behavior.

TL;DR: Improved versions of MCUKF are proposed by introducing a new cost function for the filtering update step based on the Gauss-Newton method and the Levenberg-Marguardt method and have better estimation accuracy and numerical stability under non-Gaussian noise compared with existing related filters.

TL;DR: This work considers the underwater tracking of an unknown and time-varying number of targets, i.e., acoustic emitters, using passive array sonar systems, and proposes a complete particle filter track-before-detect (PF-TBD) signal processing procedure for these systems.

TL;DR: An improved version of the popular guided filtering technique, Side Window Guided Filtering (SWGF), which consistently improves the edge preservation performances of GF and its other variations.