Showing papers in "Signal Processing in 2014"

TL;DR: This review aims to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.

TL;DR: Current applications of wavelets in rotary machine fault diagnosis are summarized and some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, newWavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosed are discussed.

TL;DR: Simulations and performance evaluations show that the proposed system is able to produce many 1D chaotic maps with larger chaotic ranges and better chaotic behaviors compared with their seed maps.

TL;DR: A novel reversible data hiding technique in encrypted images where some pixels are estimated before encryption so that additional data can be embedded in the estimating errors and the data extraction and image recovery are free of errors.

TL;DR: Two reversible data hiding methods in encrypted images, namely a joint method and a separable method, are introduced by adopting prediction error, which provides improved reversibility and good visual quality of recovered image for high payload embedding.

TL;DR: This paper reconstructs the interference-plus-noise covariance matrix in a sparse way, instead of searching for an optimal diagonal loading factor for the sample covariance Matrix, to demonstrate that the performance of the proposed adaptive beamformer is almost always equal to the optimal value.

TL;DR: A new scheme, Sparse Extraction of Impulse by Adaptive Dictionary (SpaEIAD), to extract impulse components relies on the sparse model of compressed sensing, involving the sparse dictionary learning and redundant representations over the learned dictionary.

TL;DR: This paper proposes a new framework for image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization, and proposes a split Bregman iteration based technique to solve the non-convex L 0 minimization problem efficiently.

TL;DR: An optimal discrete wavelet transform-singular value decomposition (DWT-SVD) based image watermarking scheme using self-adaptive differential evolution (SDE) algorithm is presented and maintains a satisfactory image quality and watermark can still be identified after various attacks even though the watermarked image is seriously distorted.

TL;DR: Experimental results show that the proposed blind image watermarking scheme has stronger robustness against most common attacks such as image compression, filtering, cropping, noise adding, blurring, scaling and sharpening etc.

TL;DR: A novel image encryption algorithm using a bitplane of a source image as the security key bitplane to encrypt images and a bit-level scrambling algorithm to change bit positions is proposed.

TL;DR: A partly ensemble EMD (PEEMD) method is proposed to resolve the mode mixing problem and can eliminate the residue noise in the IMFs effectively and generates IMFs with better performance, and represents a sound improvement over the original EMD, EEMD and CEEMD.

TL;DR: This paper provides statistical analysis for efficient detection of signal components when missing data samples are present and the determination of the sufficient number of observation and the minimum number of missing samples which still allow proper signal detection.

TL;DR: The numerical simulation shows that the designed synchronization method can effectively synchronize the fractional logistic map and the Caputo-like delta derivative is adopted as the difference operator.

TL;DR: This paper addresses the design of robust weighted fusion Kalman filters for multisensor time-varying systems with uncertainties of noise variances using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule.

TL;DR: This paper considers the state filtering and parameter estimation problems for state space systems with scarce output availability, and a combined parameter estimation and state filtering algorithm is presented for canonical state space models.

TL;DR: A new method, based on natural frequency changes, able to detect damages in beam-like structures and to assess their location and severity, considering the particular manner in which the natural frequencies of the weak-axis bending vibration modes change due to the occurrence of discontinuities is presented.

TL;DR: Mixed H ∞ and passive filter design for Markovian jump impulsive networked control systems with norm bounded uncertainties and random packet dropouts and mode-dependent conditions are established to guarantee the filtering error system to be robustly stochastically stable and achieve a prescribed performance index.

TL;DR: The proposed adaptive spectral kurtosis filtering technique is applied in the extraction of the signal transients that shows the gear fault, which proves the effectiveness of the proposed technique in extracting the signaltransients in the practical application.

TL;DR: It is possible to define a general threshold that separates signal components from spectral noise, in the cases when some components are masked by noise, and this threshold can be iteratively updated, providing an iterative version of blind and simple compressive sensing reconstruction algorithm.

TL;DR: The experimental results verify the significant efficiency improvement of the proposed method in output quality and energy consumption, when compared with other fusion techniques in DCT domain.

TL;DR: Results of analyzing synthetic signal, incipient rotor imbalance fault of Bently test-rig and weak electrocardiogram (ECG) signal show that the improved HHT combined with wavelet analysis have excellent weak signal detecting performance whilst achieving robustness against low signal-to-noise ratio (SNR).

TL;DR: Sufficient criteria on stochastic finite-time H ∞ stabilization via observer-based fuzzy state feedback are presented for the solvability of the problem, which can be tackled by a feasibility problem in terms of linear matrix inequalities.

TL;DR: A novel region level based multifocus image fusion method that can locate the boundary of the focus region accurately and exploit the spatial frequency and structural similarity index to improve the visual quality of the fusion result.

TL;DR: This paper presents a two-stage gradient based and a least squares based iterative estimation algorithms for controlled autoregressive ARMA systems that requires less computation compared with the least squaresbased iterative algorithm.

TL;DR: This paper focuses on a special class of PFSA, which captures finite history of the symbol strings, called D-Markov machines, which have a simple algebraic structure and are computationally efficient to construct and implement.

TL;DR: This work develops an algorithm, called 'overlapping group shrinkage' (OGS), based on the minimization of a convex cost function involving a group-sparsity promoting penalty function, that produces denoised speech that is relatively free of musical noise.

TL;DR: Simulation results show that the proposed algorithms outperform existing methods and provide root mean-square error performance very close to the Cramer–Rao lower bound.

TL;DR: The transforms constructed are then used as the basis of a novel image encryption scheme, and security aspects of such a scheme are analyzed through computer simulations and specific metrics.

TL;DR: Experimental results show the effectiveness and validity of noise handling in human action and UCI datasets, and ELS-TSVM has also obtained superior accuracy compared with the related methods while its time complexity is remarkably lower than SVM.