Showing papers in "Signal Processing in 2016"

Abstract: In this paper, a novel image encryption approach based on permutation-substitution (SP) network and chaotic systems is proposed. It consists of four cryptographic phases: diffusion, substitution, diffusion and permutation. Firstly, a diffusion phase is proposed based on new chaotic map. Then, a substitution phase based on strong S-boxes followed by a diffusion phase based on chaotic logistic map are presented which will, in turn, significantly increase the encryption performance. Finally, a block permutation phase is accomplished by a permutation function to enhance the statistical performance of the proposed encryption approach. Conducted experiments based on various types of differential and statistical analyses show that the proposed encryption approach has high security, sensitivity and speed compared to previous approaches A novel image encryption approach based on SP network and chaos is proposed.Qualitative and quantitative analysis verify the effectiveness of the proposed encryption scheme.The encryption scheme shows superior performance than previous schemes.

Abstract: Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images As SR has been developed for more than three decades, both multi-frame and single-frame SR have significant applications in our daily life This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years Regularized SR methods are most commonly employed in the last decade Technical details are discussed in this article, including reconstruction models, parameter selection methods, optimization algorithms and acceleration strategies Moreover, an exhaustive summary of the current applications using SR techniques has been presented Lastly, the article discusses the current obstacles for future research

Abstract: We propose a straightforward and efficient fusion-based method for enhancing weakly illumination images that uses several mature image processing techniques. First, we employ an illumination estimating algorithm based on morphological closing to decompose an observed image into a reflectance image and an illumination image. We then derive two inputs that represent luminance-improved and contrast-enhanced versions of the first decomposed illumination using the sigmoid function and adaptive histogram equalization. Designing two weights based on these inputs, we produce an adjusted illumination by fusing the derived inputs with the corresponding weights in a multi-scale fashion. Through a proper weighting and fusion strategy, we blend the advantages of different techniques to produce the adjusted illumination. The final enhanced image is obtained by compensating the adjusted illumination back to the reflectance. Through this synthesis, the enhanced image represents a trade-off among detail enhancement, local contrast improvement and preserving the natural feel of the image. In the proposed fusion-based framework, images under different weak illumination conditions such as backlighting, non-uniform illumination and nighttime can be enhanced. HighlightsA fusion-based method for enhancing various weakly illuminated images is proposed.The proposed method requires only one input to obtain the enhanced image.Different mature image processing techniques can be blended in our framework.Our method has an efficient computation time for practical applications.

Abstract: The sine signal is used widely in the signal processing, communication, system analysis and system identification. This paper proposes a damping parameter estimation algorithm for dynamical systems based on the sine frequency response. The measured data are collected by taking the sine signals as the input. Analyzing the system's output sine response function, we can construct a nonlinear objective function. By using the nonlinear optimization techniques, we propose an iterative algorithm to estimate the system parameters. In order to overcome the singular or ill-conditioned matrix during the iterative process, we introduce a damping factor in the proposed iterative algorithm. At the same time the gradient iterative parameter estimation algorithm and the Gauss-Newton iterative parameter estimation algorithm are derived for comparing the performance of the presented methods. Moreover, the simulation results given by an example indicate that the proposed method works well.

Abstract: A novel signal denoising method that combines variational mode decomposition (VMD) and detrended fluctuation analysis (DFA), named DFA-VMD, is proposed in this paper. VMD is a recently introduced technique for adaptive signal decomposition, which is theoretically well founded and more robust to sampling and noise compared with empirical mode decomposition (EMD). The noisy signal is first broken down into a given number K band-limited intrinsic mode functions (BLIMFs) by VMD. Then a simple criterion based on DFA is designed to select the number K, aiming to avoid the impact of overbinning or underbinning on the VMD denoising. In addition, DFA is also developed to define the relevant modes to construct the filtered signal. After that, the computational complexity of DFA-VMD denoising is analyzed, and its time complexity is equivalent to the EMD. Experimental results, on simulated and real signals, show the superior performance of this proposed filtering over EMD-based denoisings and discrete wavelet threshold filtering. DFA-VMD is first proposed to remove the white Gaussian noise.The number of the band-limited modes K is analyzed based on DFA.The relevant modes for partial construction are selected using DFA.The order of time complexity of DFA-VMD is equal to that of EMD.

Abstract: Breast cancer is the leading type of malignant tumor observed in women and the effective treatment depends on its early diagnosis. Diagnosis from histopathological images remains the "gold standard" for breast cancer. The complexity of breast cell histopathology (BCH) images makes reliable segmentation and classification hard. In this paper, an automatic quantitative image analysis technique of BCH images is proposed. For the nuclei segmentation, top-bottom hat transform is applied to enhance image quality. Wavelet decomposition and multi-scale region-growing (WDMR) are combined to obtain regions of interest (ROIs) thereby realizing precise location. A double-strategy splitting model (DSSM) containing adaptive mathematical morphology and Curvature Scale Space (CSS) corner detection method is applied to split overlapped cells for better accuracy and robustness. For the classification of cell nuclei, 4 shape-based features and 138 textural features based on color spaces are extracted. Optimal feature set is obtained by support vector machine (SVM) with chain-like agent genetic algorithm (CAGA). The proposed method was tested on 68 BCH images containing more than 3600 cells. Experimental results show that the mean segmentation sensitivity was 91.53% (±4.05%) and specificity was 91.64% (±4.07%). The classification performance of normal and malignant cell images can achieve 96.19% (±0.31%) for accuracy, 99.05% (±0.27%) for sensitivity and 93.33% (±0.81%) for specificity.

Abstract: Lately, a number of image encryption algorithms that are either based on pixel level or bit level encryption have been proposed. However, not only pixel level permutation, but also bit level permutation has its intrinsic drawbacks. This paper proposes a new cryptosystem to address these drawbacks. Different kinds of permutation algorithms are first comprehensively analyzed and compared. Because, from a bit level perspective, an image can be considered as a natural three-dimensional (3D) bit matrix (width, height, and bit length), a new 3D bit matrix permutation is proposed, in which the Chen system is used to develop a random visiting mechanism to the bit level of the plain-image. By combining aspects of the Chen system with a 3D Cat map in the permutation stage, a new mapping rule is developed to map one random position to another random position (that is, double random position permutation) in the 3D matrix rather than using traditional sequential visiting to the plain-image. Simulations are carried out and the results confirm the security and efficiency of our new cryptosystem. The problems of pixel- or bit-level permutation are analyzed.A 3D bit level permutation is proposed.A random visiting to plain-image is proposed.

Abstract: In year 2000, an efficient hierarchical chaotic image encryption (HCIE) algorithm was proposed, which divides a plain-image of size M × N with T possible value levels into K blocks of the same size and then operates position permutation on two levels: intra-block and inter-block. As a typical position permutation-only encryption algorithm, it has received intensive attention. The present paper analyzes specific security performance of HCIE against ciphertext-only attack and known/chosen-plaintext attack. It is found that only O ( ? log T ( M ? N / K ) ? ) known/chosen plain-images are sufficient to achieve a good performance, and the computational complexity is O ( M ? N ? ? log T ( M ? N / K ) ? ) , which effectively demonstrates that hierarchical permutation-only image encryption algorithms are less secure than normal (i.e., non-hierarchical) ones. Detailed experiment results are given to verify the feasibility of the known-plaintext attack. In addition, it is pointed out that the security of HCIE against ciphertext-only attack was much overestimated. HighlightsSecurity performance of an encryption algorithm called HCIE is analyzed in detail.Hierarchical permutation-only encryption schemes are less secure than normal ones.Security of HCIE against ciphertext-only attack was reported being overestimated.

Abstract: A concise survey of the literature on cyclostationarity of the last 10 years is presented and an extensive bibliography included. The problems of statistical function estimation, signal detection, and cycle frequency estimation are reviewed. Applications in communications are addressed. In particular, spectrum sensing and signal classification for cognitive radio, source location, MMSE filtering, and compressive sensing are discussed. Limits to the applicability of the cyclostationary signal processing and generalizations of cyclostationarity to overcome these limits are addressed in the companion paper “Cyclostationarity: Limits and generalizations”.

Abstract: A type of norm-adaption penalized least mean square/fourth (NA-LMS/F) algorithm is proposed for sparse channel estimation applications. The proposed NA-LMS/F algorithm is realized by incorporating a p-norm-like into the cost function of the conventional least mean square/fourth (LMS/F) which acts as a combination of the l0- and l1-norm constraints. A reweighted NA-LMS/F (RNA-LMS/F) algorithm is also developed by adding a reweighted factor in the NA-LMS/F algorithm. The proposed RNA-LMS/F algorithm exhibits an improved performance in terms of the convergence speed and the steady-state error, which can provide a zero attractor to further exploit the sparsity of the channel by the use of the norm adaption penalty and the reweighting factor. The simulation results obtained from the sparse channel estimations are given to verify that our proposed RNA-LMS/F algorithm is superior to the previously reported sparse-aware LMS/F and the conventional LMS/F algorithms in terms of both the convergence speed and the steady-state behavior. HighlightsNorm-adaption penalized LMS/F (NA-LMS/F) algorithm is proposed for channel estimation.Reweighting NA-LMS/F (RNA-LMS/F) algorithm is proposed and analyzed in detail.Simulations verify the performance of the proposed NA-LMS/F and RNA-LMS/F algorithms.RNA-LMS/F algorithm has fastest convergence and best channel estimation performance.

Abstract: With the increasing use of media in communications, there is a need for image encryption for security against attacks. In this paper, we have proposed a new algorithm for image security using Elliptic Curve Cryptography (ECC) diversified with DNA encoding. The algorithm first encodes the RGB image using DNA encoding followed by asymmetric encryption based on Elliptic Curve Diffie-Hellman Encryption (ECDHE). The proposed algorithm is applied on standard test images for analysis. The analysis is performed on key spaces, key sensitivity, and statistical analysis. The results of the analysis conclude that the proposed algorithm can resist exhaustive attacks and is apt for practical applications. HighlightsA robust image encryption algorithm is proposed based on DNA and ECDHE.No need for biological DNA expriments.The constructed key space is designed to provide the users' with high level of security.

Abstract: The variational mode decomposition (VMD) was proposed recently as an alternative to the empirical mode decomposition (EMD). To shed further light on its performance, we analyze the behavior of VMD in the presence of irregular samples, impulsive response, fractional Gaussian noise as well as tones separation. Extensive numerical simulations are conducted to investigate the parameters mentioned in VMD on these filter bank properties. It is found that, unlike EMD, the statistical characterization of the obtained modes reveals a different equivalent filter bank structure, robustness with respect to the nonuniformly sampling and good resolution in spectrum analysis. Moreover, we illustrate the influences of the main parameters on these properties, which provides a guidance on tuning them. Based on these findings, three potential applications in extracting time-varying oscillations, detrending as well as detecting impacts using VMD are presented. HighlightsAn in-depth elaboration on the inherent characteristics of VMD is investigated through extensive numerical experiments.A well-controlled use of the VMD technique for specific application is addressed.Three potential applications of VMD are illustrated.

Abstract: Digital audio watermarking is an important technique to secure and authenticate audio media. This paper provides a comprehensive review of the twenty years' research and development works for digital audio watermarking, based on an exhaustive literature survey and careful selections of representative solutions. We generally classify the existing designs into time domain and transform domain methods, and relate all the reviewed works using two generic watermark embedding equations in the two domains. The most important designing criteria, i.e., imperceptibility and robustness, are thoroughly reviewed. For imperceptibility, the existing measurement and control approaches are classified into heuristic and analytical types, followed by intensive analysis and discussions. Then, we investigate the robustness of the existing solutions against a wide range of critical attacks categorized into basic, desynchronization, and replacement attacks, respectively. This reveals current challenges in developing a global solution robust against all the attacks considered in this paper. Some remaining problems as well as research potentials for better system designs are also discussed. In addition, audio watermarking applications in terms of US patents and commercialized solutions are reviewed. This paper serves as a comprehensive tutorial for interested readers to gain a historical, technical, and also commercial view of digital audio watermarking. HighlightsThe paper systematically categorizes all important existing audio watermark embedding schemes in a concise and effective way based on two generic embedding functions, followed by extensive discussions and analysis.The measurement and control approaches to ensure the imperceptibility property of existing audio watermarking systems are exhausted and categorized into heuristic and analytical groups, with detailed analysis and comparison.Existing attacks to audio watermarking systems are comprehensively studied and rigorously evaluated against a series of representative audio watermarking systems.Current open challenges and future research potentials are sufficiently addressed in this paper.Audio watermarking applications in terms of US patents and commercial products are extensively reviewed.

Abstract: In signal processing applications, it is often necessary to extract oscillatory components and their properties from time-frequency representations, e.g. the windowed Fourier transform or wavelet transform. The first step in this procedure is to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest and providing a measure of its instantaneous frequency. This is not a trivial issue, and the optimal method for extraction is still not settled or agreed. We discuss and develop procedures that can be used for this task and compare their performance on both simulated and real data. In particular, we propose a method which, in contrast to many other approaches, is highly adaptive so that it does not need any parameter adjustment for the signal to be analyzed. Being based on dynamic path optimization and fixed point iteration, the method is very fast, and its superior accuracy is also demonstrated. In addition, we investigate the advantages and drawbacks that synchrosqueezing offers in relation to curve extraction. The codes used in this work are freely available for download. HighlightsIdentifying and following instantaneous (ridge) frequencies in complex signals.Adaptive, universal, extraction of ridge frequencies from time-frequency representations.Comparison of the performance of different approaches for ridge curve extraction.Relative performance of the synchrosqueezed transforms in terms of curve extraction.Freely available codes implementing the new method.

Abstract: The sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter suffers from low computational efficiency since a large number of particles are often required, especially when there are a large number of targets and dense clutter. In order to speed up the computation, an algorithmic framework for parallel SMC-PHD filtering based on multiple processors is proposed. The algorithm makes full parallelization of all four steps of the SMC-PHD filter and the computational load is approximately equal among parallel processors, rendering a high parallelization benefit when there are multiple targets and dense clutter. The parallelization is theoretically unbiased as it provides the same result as the serial implementation, without introducing any approximation. Experiments on multi-core computers have demonstrated that our parallel implementation has gained considerable speedup compared to the serial implementation of the same algorithm. A fully and unbiasedly parallel implementation framework of the SMC-PHD filtering is proposed based on the centralized distributed system that consists of one central unit (CU) and several independent processing elements (PEs). Display Omitted An algorithmic framework for parallel SMC-PHD filtering is proposed.All the main calculations of the filter are unbiasedly paralleled.The parallelization obtains theoretically the same result as the serial implementation.Considerable speed-up is gained.

Abstract: This paper proposes a new method to reduce cross-terms in the Wigner-Ville distribution (WVD) using tunable-Q wavelet transform (TQWT). The suggested method exploits the advantages of sub-band filtering of filter-bank and also retaining the time-resolution property of the wavelet decomposition to achieve signal decomposition. Signal components in sub-bands obtained using TQWT are further separated in time-domain using time-domain energy distribution to eliminate inner-interference terms. Simulation results for multi-component non-stationary signals are presented in order to show the efficacy of the suggested method for cross-terms reduction in WVD. Results are compared with the existing methods based on the Fourier-Bessel (FB) series expansion and filter-bank based cross-terms reduction methods in WVD, in order to show the advantages over the compared methods. HighlightsThis paper presents a new method based on TQWT for cross terms reduction in WVD.The proposed method has been studied on multi-component non-stationary signals.The simulation results have been compared with the other existing methods.The proposed method works well even in the presence of noise.

Abstract: With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global image descriptors for image instance retrieval. While the good performance of CNNs for image classification are unambiguously recognised, which of the two has the upper hand in the image retrieval context is not entirely clear yet.We propose a comprehensive study that systematically evaluates FVs and CNNs for image instance retrieval. The first part compares the performances of FVs and CNNs on multiple publicly available data sets and for multiple criteria. We show that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together. The second part of the study focuses on the impact of geometrical transformations. We show that performance of CNNs can quickly degrade in the presence of certain transformations and propose a number of ways to incorporate the required invariances in the CNN pipeline.Our findings are organised as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem. HighlightsCNNs exhibit very limited invariance to rotation changes compared to FVDoG.CNNs are more robust to scale changes than any variants of FV.Max-pooling across rotated/scaled database images gains rotation/scale invariance.Combining FV with CNN can improve retrieval accuracy by a significant margin.

Abstract: Digital image sometimes needs to be stored and processed in an encrypted format to maintain security and privacy, e.g., cloud storage and cloud computing. For the purpose of content notation and/or tampering detection, the cloud servers need to embed some additional information directly in these encrypted images. As an emerging technology, reversible data hiding in the encrypted domain will be useful in cloud computing due to its ability to preserve the confidentiality. In this paper, a novel separable and error-free reversible data hiding scheme in encrypted images is proposed. After analyzing the property of interpolation technology, a stream cipher is utilized to encrypt sample pixels and a specific encryption mode is designed to encrypt interpolation-error of non-sample pixels. Then, the data-hider, who does not know the original image content, may reversibly embed secret data into interpolation-error using a modified version of histogram shifting and difference expansion technique. In order to adapt to different application scenarios, data extraction can be done either in the encrypted domain or in the decrypted domain. In addition, real reversibility is realized, that is, data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme. Embedding is done on the encrypted image without knowing the decryption key.Two different encryption schemes are utilized to enhance the security.More flexible embedding mechanism is adopted for different capacity requirements.No errors occur in data extraction and image recovery.The hidden data can be extracted from the encrypted and/or decrypted image.

Abstract: In this paper, a robust watermarking scheme based on orthogonal Fourier-Mellin moments and chaotic map is introduced, which achieves copyright authentication for double images simultaneously. The proposed scheme consists of an ownership registration phase and a verification phase. Compared to the congeneric schemes, the new scheme achieves a high level security and saves storage space. Firstly, two images are combined into a single-channel architecture, then the feature invariants derived from orthogonal Fourier-Mellin moments are computed and utilized to construct a binary feature image. Together with the watermark, they are scrambled by chaotic map and subsequently used for generating the verification image. Experimental results demonstrate the validity and security of the proposed scheme as well as its robustness against different attacks. A novel robust watermarking scheme for double images is reported.The introduced scheme employed moment invariants and chaotic map is more robust.Moment order and initial values of chaotic map enhance the scheme's security greatly.

Abstract: The objective quality assessment of stereoscopic images plays an important role in three-dimensional (3D) technologies. In this paper, we propose an effective method to evaluate the quality of stereoscopic images that are afflicted by symmetric distortions. The major technical contribution of this paper is that the binocular combination behaviors and human 3D visual saliency characteristics are both considered. In particular, a new 3D saliency map is developed, which not only greatly reduces the computational complexity by avoiding calculation of the depth information, but also assigns appropriate weights to the image contents. Experimental results indicate that the proposed metric not only significantly outperforms conventional 2D quality metrics, but also achieves higher performance than the existing 3D quality assessment models. HighlightsAn effective 3D visual saliency model is proposed.A full-reference SIQA model is built based on human binocular characteristics.It has less computing complexity and higher consistency with the subjective values.It can precisely assess the quality of images with different types of distortions.

Abstract: This paper proposes the adaptation of well-known strategies successfully used in speech processing: Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual Linear Prediction (PLP) coefficients. Additionally characteristics like RASTA filtering or delta coefficients are also considered and evaluated for inertial signal processing. These adaptations have been incorporated into a Human Activity Recognition and Segmentation (HARS) system based on Hidden Markov Models (HMMs) for recognizing and segmenting six different physical activities: walking, walking-upstairs, walking-downstairs, sitting, standing and lying.All experiments have been done using a publicly available dataset named UCI Human Activity Recognition Using Smartphones, which includes several sessions with physical activity sequences from 30 volunteers. This dataset has been randomly divided into six subsets for performing a six-fold cross validation procedure. For every experiment, average values from the six-fold cross-validation procedure are shown.The results presented in this paper overcome significantly baseline error rates, constituting a relevant contribution in the field. Adapted MFCC and PLP coefficients improve human activity recognition and segmentation accuracies while reducing feature vector size considerably. RASTA-filtering and delta coefficients contribute significantly to reduce the segmentation error rate obtaining the best results: an Activity Segmentation Error Rate lower than 0.5%. Human activity segmentation using Hidden Markov Models.Frequency-based feature extraction from Inertial Signals.RASTA filtering analysis and delta coefficients.Important dimensionality reduction.

Abstract: In this paper we propose a multimodal feature learning mechanism based on deep networks (i.e., stacked contractive autoencoders) for video classification. Considering the three modalities in video, i.e., image, audio and text, we first build one Stacked Contractive Autoencoder (SCAE) for each single modality, whose outputs will be joint together and fed into another Multimodal Stacked Contractive Autoencoder (MSCAE). The first stage preserves intra-modality semantic relations and the second stage discovers inter-modality semantic correlations. Experiments on real world dataset demonstrate that the proposed approach achieves better performance compared with the state-of-the-art methods. HighlightsA two-stage framework for multimodal video classification is proposed.The model is built based on stacked contractive autoencoders.The first stage is single modal pre-training.The second stage is multimodal fine-tuning.The objective functions are optimized by stochastic gradient descent.

Abstract: This paper investigates the source localization problem based on time difference of arrival (TDOA) measurements in the presence of random noises in both the TDOA and sensor location measurements. We formulate the localization problem as a constrained weighted least squares problem which is an indefinite quadratically constrained quadratic programming problem. Owing to the non-convex nature of this problem, it is difficult to obtain a global solution. However, by exploiting the hidden convexity of this problem, we reformulate it to a convex optimization problem. We further derive a primal-dual interior point algorithm to reach a global solution efficiently. The proposed method is shown to analytically achieve the Cramer-Rao lower bound (CRLB) under some mild approximations. Moreover, when the location geometry is not desirable, the proposed algorithm can efficiently avoid the ill-conditioning problem. Simulations are used to corroborate the theoretical results which demonstrate the good performance, robustness and high efficiency of the proposed method. HighlightsWe explore the source localization problem using noisy TDOA measurements in the presence of random sensor position errors.By exploiting the hidden convexity, the formulated non-convex localization problem is transformed to a convex optimization problem.The proposed convex localization algorithm analytically achieves the CRLB under some mild approximations.The proposed algorithm can efficiently avoid the ill-conditioning problem.

Abstract: Face images captured by surveillance cameras usually have low-resolution (LR) in addition to uncontrolled poses and illumination conditions, all of which adversely affect the performance of face matching algorithms. In this paper, we develop a novel method to address the problem of matching a LR or poor quality face image to a gallery of high-resolution (HR) face images. In recent years, extensive efforts have been made on LR face recognition (FR) research. Previous research has focused on introducing a learning based super-resolution (LBSR) method before matching or transforming LR and HR faces into a unified feature space (UFS) for matching. To identify LR faces, we present a method called coupled discriminant multi-manifold analysis (CDMMA). In CDMMA, we first explore the neighborhood information as well as local geometric structure of the multi-manifold space spanned by the samples. And then, we explicitly learn two mappings to project LR and HR faces to a unified discriminative feature space (UDFS) through a supervised manner, where the discriminative information is maximized for classification. After that, the conventional classification method is applied in the CDMMA for final identification. Experimental results conducted on two standard face recognition databases demonstrate the superiority of the proposed CDMMA.

Abstract: The normalized least-mean-square (NLMS) adaptive filter is widely used in system identification. In this paper, we develop an optimized NLMS algorithm, in the context of a state variable model. The proposed algorithm follows a joint-optimization problem on both the normalized step-size and regularization parameters, in order to minimize the system misalignment. Consequently, it achieves a proper compromise between the performance criteria, i.e., fast convergence/tracking and low misadjustment. Simulations performed in the context of acoustic echo cancellation indicate the good features of the proposed algorithm. HighlightsAn optimized normalized least-mean-square (NLMS) algorithm is developed for system identification, in the context of a state variable model.The proposed algorithm is based on a joint-optimization on both the normalized step-size and regularization parameters, in order to minimize the system misalignment.The performance of the proposed joint-optimized NLMS (JO-NLMS) algorithm is evaluated in the framework of acoustic echo cancellation.The JO-NLMS algorithm achieves both fast convergence and tracking, but also low misadjustment.

Abstract: Image quality assessment (IQA) aims at developing computational models that can precisely and automatically estimate human perceived image quality. To date, various IQA methods have been proposed to mimic the processing of the human visual system, with limited success. Here, we present a novel IQA approach named biologically inspired feature similarity (BIFS), which is demonstrated to be highly consistent with the human perception. In the proposed approach, biologically inspired features (BIFs) of the test image and the relevant reference image are first extracted. Afterwards, local similarities between the reference BIFs and the distorted ones are calculated and then combined to obtain a final quality index. Thorough experiments on a number of IQA databases demonstrate that the proposed method is highly effective and robust, and outperform state-of-the-art FR-IQA methods across various datasets. We propose a novel IQA approach named biologically inspired feature similarity (BIFS), which is demonstrated to be highly consistent with the human perception.In the proposed approach, biologically inspired features (BIFs) of the test image and the relevant reference image are first extracted.Afterwards, local similarities between the reference BIFs and the distorted ones are calculated and then combined to obtain a final quality index.Thorough experiments on a number of IQA databases demonstrate that the proposed method is highly effective and robust, and outperform state-of-the-art FR-IQA methods across various datasets.

Abstract: In the case where the measurement noise involves impulsive interference, distributed estimation algorithms based on the mean-square error (MSE) criterion may suffer from severely degraded convergence performance or divergence. To address this problem, we modify the adapt-then-combine (ATC) diffusion LMS (DLMS) algorithm by applying the sign operation to the error signals at all agents to develop a diffusion sign-error LMS (DSE-LMS) algorithm. Furthermore, the stochastic behavior of the DSE-LMS algorithm is analyzed for Gaussian inputs and contaminated Gaussian noise based on Price's theorem. Simulation results show the robustness of the DSE-LMS algorithm against impulsive interference and validate the theoretical findings. HighlightsThe diffusion LMS algorithm may suffer from severely degraded convergence performance in impulsive interference environments.The proposed diffusion sign-error LMS algorithm is robust against impulsive interferences.The stochastic behavior of the diffusion sign-error LMS algorithm is analyzed based on Price's theorem.

Abstract: Anomaly detection is one of the most popular applications in hyperspectral remote sensing image analysis. Anomaly detection technique does not require any prior features or information of targets of interest and has draw the increasing interest in target detection domain for hyperspectral imagery (HSI) in the recent twenty years. From hyperspectral data, the approximately continuous spectral features which are attributed to the high spectral resolution of hyperspectral image can be achieved. Unfortunately, most conventional anomaly detectors merely take advantage of the spectral information in hyperspectral data and rarely give the consideration to spatial information within neighboring pixels. With the development of remote sensing technology, the high spatial resolution can also be acquired by the hyperspectral airborne/spaceborne sensors. Then, further improvement in algorithmic performance may be achieved if both the spectral and spatial information is combined. This article proposes a novel local summation anomaly detection method (LSAD) which combines the multiple local distributions from neighboring local windows surrounding the pixel under test (PUT) with spectral-spatial feature integration. Some other detection performance enhanced operations such as feature extraction and edge expansion are also used. The proposed local summation anomaly detection method makes allowance for exploiting more sufficient local spatial neighboring relationship of local background distribution around the test pixel considered in detection processing. Moreover, summated local background statistics can get better performance in suppressing background materials and extruding anomalies. Feature extraction enables LSAD with robust background feature statistics and edge expansion can ensure no loss of edge detection information. Experiments are implemented on a simulated PHI data and two real hyperspectral images. The experimental results demonstrate that the proposed anomaly detection strategy outperforms the other traditional anomaly detection methods. Anomaly detection technique has drawn the increasing interest in hyperspectral imagery processing fields.This article proposes a novel local summation anomaly detection method.The method combines the multiple local distributions from neighboring local windows with spectral-spatial feature integration.

Abstract: On the basis of the auxiliary model identification idea, this paper studies the filtering based parameter estimation issues for a class of multivariable control systems with colored noise. An auxiliary model based hierarchical stochastic gradient (AM-HSG) algorithm is given for comparison and a data filtering AM-HSG identification algorithm is derived by using the data filtering technique. Its main key is to decompose a multivariable system into two subsystems and to coordinate the associate items between two subsystem identification algorithms. The convergence analysis indicates that the parameter estimates given by the presented algorithms converge to the true values under proper conditions by using the stochastic process theory. The simulation results show that the proposed hierarchical stochastic gradient estimation algorithms are effective. HighlightsThe parameter estimation of multivariable systems with colored noise are discussed.An auxiliary model based hierarchical stochastic gradient (HSG) method is proposed.A filtering based auxiliary model HSG algorithm is proposed through filtering data.The filtering based algorithm can generate higher accurate parameter estimates.The convergence theorems of the proposed estimation algorithms are established.

Abstract: The incipient fault detection in industrial processes with unknown distribution of measurements signals and unknown changed parameters is an important problem which has received much attention these last decades. However most of the detection methods (online and offline) need a priori knowledge on the signal distribution, changed parameters, and the change amplitude (Likelihood ratio test, Cusum, etc.). In this paper, an incipient fault detection method that does not need any a priori knowledge on the signals distribution or the changed parameters is proposed. This method is based on the analysis of the Kullback-Leibler Divergence (KLD) of probability distribution functions. However, the performance of the technique is highly dependent on the setting of a detection threshold and the environment noise level described through Signal to Noise Ratio (SNR) and Fault to Noise Ratio (FNR). In this paper, we develop an analytical model of the fault detection performances (False Alarm Probability and Missed Detection Probability). Thanks to this model, an optimisation procedure is applied to optimally set the fault detection threshold depending on the SNR and the fault severity. Compared to the usual settings, through simulation results and experimental data, the optimised threshold leads to higher efficiency for incipient fault detection in noisy environment. HighlightsWe propose an incipient fault detection method that does not need any a priori information on the signals distribution or the changed parameters.We show that the performance of the technique is highly dependent on the setting of a detection threshold and the environment noise level.We develop an analytical model of the fault detection performances (False Alarm Probability and Missed Detection Probability).Based on the aforementioned model, an optimisation procedure is applied to optimally set the fault detection threshold depending on the noise and the fault severity.Compared to the usual settings, a performed validation of this approach with through simulation results and experimental data is given.