Adaptive Regularization Parameter Research Articles

Regularized Tapered Sample Covariance Matrix

Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamforming (widening the spectral null width associated with an interferer). In this paper, the focus is on high-dimensional setting where the dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p$</tex-math></inline-formula> is high, while the data aspect ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n/p$</tex-math></inline-formula> is low. We propose an estimator called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tabasco</small> (TApered or BAnded Shrinkage COvariance matrix) that shrinks the tapered sample covariance matrix towards a scaled identity matrix. We derive optimal and estimated (data adaptive) regularization parameters that are designed to minimize the mean squared error (MSE) between the proposed shrinkage estimator and the true covariance matrix. These parameters are derived under the general assumption that the data is sampled from an unspecified elliptically symmetric distribution with finite 4th order moments (both real- and complex-valued cases are addressed). Simulation studies show that the proposed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tabasco</small> outperforms all competing tapering covariance matrix estimators in diverse setups. An application to space-time adaptive processing (STAP) also illustrates the benefit of the proposed estimator in a practical signal processing setup.

Sorted L1 regularization method for damage detection based on electrical impedance tomography.

Carbon fiber reinforced polymers (CFRPs) are composite materials in which carbon provides strength and stiffness, whereas polymers provide cohesiveness and toughness. The electrical impedance of CFRP laminates is changed due to different kinds of damages. Electrical impedance tomography (EIT) has significant advantages such as non-intrusion, portability, low cost, and quick response and has widely been used as a nondestructive testing method. Therefore, EIT has great potential in structural health monitoring of CFRPs. Regularization can solve the ill-posed inverse problem of EIT. However, conventional regularization algorithms have their own limitations, such as over-smoothness of reconstructed edges and unstable solution caused by measurement noise. In addition, the anisotropic property of CFRPs also affects the image quality based on traditional methods. In this paper, the sorted L1-norm regularization is proposed. It promotes grouping highly correlated variables while encouraging sparsity by using more effective penalty terms. The sharp edges between different materials can be obtained, and the obtained solution is more stable. The image quality of different objects, especially the image quality of multi-targets, can be significantly improved with this new method. In addition, the sorted L1 norm can generate adaptive regularization parameters without empirical selection. The new regularization problem is solved by the alternating direction method of multipliers. Both experimental and simulation results demonstrate that the sorted L1 norm improves the quality of reconstructed images under various noise levels. The proposed method is comprehensively evaluated with three image quality criteria by numerical simulation quantitatively.

Activity recognition from Smartphone data using WSVM-HMM classification

A lot of real-life mobile sensing applications are becoming available nowadays. The traditional approach for activity recognition employs machine learning algorithms to learn from collected data from smartphpne and induce a model. The model generation is usually performed offline on a server system and later deployed to the phone for activity recognition. In this paper, we propose a new hybrid classification model to perform automatic recognition of activities using built-in embedded sensors present in smartphones. The proposed method uses a trick to classify the ongoing activity by combining Weighted Support Vector Machines (WSVM) model and Hidden Markov Model (HMM) model. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our proposed method outperforms the state-of-the-art on a large benchmark dataset.

Flexible Krylov Methods for Edge Enhancement in Imaging.

Many successful variational regularization methods employed to solve linear inverse problems in imaging applications (such as image deblurring, image inpainting, and computed tomography) aim at enhancing edges in the solution, and often involve non-smooth regularization terms (e.g., total variation). Such regularization methods can be treated as iteratively reweighted least squares problems (IRLS), which are usually solved by the repeated application of a Krylov projection method. This approach gives rise to an inner–outer iterative scheme where the outer iterations update the weights and the inner iterations solve a least squares problem with fixed weights. Recently, flexible or generalized Krylov solvers, which avoid inner–outer iterations by incorporating iteration-dependent weights within a single approximation subspace for the solution, have been devised to efficiently handle IRLS problems. Indeed, substantial computational savings are generally possible by avoiding the repeated application of a traditional Krylov solver. This paper aims to extend the available flexible Krylov algorithms in order to handle a variety of edge-enhancing regularization terms, with computationally convenient adaptive regularization parameter choice. In order to tackle both square and rectangular linear systems, flexible Krylov methods based on the so-called flexible Golub–Kahan decomposition are considered. Some theoretical results are presented (including a convergence proof) and numerical comparisons with other edge-enhancing solvers show that the new methods compute solutions of similar or better quality, with increased speedup.

Activity Recognition From Smartphone Data Using WSVM-HMM Classification

A lot of real-life mobile sensing applications are becoming available nowadays. The traditional approach for activity recognition employs machine learning algorithms to learn from collected data from smartphpne and induce a model. The model generation is usually performed offline on a server system and later deployed to the phone for activity recognition. In this paper, we propose a new hybrid classification model to perform automatic recognition of activities using built-in embedded sensors present in smartphones. The proposed method uses a trick to classify the ongoing activity by combining Weighted Support Vector Machines (WSVM) model and Hidden Markov Model (HMM) model. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our proposed method outperforms the state-of-the-art on a large benchmark dataset.

Activity recognition from smartphone data using weighted learning methods

Mobile phone based activity recognition uses data obtained from embedded sensors to infer user’s physical activities. The traditional approach for activity recognition employs machine learning algorithms to learn from collected labeled data and induce a model. To enhance the accuracy and hence to improve the overall efficiency of the system, the good classifiers can be combined together. Fusion can be done at the feature level and also at the decision level. In this work, we propose a new hybrid classification model Weighted SVM-KNN to perform automatic recognition of activities that combines a Weighted Support Vector Machines (WSVM) to learn a model with a Weighted K-Nearest Neighbors (WKNN), to classify and identify the ongoing activity. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our method outperforms the state-of-the-art on a large benchmark datasets.

Adaptive regularized parameter selection based on random projection

Arbitrary projection is a well-known AI calculation, which can be executed by neural organizations and prepared in an exceptionally productive way. Notwithstanding, the quantity of highlights should be huge enough when applied to a fairly enormous scope informational collection, which brings about moderate speed in testing technique and more extra room under certain conditions. Besides, a portion of the highlights are repetitive and even uproarious since they are arbitrarily created, so the presentation might be influenced by these highlights. In the proposed framework is to actualize Adaptive Regularization Parameter Selection. It will regularize the highlights which are significant and select a most appropriate component for an explicit issue. The primary objective is to diminish the computational expense of both characterization and relapse task by utilizing Randomization Algorithm. It gives the most ideal outcome to the high-dimensional improvement. It utilizes Multilayer neural organization for performing direct and non-straight capacity .

Rosette Trajectories for Fast MRI Based on an Adaptive Reconstruction Method

Non-Cartesian MRI k-space trajectories provide faster and more motion-robust data acquisitions than those of Cartesian trajectories. In this paper, we focus on the rosette trajectory and generalize the weighted rosette trajectories for fast undersampled k-space data acquisition. However, single-slice imaging using the rosette trajectory will be affected by the off-resonance effect. To reduce the artifacts from off-resonance slices, this paper introduced an adaptive iterative reconstruction method derived from the TV and nuclear norm regularization terms for raw MRI data reconstruction. This reconstruction problem was separated into two subproblems and solved with an adaptive regularization parameter. The results show that the weighted rosette trajectory that offers short acquisition times and good off-resonance behaviors with little blurring can be used for reconstruction with adaptive regularization parameters and achieve a superior performance.

Co-Located MIMO Radar Target Detection in Cluttered and Noisy Environment Based on 2D Block Sparse Recovery

In the co-located multiple input multiple output (MIMO) radar, the matched filter receiver suffers from high range sidelobes, since the waveforms originating from different transmitters cannot be fully orthogonal. Traditional sparse recovery based receivers can suppress the sidelobes by assuming the sparsity of range profiles. However, in most practical situations, the range profile is not sparse due to the presence of clutter. In this paper, an effective co-located MIMO radar target detection algorithm is proposed based on modified smoothed-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm sparse recovery, by exploiting the 2D block sparsity of the range-Doppler profiles of targets corresponding to different transmit-receive pairs. The proposed method can enhance the target detection performance in co-located MIMO radar system, especially for those weak targets which may be overwhelmed by high-level sidelobes of strong targets and clutters, without sacrificing computational efficiency. With a well-designed regularization approach and an adaptive regularization parameter update technique, the proposed method is also robust in noisy environment. Simulation and experimental results validate the effectiveness of the proposed method.

2D Inversion of Frequency-domain Electromagnetic Data Generated by Line Current Source

The inversion of electromagnetic data is an inherently non-unique and unstable problem due to the data noises and incomplete data coverage, which means different geo-electrical models could fit the observed data at the same level. A stable solution of an ill-posed inverse problem can be obtained by the regularization methods in which some desired structure are imposed to stabilize the inverse problem. We have developed an algorithm to invert frequency-domain electromagnetic data generated by a line current source for a 2D model of the earth. To stabilize the inversion, we adopt a smoothness-constraint approach for the model parameters and adjust the regularization parameter objectively using an adaptive regularization parameter technique. For calculating the sensitivities using any of the accurate methods will be very time-consuming, we applied the adjoint-equation approximate method. By means of the numerical examples using synthetic data, we have demonstrated that the inversion method can be effectively to reconstruct the subsurface resistivity structure.

Joint PP and PS Pre-stack Seismic Inversion for Stratified Models Based on the Propagator Matrix Forward Engine

Pre-stack seismic inversion of the P- and S-wave velocities and bulk density is important in seismic exploration for evaluating lithological units and fluid properties. Generally, this inversion is based on ray-tracing modeling, which introduces errors and requires substantial pre-processing for stratified models due to its oversimplified single-interface assumption. To overcome those problems, we propose a pre-stack inversion method, using wave-equation-based modeling as a forward engine. Most wave-equation-based pre-stack inversions are based on the reflectivity method and adopt nonlinear optimization algorithms, although accurate, but computationally expensive. Hence, we use a fast propagator matrix (PM) method valid for layered media. To improve the stability and accuracy, the PP data inversion is extended to joint PP and PS PM-based inversion (JPMI). A linear inversion scheme is adopted to reduce the computational cost, and the Frechet derivatives are computed accordingly. Moreover, to obtain an optimal model solution, the L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) optimization algorithm and L-curve criterion, an adaptive regularization parameter acquisition method, are implemented. A posterior probability analysis shows that the method has a higher parameter sensitivity than the joint exact Zoeppritz-based inversion and gives better estimations than the single-data inversion. We discuss the effects of dataset weight, internal multi-reflections, time window setting, noise level and initial model by using model tests. Synthetic and real-data examples demonstrate that the algorithm is better than the single PP inversion in terms of stability and accuracy, especially for S-wave velocity and density estimations.

Adaptive Deep Cascade Broad Learning System and Its Application in Image Denoising

This article proposes a novel regularization deep cascade broad learning system (DCBLS) architecture, which includes one cascaded feature mapping nodes layer and one cascaded enhancement nodes layer. Then, the transformation feature representation is easily obtained by incorporating the enhancement nodes and the feature mapping nodes. Once such a representation is established, a final output layer is constructed by implementing a simple convex optimization model. Furthermore, a parallelization framework on the new method is designed to make it compatible with large-scale data. Simultaneously, an adaptive regularization parameter criterion is adopted under some conditions. Moreover, the stability and error estimate of this method are discussed and proved mathematically. The proposed method could extract sufficient available information from the raw data compared with the standard broad learning system and could achieve compellent successes in image denoising. The experiments results on benchmark datasets, including natural images as well as hyperspectral images, verify the effectiveness and superiority of the proposed method in comparison with the state-of-the-art approaches for image denoising.

Random dynamic load identification for stochastic structural-acoustic system using an adaptive regularization parameter and evidence theory

In practical engineering, the non-contact measurement is more excellent due to preventing the potential damage of contact transducers. In this paper, a sound pressure-based method is proposed for identifying the random dynamic load acting on structural-acoustic coupled (SAC) system. Considering the characteristic of pseudo excitation and the influence rule of the regularization parameter, an adaptive regularization parameter is formulated to deal with the ill-posedness and reduce the error amplification of random excitation identification. Compared with the Tikhonov regularization method and Moore-Penrose pseudo inverse (MPI) method, the proposed improved regularization method reduces the identified error by 2.24 × 103 N2/Hz and 2.91 × 105 N2/Hz at 4 Hz and 6 Hz respectively in the studied example. The identified loads by improved regularization method match the actual loads adequately in the full frequency. Furthermore, owing to the uncertainty of the SAC system, the method for solving the stochastic system problem is investigated by the evidence theory combined with the interval analysis. Accordingly, the lower and upper bounds are derived to assess the statistical property of identified random dynamic loads. Finally, the effectiveness of the proposed methods is demonstrated by several numerical examples of complex stochastic systems.

A Quotient Function Method for Selecting Adaptive Dynamic Load Identification Optimal Regularization Parameter

A Quotient Function Method for Selecting Adaptive Dynamic Load Identification Optimal Regularization Parameter

Pavement Crack Detection Using Progressive Curvilinear Structure Anisotropy Filtering and Adaptive Graph-Cuts

Delineation of pavement cracks is essential for the damage assessment and maintenance of pavements. Existing methods are not sufficiently robust to interferences including varied illumination, non-uniform intensity, and complex texture noise. An integrated system for the automatic extraction of pavement cracks based on progressive curvilinear structure filtering and optimized segmentation techniques is proposed in this paper. Considering phase congruency and path morphological transformation, a phase congruency guided multi-scale path anisotropy filtering (PCmPA) method is first developed to generate a crack saliency map, significantly enhancing crack structures and eliminating isotropic texture noise. Phase congruency guided multi-scale free-form anisotropic filter (PCmFFA) is then presented as an extended curvilinear structure filter considering context information to enhance PCmPA. Finally, to accurately identify crack pixels and background, the two independent global filtering responses are incorporated with the phase congruency map and integrated into the graph-cuts based global optimization model with an adaptive regularization parameter. Experiments are conducted on two public pavement datasets and a self-captured laser-scanned pavement dataset, with results demonstrating that the proposed method can achieve superior performance compared to six existing algorithms.

New Adaptive Regularization Parameter Selection Based on Expected Patch Log Likelihood

Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In t... | Find, read and cite all the research you need on Tech Science Press

Nonconvex Group Sparsity Signal Decomposition via Convex Optimization for Bearing Fault Diagnosis

Bearing fault diagnosis is critical for rotating machinery condition monitoring since it is a key component of rotating machines. One of the challenges for bearing fault diagnosis is to accurately realize fault feature extraction from original vibration signals. To tackle this problem, the novel group sparsity signal decomposition method is proposed in this article. For the sparsity within and across groups’ property of the bearing vibration signals, the nonconvex group separable penalty is introduced to construct the objective function, leading to that the noise between the adjacent impulses can be eliminated and the impulses can be effectively extracted. Furthermore, since the penalty function is nonconvex, the convexity condition of the corresponding objective function to the global minimum is discussed. In addition, to improve the efficiency of parameter selection, this article presents an adaptive regularization parameter selection strategy. Simulation and experimental studies show that compared with the traditional method, the proposed method can better preserve the target components and reducing uncorrelated interference components for bearing fault diagnosis.

An image denoising approach based on adaptive nonlocal total variation

In the nonlocal total variation (NLTV) model the constant regularization parameter λ cannot adaptively control the balance between the regularization term and the fidelity term, which may results in over-smoothing and the more losing image details in non-flat areas when λ is small, or insufficient noise removal in flat areas when λ is large. It is better that λ has different values according to the characteristics of image areas. In this paper, we introduce an adaptive regularization parameter λ(x) which can recognize flat areas and non-flat areas of an image and propose an improved NLTV model by replacing regularization parameter λ in NLTV model with the function λ(x). In addition, we calculate the similarity weight function of our model from the pre-filtered image to reduce the influence of noise on it. Experimental results demonstrate our approach outperforms some existing methods in terms of objective criteria and subjective visual perception.

A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.

An adaptive regularization method for low-dose CT reconstruction from CT transmission data in Poisson–Gaussian noise

Much research has been devoted to the problem of low-dose computerized tomography (CT) image reconstruction with a scan protocol by both lowering the X-ray tube current (low-mAs) and reducing the total number of projection views(sparse view). However, the CT transmission data may be severely corrupted by X-ray quanta noise and system electronic noise. Recently, a nonlocal means (NLM) regularization method for low-dose CT reconstruction was proposed. Although this method is effective in suppressing both Poisson–Gaussian noise and artifacts, it has two disadvantages: the heavy computational burden and blurred edge information in CT reconstruction. This paper proposes an adaptive patch-wise regularization method for low-dose CT reconstruction from available CT transmission data in Poisson–Gaussian noise. The proposed cost function includes a penalized weighted least-square term and two patch-wise regularization terms which are combined with a novel adaptive regularization parameter. The two regularization terms take advantage of image redundant information across different scales. By exploiting both global and local structure information, the reconstruction accuracy is enhanced. In addition, we select the adaptive regularization parameter based on the texture of the image patch so that the edge information is further maintained. Furthermore, our algorithm updates a patch rather than a pixel, the computation burden is greatly reduced. Finally, experiment results show that the proposed adaptive patch-wise regularization method for low-dose CT reconstruction is superior to several conventional regularization-based approaches in terms of computation efficiency and resolution quality.