Filter Matrix Research Articles

Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection

In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation has been investigated for hyperspectral imagery (HSI) detection, where approximation of testing pixel can be obtained by solving l1-norm minimization. However, l1-norm minimization does not always yield a sufficiently sparse solution when a dictionary is not large enough or atoms present a certain level of coherence. Comparatively, non-convex minimization problems, such as the lp penalties, need much weaker incoherence constraint conditions and may achieve more accurate approximation. Hence, we propose a novel detection algorithm utilizing sparse representation with lp-norm and propose adaptive iterated shrinkage thresholding method (AISTM) for lp-norm non-convex sparse coding. Target detection is implemented by representation of the all pixels employing homogeneous target dictionary (HTD), and the output is generated according to the representation residual. Experimental results for four real hyperspectral datasets show that the detection performance of the proposed method is improved by about 10% to 30% than methods mentioned in the paper, such as matched filter (MF), sparse and low-rank matrix decomposition (SLMD), adaptive cosine estimation (ACE), constrained energy minimization (CEM), one-class support vector machine (OC-SVM), the original sparse representation detector with l1-norm, and combined sparse and collaborative representation (CSCR).

Empirical evaluation of sand filters to evolve practical designs for artificial recharge through dry wells

• Agricultural runoff can be filtered for groundwater recharge through dry wells. • Hydraulic conductivity of filters reduced exponentially with cumulative filtration. • Provision of sedimentation tank or raised filter helped maintain high conductivity. • The top deposition and entrapment in 5 cm accounted for 87.5% of the total sediment. • Removal of top 3–4 cm of layer resulted in 70–80% recovery of hydraulic conductivity. A laboratory based empirical assessment of hydraulics of sand filters was carried out under the compelling practical condition of exposure to alternate cycles (wet and dry spell) of filtration. The lab-scale simulations purportedly attempted to mimic the field scale condition of sediment loading in runoff as suspended sediment. The sand filters having a layer thickness of 50 cm and 30 cm with a particle size of <2 mm and 0.2–0.5 mm on the gravel-pebble base were tested for the progressive change in hydraulic conductivity and sediment removal during the filtration event under a combination of treatments using different sediment concentrations (1.5 g l −1 , 2 g l −1 , and 2.5 g l −1 ) and water heads (20 cm and 40 cm). A sharp reduction in filter hydraulic conductivity was observed in the case of higher sediment concentration. A filtration hiatus of 2 days between tests resulted in significant improvement in the hydraulic conductivity of the filter matrix. The provisions of pre-entry sedimentation trap before filtration could maintain a scaled-up hydraulic conductivities for a longer duration. A layer of additional reinforcement of an agro-net above the filter matrix, though facilitated easy cleaning after a filtration event, but resulted in a scaled-down hydraulic efficiency. Removal of the top 3 to 4 cm of the filter top could recover about 70 to 80% of the initial hydraulic conductivity. After filtration of sediment equivalent to 16.1 and 17.0 kg m −2 , the top deposition accounted for 72.07 and 73.12%, respectively, which along with the sediment entrapped in the top 5 cm accounted for more than 87% of the total sediment. A steady state concentration of sediment in the filtrate in all the above combinations was found to converge asymptotically below 0.10 g l −1 which, in a limited means used as a basis to custom designed field scale recharge filter using the sand as matrix filter.

DAYENU: a simple filter of smooth foregrounds for intensity mapping power spectra

ABSTRACT We introduce DPSS Approximate lazY filtEriNg of foregroUnds (dayenu), a linear, spectral filter for H i intensity mapping that achieves the desirable foreground mitigation and error minimization properties of inverse co-variance weighting with minimal modelling of the underlying data. Beyond 21-cm power-spectrum estimation, our filter is suitable for any analysis where high dynamic-range removal of spectrally smooth foregrounds in irregularly (or regularly) sampled data is required, something required by many other intensity mapping techniques. Our filtering matrix is diagonalized by Discrete Prolate Spheroidal Sequences which are an optimal basis to model band-limited foregrounds in 21-cm intensity mapping experiments in the sense that they maximally concentrate power within a finite region of Fourier space. We show that dayenu enables the access of large-scale line-of-sight modes that are inaccessible to tapered discrete Fourier transform estimators. Since these modes have the largest SNRs,dayenu significantly increases the sensitivity of 21-cm analyses over tapered Fourier transforms. Slight modifications allow us to use dayenu as a linear replacement for iterative delay clean ing (dayenurest). We refer readers to the Code section at the end of this paper for links to examples and code.

Applicability of benchtop multi-wavelength polar photometers to off-line measurements of the Multi-Angle Absorption Photometer (MAAP) samples

In this study, the applicability of a benchtop polar photometer (PP_UniMI) to retrieve multi-wavelength aerosol absorption coefficient data by off-line measurements of the Multi-Angle Absorption Photometer (MAAP) sample spots is presented. MAAP is a widespread single wavelength online instrument providing the aerosol absorption coefficient and the equivalent black carbon concentration. In this work, MAAP samples collected during four different campaigns were analysed off-line with PP_UniMI.First of all, data from PP_UniMI and MAAP were compared to investigate contributions to measurement uncertainties. In particular, the role of the following assumptions performed in the MAAP was investigated:1) reconstructing angular distribution of light scattered by filter samples from measurements at three fixed angles using analytical functions;2) setting the fraction of the back-scattered radiation by the blank filter (BM) at a fixed value BM = 0.7;3) assuming a fixed value for the asymmetry factor g = 0.75.Samples collected at several sites showed an agreement within 5% when data from the two instruments were retrieved using the same approximations (i.e. backscattered radiation from the filter matrix BM set at a fixed value, phase functions reconstructed by analytical functions from measurements at 3 angles, same asymmetry factor) in the data retrieval algorithm. Conversely, larger differences (14% on average) between off-line measurements and averaged MAAP data were obtained when the high-angular resolved information available by PP_UniMI was exploited. By analysing the role of MAAP assumptions for σap retrieval, it resulted that fixing BM = 0.7 was the main responsible for the detected differences. Indeed, high-angular resolved off-line measurements by PP_UniMI allow to directly measure BM, obtaining BM = 0.88 on white spots. It is noteworthy that the observed results were similar at all investigated sites, so they proved to be independent of the aerosol type and can likely be attributed to instrumental effects. Moreover, a sensitivity test was carried out also to check the impact of the fixed value used for the asymmetry factor (set at g = 0.75 in both instruments). Varying g values within the typical range for ambient aerosol (0.50–0.75) the estimate of aerosol absorbance ABS (directly obtained from PP_UniMI measurements and linked to σap) was affected by 8% at most, thus being a minor source of uncertainty in the calculation. The effect of the variability in blank spots used for off-line analyses was also evaluated and resulted in a contribution smaller than 3% to the uncertainty of the methodology employed.Finally, the possibility of exploiting multi-wavelength assessment of absorption coefficients is an added value of PP_UniMI; indeed, it allows to estimate the contribution of different aerosol sources and components to the absorption coefficient using MAAP tapes used in present or past campaigns.

Structured and Consistent Multi-Layer Multi-Kernel Subtask Correction Filter Tracker

Some multi-task correlation filter trackers achieve the top-ranked performance in terms of accuracy and robustness. However, they directly fuse multiple types of features into a single kernel space. This operation fails to fully explore the discriminative strength and diversity of different features, and also ignores the structured correspondence of different tasks. To solve these issues, we propose a structured multi-kernel subtask correlation filter tracker with temporal-spatial consistency, which enjoys the merits of both layered multi-kernel subtask learning and structured correlation filter. Specifically, we firstly assign one kernel space to each channel feature. Multi-channel features correspond to multi-kernel spaces to boost their powerful discriminability. And then, we divide the target into multi-layer patches with different sizes, and regard the correlation filter trace of each patch with one channel feature as a subtask. In the following, we incorporate globally and locally structured correlation filters into a unified multi-kernel subtask particle tracking framework. The global and local subtasks complement and enhance each other with similar motion model. The proposed tracker not only exploits the cooperation and complementarity of layered multi-kernel subtask correlation filters, but also mines the underlying geometric structure of global subtasks, and the inner spatial locality correspondences of local subtasks inside the target. This operation is achieved by dual group sparsity regularized terms with mixed-norm l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p,q</sub> , which decomposes the multi-kernel subtask filter matrix into two collaborative components. They correspond to the adaptive filter feature selection and outlier subtask detection, respectively. Besides, the developed tracking model maintains the temporal coherence and spatial consistency of multi-layer subtask filters via the smooth regularizer. Finally, the tracking formulation is optimized by the accelerated proximal gradient approach (APG). Encouraging analyses on six benchmark datasets, verify the favorable effectiveness and robustness of our method against state-of-the-art trackers.

Radiomics Analysis and Unsupervised Self-Organizing-Map Technique to Predict Radiation-Induced Pneumonitis in Patients with Lung Cancer

Lung radiation pneumonitis (RP) is one of the major toxicities experienced by lung cancer patients (13%–37%) receiving thoracic radiation therapy (RT) (Kocak Z, et al. Int. J. Radiat. Oncol. Biol. Phys. 2005 and Rodrigues G, et al. Oncol. 2004). Investigation of imaging biomarkers that can predict the incidence of RP can be useful toward reduce the probability of RP development. In this pilot study, we investigated the application of Kohonen Self-Organizing Map (K-SOM; a type of adaptive model that is trained using unsupervised competitive-learning (Kohonen T, Springer 1995)), for analysis of radiomic features extracted from normal lung tissue of non-RP and RP patients. The goal was to build an adaptive model to stratify patients and to reveal dominant characteristics of radiomics features for patient stratification. These characteristics are likely to remain unnoticed using conventional statistical analysis. To perform deep and unsupervised analysis of radiomics features extracted from planning CT images of normal lung tissue for patients with locally advanced, non-small cell lung cancers (NSCLC) to characterize and stratify patients with and without radiation-induced RP. Planning CT images of 41 patients (14 with RP and 27 with no evidence of RP) with stage-III lung cancers, treated with IMRT/3D-CRT, were studied. One hundred sixty eight radiomics features were extracted from the volume of normal lung tissue receiving ≥20 Gy, excluding the ITV, according to the following 8 different classes: Intensity Histogram Based Features (IHBF), Gray Level Run Length (GLRL), Law’s Textural information (LAWS), Discrete Orthonormal Stockwell Transform (DOST), Local Binary Pattern (LBP), 2D-Wavelet Transform (2DWT), 2D-Gabor Filter (2DGF), and Gray Level Co-Occurrence Matrix (GLCM). A K-SOM ([9×9]-neurons) was constructed using 168 radiomic features and was trained and evaluated using random-permutation-sampling method (100-iterations, 67% and 33% for modeling and testing respectively). Regions pertaining to the dominant characteristics of the discriminant features were identified in the K-SOM space and used to calculate stratification power of the classifier. The K-SOM revealed 3 dominant regions associated with the RP status: high-certainty-non-RP, high-certainty-RP, and intermediate/uncertain region. The average of AUC, positive predictive and negative predictive values were 76.20%, %71.65, and 82.12% respectively. Correlation-based dissimilarity analysis (r<0.2, p-value<0.05) ranked and revealed the stability and robustness of the 6 discriminant features as follows: Skewness, Entropy, and Moment (3 and 4) from IHBF, Entropy-CA1 from DOST, and Short-Run-Emphasis from GLRL. The results of this pilot study, albeit subject to confirmation in a larger patient population, suggest a potential role for the use of an unsupervised method for radiomics-based stratification and prediction of radiation-induced pneumonitis in patients with locally advanced NSCLC.

Engineering Applications of Adaptive Kalman Filtering Based on Singular Value Decomposition (SVD)

This paper presents the results of applying the new mechanization of the Kalman filter (KF) algorithm using singular value decomposition (SVD). The proposed algorithm is useful in applications where the influence of round-off errors reduces the accuracy of the numerical solution of the associated Riccati equation. When the Riccati equation does not remain symmetric and positive definite, the fidelity of the solution can degrade to the point where it corrupts the Kalman gain, and it can corrupt the estimate. In this research, we design an adaptive KF implementation based on SVD, provide its derivation, and discuss the stability issues numerically. The filter is derived by substituting the SVD of the covariance matrix into the conventional discrete KF equations after its initial propagation, and an adaptive estimation of the covariance measurement matrix Rk is introduced. The results show that the algorithm is equivalent to current methods in terms of robustness, and it outperforms the estimation accuracy of the conventional Kalman filter, square root, and unit triangular matrix diagonal (UD) factorization methods under ill-conditioned and dynamic applications, and is applicable to most nonlinear systems. Four sample problems from different areas are presented for comparative study from an ill-conditioned sensitivity matrix, navigation with a dual-frequency Global Positioning System (GPS) receiver, host vehicle dynamic models, and distance measuring equipment (DME) using simultaneous slant range measurements, performed with a conventional KF and SVD-based (K-SVD) filter.

Vehicle Re-Identification With Image Processing and Car-Following Model Using Multiple Surveillance Cameras From Urban Arterials

In this paper, a vehicle Re-ID framework which integrates image processing and traffic flow model is developed. First, the CNN network is applied for vehicle detection and tracking and extracting attribute recognition. Particularly, attributes including vehicle color, type, make, and Re-ID feature are extracted to derive a similarity matrix between upstream and downstream vehicles. However, solely using these features could not achieve satisfaction matching accuracy. Our testing only shows a moderate accurate of around 72.3%. To further improve the Re-ID rate, this paper integrates visual information with the well-known IDM car-following mode. In our framework, IDM is first used to estimate the arrival time window for each upstream vehicle; and then with this time window derive a filter matrix which set the similarity as 0 for the matching vehicles outside the time window. Combining similarity matrix and filter matrix, the new developed Re-ID framework improves the matching rate to 95.7%. Furthermore, the proposed framework can even help identify vehicles that may have changed lanes, overtaken vehicles or driven on a sideroad. Such information is certainly valuable for future research on performance measure, traffic control, and congestion mitigation. Considering the significance of the trajectory data to nowadays traffic control and management and popularity of today’s surveillance cameras, this research certainly will contribute to the improvement of arterial traffic performance measure and efficient control.

Sub-full Model-Based Heterogeneous Sensor Fusion for Lateral State Estimation of Preceding Target Vehicles

Trajectory planning of an automated vehicle requires behavior knowledge of preceding target vehicles (PTVs), and lateral states, such as lateral velocity and yaw rate are key enablers for precise behavior description. However, lateral states of a PTV can hardly be measured directly by common onboard sensors. In addition, although these states can be transmitted via vehicle to vehicle (V2V), the accuracy is limited by the state holding process during the communication interval. Aiming at improved PTV lateral states, this article considers the estimation of these states using visual and V2V measurements, and proposes a three-stage fusion architecture consisting of input estimator, heterogeneous model-based local estimators, and fusion center. Specifically, to cope with the low rate of the received steering angle, the input estimator is constructed to obtain high rate control inputs. In the local estimator design, unlike the conventional modeling problems mixing control input errors with model errors in process noise, this paper separates them and develops a model adaption algorithm to compensate the time-varying covariance of the errors of the estimated control inputs. Then, to fuse the heterogeneous local estimates in the fusion center, a subfull model-based information matrix filter is designed to address this specific heterogeneous fusion problem, in which the local models are treated as submodels of a common full model. Hardware-in-the-loop experimental results show that the proposed method gives more accurate estimates in comparison with other approaches.

Substantial Improvement of Oil Aerosol Filtration Performance Using In-Plane Asymmetric Wettability.

Oil aerosol usually causes air pollution, health issues, and corrosion to equipment. The removal of aerosol oil particles from the air is a crucial process in industrial production and daily life. Although fibrous filters have been a widely used material for the separation of oil aerosol from the air, it is still a challenge to separate submicrometer aerosol oil particles with both high filtration efficiency and low resistance. Herein, we report a novel approach to markedly reduce the pressure drop of a fibrous filter and simultaneously increase its aerosol filtration efficiency, only by surface treatment to make the filter have in-plane alternating superoleophilic and superoleophobic patterns. We used a spraying method to prepare superoleophobic and superoleophilic patterns on the filter. The best filtration results were achieved when two layers of the patterned filters that have superoleophobic and superoleophilic strips (both width, 5 mm) were stacked in a way that the opposite wetting surfaces contacted each other between the layers. The filter showed a much-reduced filtration resistance and the pressure drop (4.16 kPa) at the pseudo-steady state being at least 45% lower when compared to the two-layer controls with a homogeneous surface wettability (i.e., untreated surface, superoleophobicity, and superoleophilicity). It also showed higher filtration efficiency (98.37% for small oil mists and 99.99% for large oil mists) and over two times higher quality factor (0.99 kPa-1 for small oil mists and 2.27 kPa-1 for large oil mists). The asymmetric wettability leads to the formation of unobstructed channels for the air stream to penetrate through the filter matrix, leading to a low resistance with improved oil capture efficiency. The pattern strip width showed an effect on filtration performance. This unexpected finding may provide a novel approach to designing high-performance, low energy consumption, and long-life coalescence filters.

Wavelet-based progressive fast recompression for large deformed mesh

Progressive compression is one of the most effective multi-resolution schemes for sending large 3D meshes in a network environment. However, if we change the 3D meshes, i.e., adding watermarks or changing the pose of models, the deformed 3D mesh model should be compressed again from scratch and it may take too much time especially for large mesh models. In this paper, we greatly reduce the compression time for deformed models, which we call recompression, when connectivity-preserving deformation is applied to mesh models. We first compress a large mesh progressively using a wavelet-based method, and then extract vertices and wavelet coefficients affected by deformed area using our propagation algorithm. Direct modification acts on the required elements using original matrix filters without additional postprocessing, boosting compression performance. Our method increases the recompression speed up to 17 fold compared with the original speed, even if the entire mesh is deformed. We also show that the speedup increases up to 33 fold when local deformation acts on the local regions comprising 5% of mesh.

Blackwater processing via vermifiltration: Worm-based toilet (wormlet) in the arid context of Burkina Faso

In Burkina Faso, faecal sludge is mostly discharged into the environment untreated, with associated public health risks. Thus, sanitation technology that efficiently treats Blackwater (BW) in-situ is urgently required. This paper reports the design and testing of a proposed worm-based toilet (wormlet). The presence of E.eugeniae (EE) is found to reduce the total accumulated BW by a maximum of 61.74%. Over five to ten weeks, 1 kg of BW (dry portion) can be processed to 0.49–0.61 kg of vermicast by the EE. The wormlet effluent is of superior quality, as the log 10 removal efficiency of the bedding and filter matrix reached 7.45 and 5.85 for E. coli and faecal coliform, respectively. Helminth egg removal was found to be 97% within the 75-cm depth. Average organics and nutrient removal efficiencies are 99–100% for total solids and total suspended solids; 99% and 98% for the biochemical and chemical oxygen demand, respectively. Removal efficiencies between 97 and 99% were found for NH4+, NO3−, NO2−, and PO43−. A 100% turbidity removal was found in the systems containing charcoal. Thus, the wormlet appears to be superior for in-situ BW treatment, generating sub-products that can be reused or safely discarded.

A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models

With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and flexible sense – we demand to attack various kinds of graph embedding model with black-box driven. To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter. As such, a generalized adversarial attacker: GF-Attack is constructed by the graph filter and feature matrix. Instead of accessing any knowledge of the target classifiers used in graph embedding, GF-Attack performs the attack only on the graph filter in a black-box attack fashion. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experimental results validate the effectiveness of our attacker on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different graph embedding models.

A novel compound fault diagnosis method using intrinsic component filtering

The compound fault diagnosis of gearboxes, which can prevent breakdown accidents and minimize production loss, has become a challenging hotspot. In practical applications, the early fault signal is weak and often concealed by environmental noise. Therefore, separating the different fault components from signals with weak faults and strong noise is the key to performing compound fault diagnosis. In this paper, a novel compound fault diagnosis method based on intrinsic component filtering is proposed for compound fault diagnosis, which can also be regarded as a multi-dimension blind deconvolution method. Firstly, a Hankel matrix is constructed from the collected vibration data. Secondly, the filter matrix is trained by intrinsic component filtering in an unsupervised way without any time-consuming preprocessing or a prior basis. Finally, Hilbert demodulation analysis is conducted on the filtered data. Different fault components can be diagnosed according to the envelope spectra of the fault components. The separation performance of the proposed method is validated by the simulation data and the experiment signals in a noisy environment. The results show that the proposed method can train the different filters using the compound fault adaptively, demonstrating superior performance to existing methods.

Image-based effective feature generation for protein structural class and ligand binding prediction.

Proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein’s tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-ligand binding is accountable for managing the tasks of biological receptors that help to cure diseases and many more. Therefore, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-ligand binding prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we have proposed a supervised learning algorithm for predicting protein-ligand binding, which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms.

Robust H∞ filtering for finite-time boundedness of Markovian jump system with distributed time-varying delays

This paper is concerned with the problem of the guaranteed filtering with interval time-varying delay and Markovian jumping system. By constructing Lyapunov–Krasovskii functional, a novel delay-dependent criterion is presented such that the error system is stochastically finite-time bounded. In order to obtain results, Jensen's inequality, Wirtinger's integral inequality and Auxiliary function-based integral inequalities are employed. The filter matrix can be achieved by solving the linear matrix inequalities (LMIs). Numerical example and simulation results are provided to demonstrate the effectiveness of the proposed method.

Prediction of Favorable Carbonate Reservoirs under Extremely Thick Salts via Poststack Facies-Controlled and Prestack Zoeppritz Equation Inversions in the Santos Basin of Brazil

Subsalt carbonate rocks in Brazil’s deepwater region have broad prospects for oil and gas exploration and development. Due to the low-frequency bandwidth of the seismic data and the poor signal quality for this kind of reservoir target, there is a demand for accurate seismic prediction methods. We employ the facies-controlled inversion using a low-pass filter matrix to ensure the accuracy of the low frequency and to improve the robustness of the inversion results. We integrated the concept of adaptive regularization constraint of the Zoeppritz equation into the generalized linear inversion theory framework, which overcomes the shortcomings of the approximate equation. Making full use of the large angle prestack seismic information, Zoeppritz equation inversion improves the accuracy of the inversion results. The application of this method in carbonate reservoirs under extremely thick salts in the Santos Basin of Brazil indicates the feasibility and practicality of the proposed integrated prediction method.

A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

Reducing noise from images is an essential structure of the image processing study. Noises can arise with images through achievement on diffusion. The existence of noise can delay the right operation of these images for many applications such as satellite and medical images. Reducing denois in images multiplicatively (DIM) has been developed and modified by many researchers during the past few years. DIM can destroy almost all data of the original image, especially the texture of images. Our aim is to present a new technique to solve this problem. The technique is based on a new fractional calculus called the conformable fractional calculus (CFC). This type of calculus has advantages because of its formula involves a controller, which can be applied to complex problems such as DIM. The proposed structures of CFC windows are given by four masks suggested for x and y directions. On four directional angles, a convolution operational product of the input image pixels with a CFC mask window has been completed. The visual observation and peak signal-to-noise ratio with Root Mean Square Error are employed for measurements. The experiments showed that the skillful filtering outcomes are indicated high score than some well known filers such as Gaussian filter, Sobel edge filter, Canny edge filter and gray-level co-occurrence matrix. Compering is illustrated as well with newly researches.

Some Actual Problems of Magnetic Filters With Magnetized Packed Beds and Comparative Analysis of Filter Performances

In this article, the filter performance characteristics of magnetic filters obtained by different physical models were compared. Performance models for magnetic filters were chosen based on the balance of forces and momentum that act on the captured particle, and on the collision efficiency of the particle with the filter matrix. The filter performance characteristics of the filtration systems are evaluated based on the magnitude of the external magnetic field, the rate of filtration, the variation of matrix element sizes, and captured particles. Three-dimensional variations of these characteristics were investigated. The experimental data from the literature for the different working conditions were used for comparing various models. The effect of filtration velocity and size variation of the captured particles on the magnetic filter performance over a wide range were evaluated using different models. As a result, a practical mathematical model with the exponential change of the system parameters was found to offer more possibilities to determine the magnetic filter performance. It was proposed that it is possible to determine whether the obtained filter performance models are valid either by making appropriate experiments or using the data in the literature as reference values.

Shortest multi-wavelet matrix filters for refinable vector splines

In multi-wavelet decomposition techniques for the analysis of a given vector-valued signal, it is desirable with respect to the computational efficiency of the associated algorithms that the low-pass and high-pass matrix filters sequences be as short as possible. By applying a multi-wavelet construction method based on the solving of a system of four matrix Laurent polynomial identities, and using as main building block a class of arbitrarily smooth refinable vector splines, we establish an explicitly formulated class of shortest possible associated matrix filter sequences for decomposition, as well as minimally supported spline multi-wavelets for these optimal filter sequences.