Anisotropic Diffusion Research Articles

Imaging local diffusion in microstructures using NV-based pulsed field gradient NMR.

Understanding diffusion in microstructures plays a crucial role in many scientific fields, including neuroscience, medicine, or energy research. While magnetic resonance (MR) methods are the gold standard for diffusion measurements, spatial encoding in MR imaging has limitations. Here, we introduce nitrogen-vacancy (NV) center-based nuclear MR (NMR) spectroscopy as a powerful tool to probe diffusion within microscopic sample volumes. We have developed an experimental scheme that combines pulsed gradient spin echo (PGSE) with optically detected NV-NMR spectroscopy, allowing local quantification of molecular diffusion and flow. We demonstrate correlated optical imaging with spatially resolved PGSE NV-NMR experiments probing anisotropic water diffusion within an individual model microstructure. Our optically detected PGSE NV-NMR technique opens up prospects for extending the current capabilities of investigating diffusion processes with the future potential of probing single cells, tissue microstructures, or ion mobility in thin film materials for battery applications.

Stability of diffusion flames under shear flow: Taylor dispersion and the formation of flame streets

Diffusion flame streets, observed in non-premixed micro-combustion devices, align parallel to a shear flow. They are observed to occur in mixtures with high Lewis number (Le) fuels, provided that the flow Reynolds number, or the Peclet number Pe, exceeds a critical value. The underlying mechanisms behind these observations have not yet been fully understood. In the present paper, we identify the coupling between diffusive-thermal instabilities and Taylor dispersion as a mechanism which is able to explain the experimental observations above. The explanation is largely based on the fact that Taylor dispersion enhances all diffusion processes in the flow direction, leading effectively to anisotropic diffusion with an effective (flow-dependent) Lewis number in the flow direction which is proportional to 1/Le for Pe≫1. Validation of the identified mechanism is demonstrated within a simple model by investigating the stability of a planar diffusion flame established parallel to a plane Poiseuille flow in a narrow channel. A linear stability analysis, leading to an eigenvalue problem solved numerically, shows that cellular (or finite wavelength) instabilities emerge for high Lewis number fuels when the Peclet number exceeds a critical value. Furthermore, for Peclet numbers below this critical value, longwave instabilities with or without time oscillations are obtained. Stability regime diagrams are presented for illustrative cases in a Le−Pe plane where various instability domains are identified. Finally, the linear analysis is supported and complemented by time dependent numerical simulations, describing the evolution of unstable diffusion flames. The simulations demonstrate the existence of stable cellular structures and show that the longwave instabilities are conducive to flame extinction.

Performance evaluation of linear and nonlinear filters for despeckling B mode foetal heart ultrasound images

ABSTRACT Early detection of congenital heart disease (CHD), one of the most commonly occurring congenital defects, is important to reduce mortality rates. The major drawback of ultrasound imaging is the inherent speckle noise, making visual examination of anatomical structures a challenging task. This study discusses the effect of denoising using different linear and nonlinear filters on B mode foetal cardiac ultrasound images. The exhaustive study of the performance of linear filters such as mean, Laplacian, and Wiener, and nonlinear filters such as median, anisotropic diffusion, non-local means, and bilateral filters has been carried out. Performance of the filtering technique is evaluated using various fidelity measures and a new assessment parameter edge preserving index (EPI) is also used for evaluation. Non-local means filter outperforms other filtering techniques with average fidelity values as follows: PSNR: 67.97 dB, MSE: 0.01283, RMSE: 0.109, average difference (AD): 0.005, normalized absolute error (NAE): 2 × 10−4, mean absolute error (MAE): 0.011, EPI: 0.999. This work serves as a reference on pre-processing techniques that can be applied to foetal cardiac ultrasound images for beginners.

A Review on Speckle Reduction Techniques in SAR images

Synthetic Aperture Radar (SAR) is satellite imagery that has multiple applications in variegated fields but is often corrupted by single dependent multiplicative speckle noise. Its multiplicative nature decreases scope for image perception, recognition & limits SAR images applications. Thus, increasing the need for effective & astute SAR image despeckling techniques that not only excise speckle noise but also preserve SAR imageries features, details, and resolution quality. This study analyses various research literature & techniques namely, Adaptive Speckle Reduction Filter, Conditional Averaging filter, Speckle Reduction Filter, Anisotropic diffusion, Speckle Reduction Filter and Speckle Reduction Filter from theoretical, quantitative & qualitative aspects using indexes like SSIM, and RMSE to discover the comparatively superior approach.

Electron kinetics in a positive column of AC discharges in a dynamic regime

We have performed hybrid kinetic-fluid simulations of a positive column in alternating current (AC) argon discharges over a range of driving frequencies f and gas pressure p for the conditions when the spatial nonlocality of the electron energy distribution function (EEDF) is substantial. Our simulations confirmed that the most efficient conditions of plasma maintenance are observed in the dynamic regime when time modulations of mean electron energy (temperature) are substantial. The minimal values of the root mean square electric field and the electron temperature have been observed at f/p values of about 3 kHz Torr−1 in a tube of radius R = 1 cm. The ionization rate and plasma density reached maximal values under these conditions. The numerical solution of a kinetic equation allowed accounting for the kinetic effects associated with spatial and temporal nonlocality of the EEDF. Using the kinetic energy of electrons as an independent variable, we solved an anisotropic tensor diffusion equation in phase space. We clarified the role of different flux components during electron diffusion in phase space over surfaces of constant total energy. We have shown that the kinetic theory uncovers a more exciting and rich physics than the classical ambipolar diffusion (Schottky) model. Non-monotonic radial distributions of excitation rates, metastable densities, and plasma density have been observed in our simulations at pR > 6 Torr cm. The predicted off-axis plasma density peak in the dynamic regime has never been observed in experiments so far. We hope our results stimulate further experimental studies of the AC positive column. The kinetic analysis could help uncover new physics even for such a well-known plasma object as a positive column in noble gases.

Brain tumor Classification using Optimized and Relief-based Feature Reduction and Regression Neural Network

The brain is an essential part of the human body for carrying out normal functions. Nowadays, a person's fast-paced lifestyle combined with an unhealthy diet can lead to brain damage. The current state of medicine and imaging could prove to be a great boon in this case. The image's restricted borders and lack of textural variety make it difficult to detect the tumour even in its earliest stages. Before, a picture was categorised with the help of a hybrid feature extraction system and an Extreme Learning Machine. Science also makes use of projection-based categorization. In this study, we will use MRI image characteristics to categorize different types of brain cancer. This is how a neural network is constructed based on patterns for fast processing. An anisotropic diffusion filter was used in the preprocessing of these images. The cancerous tissue is then separated using thresholding and a morphological strategy. Based on the local binary patterns and histogram-oriented gradients of cancer areas, the pattern-oriented features were generated. What we care about are the global, first-order properties of the image. In order to better all learned characteristics, the error rate and the method of relief are both decreased. A regression neural network is then used to categories the feature with the highest ratings from both PSO and the relief technique. All of the work is done in MATLAB R2020b on Windows 10 with the fig-sharing tumour dataset. Finally, its accuracy, sensitivity, and specificity are compared to those of alternative methods.

From Restricted Equivalence Functions on $L^{n}$ to Similarity Measures Between Fuzzy Multisets

Restricted equivalence functions are well-known functions to compare two numbers in the interval between 0 and 1. Despite the numerous works studying the properties of restricted equivalence functions and their multiple applications as support for different similarity measures, an extension of these functions to an n-dimensional space is absent from the literature. In this paper, we present a novel contribution to the restricted equivalence function theory, allowing to compare multivalued elements. Specifically, we extend the notion of restricted equivalence functions from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L^{n}$</tex-math></inline-formula> and present a new similarity construction on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L^{n}$</tex-math></inline-formula> . Our proposal is tested in the context of color image anisotropic diffusion as an example of one of its many applications.

A linearity-preserving diamond scheme with extended least square interpolation for the heterogeneous and anisotropic diffusion problems on polyhedral meshes

A linearity-preserving diamond scheme with extended least square interpolation for the heterogeneous and anisotropic diffusion problems on polyhedral meshes

Information fusion for fully automated segmentation of head and neck tumors from PET and CT images.

PET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi-modal information available are still lacking. Our study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output-level voting-based fusions. The current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image-level fusions were implemented. In addition, a modified U2-Net architecture as DL fusion model baseline was used. Three different input, layer, and decision-level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image-level and network-level fusions), that is, output-level information fusion (voting-based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated. In single modalities, PET had a reasonable performance with a Dice score of 0.77±0.09, while CT did not perform acceptably and reached a Dice score of only 0.38±0.22. Conventional fusion algorithms obtained a Dice score range of [0.76-0.81] with guided-filter-based context enhancement (GFCE) at the low-end, and anisotropic diffusion and Karhunen-Loeve transform fusion (ADF), multi-resolution singular value decomposition (MSVD), and multi-level image decomposition based on latent low-rank representation (MDLatLRR) at the high-end. All DL fusion models achieved Dice scores of 0.80. Output-level voting-based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak , SUVmean and SUVmedian . PET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET-only and CT-only methods. In addition, both conventional image-level and DL fusions achieve competitive results. Meanwhile, output-level voting-based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC.

Anisotropic molecular diffusion in confinement I: Transport of small particles in potential and density gradients

HypothesisDiffusion in confinement is an important fundamental problem with significant implications for applications of supported liquid phases. However, resolving the spatially dependent diffusion coefficient, parallel and perpendicular to interfaces, has been a standing issue. In the vicinity of interfaces, density fluctuations as a consequence of layering locally impose statistical drift, which impedes the analysis of spatially dependent diffusion coefficients even further. We hypothesise, that we can derive a model to spatially resolve interface-perpendicular diffusion coefficients based on local lifetime statistics with an extension to explicitly account for the effect of local drift using the Smoluchowski equation, that allows us to resolve anisotropic and spatially dependent diffusivity landscapes at interfaces. Methods and simulationsAn analytic relation between local crossing times in system slices and diffusivity as well as an explicit term for calculating drift-induced systematic errors is presented. The method is validated on Molecular Dynamics simulations of bulk water and applied to simulations of water in slit pores. FindingsAfter validation on bulk liquids, we clearly demonstrate the anisotropic nature of diffusion coefficients at interfaces. Significant spatial variations in the diffusivities correlate with interface-induced structuring but cannot be solely attributed to the drift induced by local density fluctuations.

Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma

Purpose: The anatomical and physiological processes of the human body are pictured in radiology using different modalities. Magnetic Resonance Imaging (MRI) supports capturing the images of organs using magnetic field gradients. The quality of MR images is generally affected by various noises such as Gaussian, speckle, salt and pepper, Rayleigh, Rican etc. Removal of these noises from the MR images is essential for further diagnostic procedures.&#x0D; Materials and Methods: In this article, Gaussian noise, speckle noise, and salt and pepper noise are added to the MR uterus image for which different filters are applied to remove the noise for precise identification of endometrial carcinoma.&#x0D; Results: The different filters incorporated for the additive noise removal process are the bilateral filter, Non-Local Means (NLM) filter, anisotropic diffusion filter, and Convolution Neural Network (CNN). The efficiency of the filter is calculated by evaluating the response of the filter by gradually increasing the noise intensity of the MR images.&#x0D; Conclusion: Further, peak Signal-to-Noise Ratio (SNR), structural similarity index measure, image quality index and computational cost parameters are computed and analyzed.

Automated Regularized Deconvolution for Eliminating Extra-Column Effects in Fast High-Efficiency Separations.

With the introduction of ultrahigh efficiency columns and fast separations, the need to eliminate peak deformation contributed by the instrument must be effectively solved. Herein, we develop a robust framework to automate deconvolution and minimize its artifacts, such as negative dips, wild noise oscillations, and ringing, by combining regularized deconvolution and Perona-Malik (PM) anisotropic diffusion methods. A asymmetric generalized normal (AGN) function is proposed to model the instrumental response for the first time. With no-column data at various flow rates, the interior point optimization algorithm extracts the parameters describing instrumental distortion. The column-only chromatogram was reconstructed using the Tikhonov regularization technique with minimal instrumental distortion. For illustration, four different chromatography systems are used in fast chiral and achiral separations with 2.1 and 4.6 mm i.d. columns. Ordinary HPLC data can approach highly optimized UHPLC data. Similarly, in fast HPLC-circular dichroism (CD) detection, 8000 plates were gained for a fast chiral separation. Moment analysis of deconvolved peaks confirms correction of the center of mass, variance, skew, and kurtosis. This approach can be easily integrated and used with virtually any separation and detection system to provide enhanced analytical data.

A Self-Charging Concentration Cell: Theory

Batteries are a key resource in the quest for sustainable energy. Here, the theoretical basis is presented for a new type of electrochemical concentration cell that might contribute to this enterprise. The cell, which has been successfully demonstrated in the laboratory, incorporates a chemically asymmetric membrane to drive anisotropic diffusion between two solution chambers; the resulting concentration difference powers the cell. In this study, the membrane’s operation is validated via three theoretical approaches: (i) traditional equilibrium thermodynamics; (ii) balancing drift and diffusion current densities; and (iii) the time-independent diffusion equation. The physical criteria for its operation are developed and its dimensionless variables identified. The cell’s maximum instantaneous power density might exceed 107 W/m3. Its self-charging capability should confer multiple advantages over traditional concentration cells (as well as over some voltaics), including improved thermodynamic efficiency, economy, and compactness. Commonalities with other electrochemical systems (e.g., liquid chromatography, metal corrosion, and solid state diodes) are discussed, and a physical instantiation of the cell is reviewed. Recent numerical simulations corroborate its essential processes.

DIWGAN Optimized with Namib Beetle Optimization Algorithm for Intrusion Detection in Mobile Ad Hoc Networks

ABSTRACT Mobile ad hoc network (MANET) plays a major role in wireless devices such as defense and flooding. Despite their smart applications, MANET faces more security issues than traditional wired and wireless networks on account of their distinct features, such as no central coordination, dynamic topology, temporal network life, and the nature of wireless communication. To overcome these issues, this manuscript proposes a Dual Interactive Wasserstein Generative Adversarial Network optimized with Namib Beetle Optimization Algorithm is proposed for intrusion detection and preventing attacks in MANET. By utilizing the One Way Hash Chain Function, mobile users first register with the Trusted Authority. Each mobile user sends a finger vein biometric along with their user id, latitude, and longitude for authentication verification. The packet analyzer, feature extraction, preprocessing, and classification are the four parts that make up intrusion detection. To determine if any attack patterns have been identified, the packet analyzer is examined. This is executed using a Type 2 Fuzzy Controller that deems packet header information. Anisotropic diffusion Kuwahara filtering techniques is time series is taken into consideration in the preprocessing unit. The battle royal optimization algorithm is utilized in the feature extraction unit to acquire a better collection of features for packet categorization. The classification unit classifies the packets into five categories: DoS, Probe, U2R, R2L, and Anomaly using the proposed technique. Finally, the proposed method provides 26.26%, 15.57%, 32.9% higher accuracy, 33.06%, 23.82%, and 38.84% lesser delay analysed to the existing models.

Deep convolutional neural network optimized with hybrid marine predator’s and nomadic people optimization for cardiac arrhythmia classification using ECG signals

Electrocardiogram (ECG) accounts are generally utilized for analyzing and figure cardiovascular arrhythmias for examining the heart ailment. In this situation, medical professionals may need to review the ECG records over a longer period of time to diagnose cardiac arrhythmias. Heart arrhythmia is a regular indication of cardiovascular arrhythmia. Several deep learning models have been suggested to classify the Cardiac Arrhythmia. But, the existing methods do not reach adequate efficiency and increase the computational time. To overcome these issues, deep convolutional neural network optimized with hybrid marine predators and nomadic people optimization for cardiac arrhythmia classification using electrocardiogram signals is proposed in this manuscript. The input data is pre-processed using anisotropic diffusion Kuwahara filtering (ADKF) for removing noise, error, blur and histogram noises. Then Term frequency-inverse document frequency (TF-IDF) based feature extraction is utilized for feature extraction. Afterward, the extracting features are given to the Weibull Distributive Generalized Multidimensional Scaling (WDGMS) feature selection for features selection. Thus, hybrid Marine Predator’s Algorithm (MPA) and Nomadic people optimizer (NPO) is employed for optimizing the DCNN weight parameters. The proposed Deep CNN-Hyb (MPA-NPO)-AC method is implemented in python. Thus the proposed Deep CNN-Hyb(MPA-NPO)-AC method attains 14.03 %, 22.30 % and 16.35 % lower error rate; 3.32 %, 4.27 %, 5.39 % and 2.05 % greater AUC compared with existing methods, like time-series augmented signals with deep learning (SVM-CA-ECG), Linearly Adaptive Sine–Cosine Algorithm along application in Deep Neural Network for Feature Optimization in Arrhythmia Categorization utilizing ECG Signals (DNN-CA-ECG) and Categorization of normal sinus rhythm, abnormal arrhythmia and congestive heart fault ECG signals under LSTM with hybrid CNN-SVM deep neural network (CNN-CA-ECG) respectively.

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.

Edge detection techniques using nonlinear diffusion-based models

An overview of the edge detection techniques based on partial differential equations (PDE) is presented in this work. Nonlinear anisotropic diffusion-based boundary extraction approaches, like the influential Perona- Malik model and some improved variants of it, are described first. Anisotropic diffusion-based detection schemes using the mean curvature motion and nonlinear PDE-based approaches combining anisotropic diffusion to the bilateral filter are then discussed here. Some nonlinear reaction-diffusion-based edge detection methods are described next. Variational edge detection solutions using the total variation (TV) regularization or combining the anisotropic diffusion to the TV-based models are then presented. Directional diffusion-based image edge extraction algorithms are also discussed. Our own contributions in this computer vision domain are finally described.

A conservative network element method for diffusion-advection-reaction problems

We derive a conservative network element method for heterogeneous and anisotropic diffusion problems by modifying the non-conservative version, and extend it to the approximation of an additional advection term. The numerical scheme possesses the flux formulation reminiscent of classical finite volume methods. Its convergence is naturally governed by the network element theory. Numerical results illustrate the good behavior of the method even on distorted point clouds.

Significant coercivity enhancement of Ga-doped sintered Nd-Fe-B magnet by grain boundary diffusion perpendicular to the c-axis

Grain boundary diffusion (GBD) process is the most effective approach for enhancing coercivity of Nd-Fe-B magnets with minimal consumption of heavy rare earth (HRE). However, understanding of GBD mechanism is still limited, especially on the anisotropic diffusion behavior within sintered magnets. Grain boundary (GB) phase is the key factor in determining the diffusion process, which acts as diffusion channel for HRE atoms. The effects of doping Ga on the grain boundary microstructure and diffusion behavior of Tb in the sintered Nd-Fe-B magnet were investigated. Sintered magnets with different Ga content (0.15 wt%, 0.45 wt%) were prepared. It should be noted that Tb was diffused perpendicular to the easy axis (c-axis) of the magnets. The coercivity increased more significantly to 2.802 T (1.006 T higher than that of the post-sinter annealed state) in magnets with high Ga content than the low Ga content magnets (2.291 T, 0.669 T higher than the post-sintered annealed state). Such a huge coercivity increment almost reached the same value by diffusing Tb along the easy axis reported by other researchers. Chemical compositions and microstructure of the GB phase were found to be key factors in influencing the HRE diffusion process. A considerable amount of Nd6Fe13Ga phase was observed near triple junctions after post-sinter annealing in the high Ga content magnets, which markedly decreased Fe concentration in the intergranular phase. In addition, amorphous intergranular metallic Cu-rich phases provided smooth diffusion channels for HRE atoms. (Nd1-x, Tbx)2Fe14B shell was much more obvious in the high Ga content magnets. Ga dopping induced novel modifications in diffusion behavior of HRE atoms and strongly influenced the magnetic properties of permanent magnets. The present approach provide new path for the manufacture of high performance magnets, which is interesting both for technical applications and fundamental studies.

Enhancing Detection of Microcalcifications using FADHECAL for Early Stage Breast Cancer

Microcalcifications (MCCs) are reliable early signs of breast cancer. However, the small size of calcifications and low radiation factors used in digital mammograms cause low and poor quality mammogram images in detecting MCCs. This paper presents an image enhancement technique called Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) to enhance the details of MCCs in mammogram images by reducing the image noise while conserving contrast and brightness. A total of 23 mammogram images with MCCs were retrieved from the Mammographic Image Analysis Society’s database. The enhancement performance of FADHECAL was compared with Recursive Mean-Separate Histogram Equalization, Histogram Equalization and Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization. Image quality measurement tools of absolute mean brightness error (AMBE), structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used. The results showed that FADHECAL had the most superior results among other enhancement techniques, with 6.302 of AMBE, 20.453 of PSNR and 0.851 of SSIM. The proposed FADHECAL exhibited a high accuracy of 91.30% for the detection of MCCs. Hence, FADHECAL can be used as an ideal tool for identifying MCCs in early-stage breast cancer.