Non-uniform Intensity Research Articles

Degradation Behavior and Trap Analysis Based on Low-Frequency Noise of AlGaN/GaN HEMTs Subjected to Radio Frequency Overdrive Stress

In this article, the degradation behavior of the electrical characteristics of AlGaN/GaN HEMTs subjected to radio frequency (RF) overdrive stress has been studied. Trap analysis based on low-frequency noise (LFN) and electroluminescence (EL) characterizations was carried out for AlGaN/GaN HEMTs before and after RF stress. The experimental results show that drain-to-source current of AlGaN/GaN HEMTs after RF stress is obviously lower than that of the fresh devices. The maximum change of drain-to-source current was up to 79 mA in the saturation zone. The threshold voltage drifted forward and gate-lag characteristic became worse for the AlGaN/GaN HEMTs after RF stress. The EL characterizations also showed the nonuniformity of channel illumination intensity, which indicates the distribution of electron of the channel nonuniform. Trap analysis was performed by using the LFN method, and the LFN results showed that the trap density increased by about one order of magnitude in the AlGaN/GaN HEMTs after RF stress, which would lead to more depletion of the channel two-dimensional electron gas (2DEG) electrons. The mechanism of increasing trap density could be attributed to defects induced by RF stress at AlGaN surface and AlGaN barrier layer. The experimental results may offer a useful reference in the design and application of RF of AlGaN/GaN HEMTs.

Spatially adaptive hybrid variational model for temperature-dependent nonuniformity correction of infrared images

The restoration of nonuniform distorted infrared (IR) images is crucial for human visual perception and subsequent application tasks. However, existing methods sometimes fail to yield visually natural decompositions and perform insufficiently in the preservation of meaningful structures while suppressing disturbing noise. A spatially adaptive hybrid l1 − l2 variational framework for the nonuniform intensity correction of IR images is proposed. Considering the piecewise constant characteristics of latent images, a weighted l1-norm regularization method is developed to constrain the local affinity of neighborhood pixels according to their intensity and structural priors, thereby significantly preserving structures while smoothly flattening areas. Additionally, an l2-norm guided local smoothness constraint is incorporated with an absolute scale term provided by coarse estimation to characterize the bias field component to restrict potential solutions and enforce the bias component to be textureless. Moreover, the proposed l1 − l2 model is efficiently solved by an alternating direction method of multipliers scheme. Extensive experiments on both synthesized images and two real-world IR datasets indicate that the performance of the proposed method is superior to that of five existing algorithms both visually and numerically.

Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method

Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer-aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non-uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C-means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel-induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network

Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU can highly degrade the performance of automatic quantitative analysis such as segmentation, registration, feature extraction and radiomics. In this study, we present an advanced deep learning based INU correction algorithm called residual cycle generative adversarial network (res-cycle GAN), which integrates the residual block concept into a cycle-consistent GAN (cycle-GAN). In cycle-GAN, an inverse transformation was implemented between the INU uncorrected and corrected magnetic resonance imaging (MRI) images to constrain the model through forcing the calculation of both an INU corrected MRI and a synthetic corrected MRI. A fully convolution neural network integrating residual blocks was applied in the generator of cycle-GAN to enhance end-to-end raw MRI to INU corrected MRI transformation. A cohort of 55 abdominal patients with T1-weighted MR INU images and their corrections with a clinically established and commonly used method, namely, N4ITK were used as a pair to evaluate the proposed res-cycle GAN based INU correction algorithm. Quantitatively comparisons of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were made among the proposed method and other approaches. Our res-cycle GAN based method achieved an NMAE of 0.011 ± 0.002, a PSNR of 28.0 ± 1.9 dB, an NCC of 0.970 ± 0.017, and a SNU of 0.298 ± 0.085. Our proposed method has significant improvements (p < 0.05) in NMAE, PSNR, NCC and SNU over other algorithms including conventional GAN and U-net. Once the model is well trained, our approach can automatically generate the corrected MR images in a few minutes, eliminating the need for manual setting of parameters.

A Dynamic Particle Swarm Optimisation and Fuzzy Clustering Means Algorithm for Segmentation of Multimodal Brain Magnetic Resonance Image Data

Fuzzy Clustering Means (FCM) algorithm is a widely used clustering method in image segmentation, but it often falls into local minimum and is quite sensitive to initial values which are random in most cases. In this work, we consider the extension to FCM to multimodal data improved by a Dynamic Particle Swarm Optimization (DPSO) algorithm which by construction incorporates local and global optimization capabilities. Image segmentation of three-variate MRI brain data is achieved using FCM-3 and DPSOFCM-3 where the three modalities T1-weighted, T2-weighted and Proton Density (PD), are treated at once (the suffix -3 is added to distinguish our three-variate method from mono-variate methods usually using T1-weighted modality). FCM-3 and DPSOFCM-3 were evaluated on several Magnetic Resonance (MR) brain images corrupted by different levels of noise and intensity non-uniformity. By means of various performance criteria, our results show that the proposed method substantially improves segmentation results. For noisiest and most no-uniform images, the performance improved as much as 9% with respect to other methods

MHD Stagnation Point Flow over a Nonlinear Stretching/Shrinking Sheet in Nanofluids

In this study, an investigation of the steady 2-D magnetohydrodynamiic (MHD) flow of stagnation point past a nonlinear sheet of stretching/shrinking within of a non-uniform transverse magnetic intensity in nanofluids had been analysed. Considered material of nanoparticles such as copper (Cu) in water base fluid with Pr = 6.2 to analyze the influence of volume fraction parameter of nanoparticles and the stretching/shrinking sheet parameter. The governing nonlinear partial differential equations (PDEs) are converted in to the nonlinear ordinary differential equations (ODEs) and use the boundary value problem solver bvp4c in Matlab program to solve numerically through the use of a similarity transformation. The impact of the parameter of the magnetic field on the coefficient of skin friction, the local number of Nusselt and the profiles of velocity and temperature are portrayed and explained physically. The analysis reveals that the magnetic field and volume fraction of nanoparticles affect the velocity and temperature. The dual solutions are achieved where for the shrinking sheet case and the solutions are non-unique, different from a stretching sheet.

Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method

Background and ObjectiveBrain tumor segmentation is a challenging issue due to noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual MRI segmentation is a very tedious, time-consuming, and user-dependent task. This paper aims to presents a novel level set method to address aforementioned challenges for reliable and automatic brain tumor segmentation. MethodsIn the proposed method, a new functional, based on level set method, is presented for medical image segmentation. Firstly, we define a superpixel fuzzy clustering objective function. To create superpixel regions, multiscale morphological gradient reconstruction (MMGR) operation is used. Secondly, a novel fuzzy energy functional is defined based on superpixel segmentation and histogram computation. Then, level set equations are obtained by using gradient descent method. Finally, we solve the level set equations by using lattice Boltzmann method (LBM). To evaluate the performance of the proposed method, both synthetic image dataset and real Glioma brain tumor images from BraTS 2017 dataset are used. ResultsExperiments indicate that our proposed method is robust to noise, initialization, and intensity non-uniformity. Moreover, it is faster and more accurate than other state-of-the-art segmentation methods with the averages of running time is 3.25 seconds, Dice and Jaccard coefficients for automatic tumor segmentation against ground truth are 0.93 and 0.87, respectively. The mean value of Hausdorff distance, Mean absolute Distance (MAD), accuracy, sensitivity, and specificity are 2.70, 0.005, 0.9940, 0.9183, and 0.9972, respectively. ConclusionsOur proposed method shows satisfactory results for Glioma brain tumor segmentation due to superpixel fuzzy clustering accurate segmentation results. Moreover, our method is fast and robust to noise, initialization, and intensity non-uniformity. Since most of the medical images suffer from these problems, the proposed method can more effective for complicated medical image segmentation.

FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology.

Slide-free digital pathology techniques, including nondestructive 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. In order to facilitate clinical adoption of these fluorescence-based techniques, software methods have been developed to convert grayscale fluorescence images into color images that mimic the appearance of standard absorptive chromogens such as hematoxylin and eosin (H&E). However, these false-coloring algorithms often require manual and iterative adjustment of parameters, with results that can be inconsistent in the presence of intensity nonuniformities within an image and/or between specimens (intra- and inter-specimen variability). Here, we present an open-source (Python-based) rapid intensity-leveling and digital-staining package that is specifically designed to render two-channel fluorescence images (i.e. a fluorescent analog of H&E) to the traditional H&E color space for 2D and 3D microscopy datasets. However, this method can be easily tailored for other false-coloring needs. Our package offers (1) automated and uniform false coloring in spite of uneven staining within a large thick specimen, (2) consistent color-space representations that are robust to variations in staining and imaging conditions between different specimens, and (3) GPU-accelerated data processing to allow these methods to scale to large datasets. We demonstrate this platform by generating H&E-like images from cleared tissues that are fluorescently imaged in 3D with open-top light-sheet (OTLS) microscopy, and quantitatively characterizing the results in comparison to traditional slide-based H&E histology.

Spatial structure of lasing modes in wave-chaotic semiconductor microcavities

We present experimental and numerical studies of broad-area semiconductor lasers with chaotic ray dynamics. The emission intensity distributions at the cavity boundaries are measured and compared to ray tracing simulations and numerical calculations of the passive cavity modes. We study two different cavity geometries, a D-cavity and a stadium, both of which feature fully chaotic ray dynamics. While the far-field distributions exhibit fairly homogeneous emission in all directions, the emission intensity distributions at the cavity boundary are highly inhomogeneous, reflecting the non-uniform intensity distributions inside the cavities. The excellent agreement between experiments and simulations demonstrates that the intensity distributions of wave-chaotic semiconductor lasers are primarily determined by the cavity geometry. This is in contrast to conventional Fabry–Perot broad-area lasers for which the intensity distributions are to a large degree determined by the nonlinear interaction of the lasing modes with the semiconductor gain medium.

A method to compute mixed‐mode stress intensity factors for nonplanar cracks in three dimensions

SummaryMethods to compute the stress intensity factors along a three‐dimensional (3D) crack front often display a tenuous rate of convergence under mesh refinement or, worse, do not converge, particularly when applied on unstructured meshes. In this work, we propose an alternative formulation of the interaction integral functional and a method to compute stress intensity factors along the crack front which can be shown to converge. The novelty of our method is the decoupling of the two discretizations: the bulk mesh for the finite element solution and the mesh along the crack front for the numerical stress intensity factors, and hence we term it the multiple mesh interaction integral (MMII) method. Through analysis of the convergence of the functional and method, we find scalings of these two mesh sizes to guarantee convergence of the computed stress intensity factors in a variety of norms, including maximum pointwise error and total variation. We demonstrate the MMII on four examples: a semiinfinite straight crack with the asymptotic displacement fields, the same geometry with a nonuniform stress intensity factor along the crack front, a spherical cap crack in a cylinder under tension, and the elliptical crack under far‐field tension and shear.

A Cooperative Approach Based on Local Detection of Similarities and Discontinuities for Brain MR Images Segmentation.

This paper introduces a new cooperative multi-agent approach for segmenting brain Magnetic Resonance Images (MRIs). MRIs are manually processed by human radiology experts for the identification of many diseases and the monitoring of their evolution. However, such a task is time-consuming and depends on expert decision, which can be affected by many factors. Therefore, various types of research were and are still conducted to automate MRI processing, mainly MRI segmentation. The approach presented in this paper, without any parametrization or prior knowledge, uses a set of situated agents, locally interacting to segment images according to two main phases: the detection of discontinuities and the detection of similarities. An implementation of this approach was tested on phantom brain MR images to assess the results and prove its efficiency. Experimental results ensure a minimum of 89% Dice coefficient with increasing values of the noise and the intensity non-uniformity.

Object Detection in Sonar Images

The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems along the underwater object detection pipeline. A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images. The system shows an overall detection and classification performance average precision (AP) score of 0.98392 for a test set of 5000 underwater sonar frames.

Microarrays Incorporating Gold Grid Patterns for Protein Quantification.

Protein microarrays are miniaturized two-dimensional arrays, incorporating thousands of immobilized proteins, typically printed in minute amounts on functionalized solid substrates, which can be analyzed in a high-throughput fashion. Irreproducibility of the printing techniques adopted, resulting in inconsistently and nonuniformly deposited microscopic spots, nonuniform signal intensities from the printed microspots, and significantly high background noise are some of the critical issues that affect protein analysis using traditional protein microarrays. To overcome such issues, in this study, we introduced a novel gold grid pattern-based protein microarray. The grid patterns incorporated in our microarray are equivalent to the spots used for protein analysis in conventional protein microarrays. We utilized the signal intensities from the grid patterns acting as spots for quantifying the protein concentration levels. To demonstrate the utility of our novel design concept, we quantified as low as 66.7 ng/mL of bovine serum albumin using our gold grid pattern-based protein microarray. Our grid pattern-based design concept for protein quantification overcame the signal nonuniformity issues and ensured that the dominance of any distorted signal from a single spot did not affect the overall protein quantification results as encountered in conventional protein microarrays.

On a universal solution to the transport-of-intensity equation.

The transport-of-intensity equation (TIE) is one of the most well-known approaches for phase retrieval and quantitative phase imaging. It directly recovers the quantitative phase distribution of an optical field by through-focus intensity measurements in a non-interferometric, deterministic manner. Nevertheless, the accuracy and validity of state-of-the-art TIE solvers depend on restrictive pre-knowledge or assumptions, including appropriate boundary conditions, a well-defined closed region, and quasi-uniform in-focus intensity distribution, which, however, cannot be strictly satisfied simultaneously under practical experimental conditions. In this Letter, we propose a universal solution to TIE with the advantages of high accuracy, convergence guarantee, applicability to arbitrarily shaped regions, and simplified implementation and computation. With the "maximum intensity assumption," we first simplify TIE as a standard Poisson equation to get an initial guess of the solution. Then the initial solution is further refined iteratively by solving the same Poisson equation, and thus the instability associated with the division by zero/small intensity values and large intensity variations can be effectively bypassed. Simulations and experiments with arbitrary phase, arbitrary aperture shapes, and nonuniform intensity distributions verify the effectiveness and universality of the proposed method.

A 130 kWe solar simulator with tunable ultra-high flux and characterization using direct multiple lamps mapping

The indoor solar simulator is an excellent supply device for high solar flux applications, but usually suffers from the low flux under a large light area and unadjustable light distribution. In this work, a 130 kWe indoor solar simulator with tunable ultra-high flux in a projection area of 200 mm diameter is designed and established. 13 of 10 kWe xenon short-arc lamps and reflectors are coupled to make the flux be concentrated to a spot. Besides, a direct measure system consisting of a Gardon gauge and a two-dimensional moving unit is proposed to map the flux distribution. The expanded uncertainty is 7.46% with a converge factor of 2, compared with the conventional indirect camera-based method with an error up to 50%. Up to 7 lamps are simultaneously measured. The Monte Carlo method is adopted to analyze flux distributions of individual lamps as well as the whole lamp group, which coincides well with the experimental data. When the input electric power is 11.12 kW, the electric-to-light conversion efficiency is 31.60% ± 2.36% on the focus plane. The peak flux reaches 11.267 ± 1.571 MW/m2 and a mean flux amounts to 1.054 ± 0.079 MW/m2 in a projection area of 200 mm diameter. Both the distribution and non-uniformity of irradiation flux are tunable. By reducing the power of the central lamp to the half and defocusing the target, the distribution non-uniformity of the light intensity can be remarkably reduced from 68.87% to 18.59% in a projection area of 60 mm diameter. The developed device will provide a reliable source for indoor experiments.

Windowed Fourier transform and cross-correlation algorithms for molecular tagging velocimetry

Simulated and experimental molecular tagging velocimetry (MTV) images have been analyzed with a technique commonly used to process grid images on surfaces, the windowed Fourier transform with local spectrum analysis (WFT-LSA). A systematic synthetic image study of the modulation transfer function (MTF) and error tendencies of the WFT-LSA was performed and compared with a PIV-style cross-correlation algorithm to see if advanced strategies such as iterative image deformation can improve analysis of gridded images with high noise levels. Testing of single-pass algorithms showed that in typical MTV images, the WFT-LSA yields significantly lower bias errors than cross-correlation (CC) at displacements greater than 1 pixel but slightly higher random error at all displacements and image conditions. Analysis of the MTF shows that CC provided better resolution of spatial fluctuations than the WFT-LSA in many combinations of grid size and interrogation window. Tests of image deformation algorithms showed that the gap in performance between CC and WFT-LSA is maintained even as both methods improve. Additionally, WFT-LSA and CC methods are applied to real MTV experiments in high-speed gas jet flows. A preliminary analysis of phosphorescence lifetime provided by acetone vapor excited at 266 nm is used for assessing the required gas speeds for making MTV application feasible. The application of WFT-LSA to real MTV images demonstrates the ability of the algorithm to handle further real-world effects that could not be considered in the synthetic image analysis, like reduced signal-to-noise ratio and non-uniform intensity of the tagging grid across the image introduced by the actual laser beam energy distribution. With experimental images, CC is more accurate with shear flows but less robust to high noise levels than WFT-LSA, as predicted by the synthetic image analysis.

Small scale optimization in crystalline silicon solar cell on efficiency enhancement of low-concentrating photovoltaic cell

Compared with conventional silicon solar cell, concentrating photovoltaic cell has a higher incident illumination intensity and non-uniform illumination intensity distribution. There is a close relationship between the illumination intensity and the resistance and shading losses of front metal fingers. In this paper, according to the non-uniformity of the illumination distribution formed by the concentrator, the effects of different finger numbers and spacing on the performance of low-concentrating photovoltaic cells were studied. First, low-concentrating photovoltaic cell modules with different finger numbers and spacing were established, and efficiency of these modules was compared to determine the optimal finger numbers and spacing of the front metal fingers. Then, effects of the shading losses, finger resistance and lateral spreading resistance on the performance of the low-concentrating photovoltaic cell were analyzed. Results show that for the specific illumination intensity distribution of the low-concentrating photovoltaic cell when the numbers and spacing of the front metal fingers are optimized, efficiency of the low-concentrating photovoltaic cell can be increased from 13.805% to 13.837%. Additionally, it can be concluded that the optimization of the number and spacing of the front metal fingers in the crystalline silicon solar cell is an efficient way to improve the electrical performance of the concentrating photovoltaic cell.

Global context and boundary structure-guided network for cross-modal organ segmentation

In multi-modal medical images such as X-ray and CT, automated organ segmentation is often disturbed by the frequent existence of shape variation, intensity non-uniformities and blurs. Introducing supplementary information helps with eliminating the negative effects of these factors. Medical priors are often leveraged to supplement for specific tasks in previous works, but the specificity of priors hinders these methods from generalizing to cross-modal problems. In this paper we propose Global Context and Boundary Structure-guided Network (GCBSN), which utilizes global context and boundary structure to assist cross-modal organ segmentation. Specifically, we innovatively employ the global context from all spatial regions to guide our deformable convolution. Therefore, more suitable receptive fields can be generated for irregular-shaped targets. Also, we extract the global context contained in each class from the coarse segmentation to assist classifying the areas with non-uniform intensity. Moreover, we design a novel loss that weights more on the errors of positions nearby organ boundaries, and this loss function can avoid errors brought by border blurs. The cross-modal performance of GCBSN is evaluated on two datasets of different modals, i.e., X-Ray images and CT slices. On the 3D NIH Pancreas Dataset, GCBSN outperforms the baseline by 8.72% in terms of the dice coefficient (DC). On the 2D Japanese Society of Radiological Technology Dataset, the mean DC of GCBSN is 98.07% for lung and 94.91% for heart and it surpasses other state-of-the-art methods.

A 3D white referencing method for soybean leaves based on fusion of hyperspectral images and 3D point clouds

In recent years, plant phenotyping technologies have been widely applied to evaluate complex plant traits such as morphology, physiology, ecology, biochemistry, tolerance, growth and yield. Hyperspectral/multispectral cameras, artificial lighting sources, mechanisms and computers together capture images of different species of plants. Due to the non-uniform intensity of lighting sources in different wavelengths, raw images need to be calibrated using white references. Flat white panels are typically scanned as a white reference. However, geometrical factors such as leaf tilt angles cannot be calibrated by flat white references. In this publication, the effectiveness of using angled white reference to calibrate corresponding raw images was first demonstrated. Furthermore, a 3D white referencing library integrating different angles and spatial positions in the system of a hyperspectral camera and a Kinect V2 depth sensor was created. Thus, a pixel on the leaf surface can be calibrated by a point with the nearest tilt angle and spatial position in the 3D referencing library. The validating samples for this referencing library were soybean leaves grown in a greenhouse. The results showed that the reflectance spectra after 3D calibration were closer to the standard calibration (flat leaf calibrated by flat white reference) than the conventional flat white referencing calibration. Furthermore, the pixel-level normalized difference vegetation index (NDVI) distribution over the soybean leaf surface after 3D calibration was also closer to the standard calibration. This proposed 3D white referencing method had the potential to improve calibration quality of plant images. Integrating with LiDAR sensors, this new approach has an opportunity to be applied in field environments.

Uniform intensity in multifocal microscopy using a spatial light modulator.

Multifocal microscopy (MFM) offers high-speed three-dimensional imaging through the simultaneous image capture from multiple focal planes. Conventional MFM systems use a fabricated grating in the emission path for a single emission wavelength band and one set of focal plane separations. While a Spatial Light Modulator (SLM) can add more flexibility as a replacement to the fabricated grating, the relatively small number of pixels in the SLM chip, cross-talk between the pixels, and aberrations in the imaging system can produce non-uniform intensity in the different axially separated image planes. We present an in situ iterative SLM calibration algorithm that overcomes these optical- and hardware-related limitations to deliver near-uniform intensity across all focal planes. Using immobilized gold nanoparticles under darkfield illumination, we demonstrate superior intensity evenness compared to current methods. We also demonstrate applicability across emission wavelengths, axial plane separations, imaging modalities, SLM settings, and different SLM manufacturers. Therefore, our microscope design and algorithms provide an alternative to the use of fabricated gratings in MFM, as they are relatively simple and could find broad applications in the wider research community.