Analysis of Solar Images

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Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar f

Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares.

The idea of the magnetic complexity calculation model is based on the relationship between the energy stored in the magnetic fields of active regions and flares erupting from these regions. The magnetic complexity calculation model is derived from the Ising model. The Ising model is used for the analysis of magnetic interactions and structures of ferro- magnetic substances [ 12 ]. This model allows for the simpli- fication of complex interactions, since it has been success- fully employed in several areas of science. The Ising model has been applied to many physical systems such as: mag- netism, binary alloys, and the liquid-gas transition [ 17 ]. The model was also used in biology to model neural networks, flocking birds and beating heart cells [ 18 – 20 ]. Between 1969 and 1997, more than 12,000 papers were published using this model in different applications, which shows the importance and potential of this model [ 21 ]. For the first time, the Ising model has been modified and then applied to model the properties of magnetic fields formation in ac- tive regions and calculate the magnetic complexity of ac- tive regions. More details about the original Ising model and the first attempts of the modified model can be obtained in our previous publications [ 13 , 14 ]. However, further modi- fications have been applied to the model since the first at- tempts in modifying the Ising model. To avoid confusion, it is worth mentioning that the “magnetic complexity” have also been declared as the “energy” in our previous publica- tions. However, the new model imitates the magnetic config- urations in active regions, which are the key factor in flare occurrence, to provide more accurate results. The calculated values should provide a new way to indicate the flaring and non-flaring active regions, or even flare classifications. Also, the magnetic complexity calculation model has been applied to calculate the overall magnetic complexity on the solar disk. This will provide a measure of the overall magnetic activities on the front side of the Sun, and therefore can be an important indicator for flare occurrences in general.
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Comparative Fractal Analysis of 2013 November 5 Multiple Solar Eruptions with Fokker-Planck Equation Using Solar Dynamics Observatory Digital Images

Comparative Fractal Analysis of 2013 November 5 Multiple Solar Eruptions with Fokker-Planck Equation Using Solar Dynamics Observatory Digital Images

Community college pre-engineering students sometime need extra counseling on which career path such as professional engineers, research engineers, information technology engineer, etc. Hands-on experience gained in doing a research project in a laboratory and presenting the results in conferences would enhance motivation and improve retention. The Sun provides us with energy but its eruption effect on space weather has been observed to disrupt and damage power grids on Earth. NASA, NOAA, ESA etc has been funding spacecraft missions for solar observations. Digital solar images are available to users with access to standard, mass-market software such as ImageJ, Photoshop, etc. Many scientific projects utilize the Flexible Image Transport System (FITS) format, which requires specialized
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Evaluating the Performance of Automatic Sunspot Detection Algorithms using Full-Disk Solar Images

Evaluating the Performance of Automatic Sunspot Detection Algorithms using Full-Disk Solar Images

In accordance with the solar-terrestrial physics, and especially in geophysics, solar indices are of vital importance for evaluating the potential impact of solar activity on the Earth [5], as measured by indices of the geomagnetic field and/or ionospheric parameters [10]. One of the most widely used solar indices is the Wolf sunspot number that is based on the number of sunspots and sunspot groups. SIDC acts as data analysis service of the FAGS (Federation of Astronomical and Geophysical Data Analysis Services) broadcasting the daily, monthly, and yearly international sunspot numbers, with middle range predictions of vital importance for space weather services [9]. With that, they were reconstructed to 300 years back in time, and various periodicities facts have been found in these data. The most important usable things are the 11-year solar cycle and the 27-day Bartels solar rotation [3].
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Factor analysis of dynamic PET images

Factor analysis of dynamic PET images

2.3.1. SVD-based factor analysis The first generation is mainly based on apex-seeking and SVD for extraction of principal components or factors. Based on previous studies made on principal component analysis for quantitative evaluation on medical imaging [ Sch79 ; Mor90 ], Barber [ Bar80 ] was the first to effectively propose a matrix factorization-based analysis technique for gamma camera imaging. The main assumption of this method is that tissues are spatially homogeneous with respect to a given tracer and therefore a single TAC is able to characterize the variation of tracer concentration over time for all points within an organ. Moreover, while pure voxels of a tissue would present the most extreme values of their corresponding coefficient, overlapping voxels would be identified with coefficient values partitioned between each mixing factor. Indeed, Barber defines the coefficients in a voxel as summing to one and determines that they have to be positive so as to represent a physically realistic situation. This technique, referred to as factor analysis of dynamic structures (FADS), was further developed by Di Paola et al. [ Pao+82 ] and applied by Cavailloles et al. [ CBD84 ] for non-invasive gated cardiac studies under positivity constraints. Nijran and Barber [ NB85 ; NB86 ] highlighted the relevance of providing physiological a priori information on at least one of the factors to reduce the number of possible solutions to the problem. As an example, they used the differential equations from a three-compartment model to describe the tracer flow in the kidney, considered as the factor of interest. The impact of poor identification of factors was discussed in [ Hou84 ]. Houston [ Hou86 ] further addressed the identification of physiologically meaningful factors, by the use of set theory and clustering, while the work in [ Sam+87 ] tried to achieve the same goal by the use of rotation procedures. In a posterior work, Samal et al. [ Sam+87 ] investigated the ambiguous nature of general factor analysis problems applied to dynamic PET. In [ NB88 ], the relevance of constraints on providing physically meaningful factors for FADS approaches is studied. Nakamura et al. [ NSK89 ] evaluated the performance of a factor analysis method based on the maximum entropy principle in dynamic radionuclide images. In [ Dae+90 ], a background correction is implemented within factor analysis.
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MORPHOLOGICAL GRANULOMETRIC ANALYSIS OF SEDIMENT IMAGES

MORPHOLOGICAL GRANULOMETRIC ANALYSIS OF SEDIMENT IMAGES

We consider random grain models for both disjoint and non-disjoint grains. Each grain is modeled as nonnegative function on a compact domain. The domains are disjoint in the disjoint model and are allowed to intersect according to an intersection model in the non-disjoint model. Since weight-based size distributions currently used in sedimentology are based on individual grains, we apply the watershed transformation to segment intersecting grains (Meyer and Beucher, 1990, Beucher and Meyer, 1993). Finding good markers is essential for successful watershed application. Automatic methods devised to find markers are often specific to a given set of images. To avoid over segmentation, in our simulation the modeled grains have been hand marked.
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High-Profile Forensic Analysis of Images

High-Profile Forensic Analysis of Images

As described in the paper, we have verified that the feature which measures the correlations among the color channels (F 15 , in the paper’s nomenclature) is not effective for images that have undergone JPEG compression and resampling. In a difference from the original scenario presented in [7], which used machine learning techniques [8], we needed to ver- ify and give probability estimates for each capturing device. In this scenario, a purely statistical analysis was more appropriate. In order to calculate the pattern noise-signatures, we have con- sidered a scenario in which each captured paper file image, in each scanner, is replicated in different JPEG compression lev- els (70%, 75%, 85%). We have used different compression levels due to the several JPEG compressions found in the ob- ject in question (Table 1). We have discarded the borders for the analysis and considered only centered regions of 450 × 300 pixels.
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Journey of VCS from Black and White Images to Colored Images with their Performance Analysis

Journey of VCS from Black and White Images to Colored Images with their Performance Analysis

Visual Cryptography technology ,proposed in 1994 by Naor and Shamir[1],used the characteristics of human vision to decrypt the encrypted image. Their scheme acts as a building block of other VCS schemes. They hide the secret image in n distinct images called shares and the secret image can then simply be revealed by stacking together k shares. This is called as (k,n)-threshold scheme(threshold is k, which means secret image is visible if and only if any k transparencies are stacked together).Here each share looked like a collection of random pixels. Naor and Shamir analyzed the case of (k, n)- threshold VCS for black and white secret image. Its major features:
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Generation of Synthetic Solar Images with GANs

Generation of Synthetic Solar Images with GANs

This problem lies in the area of texture generation, two different main gen- erative models exist for these kind of problems: Generative Adversarial Networks and Variational Autoencoders. The GAN model was proposed by Ian J. Goodfellow et al. in 2014 [ 1 ] (see figure 2). They proposed a new framework for estimating generative models via an adversarial process. Two models are simultaneously trained: a generative model G that captures the data distribution, and a generative model D that estimates the probability that a sample came from the training data rather than G. This makes them capable of learning to mimic any distribution of data (image, audio, text, ...), as an example, a generator may learn the probability distribution of a big set of images, where each image represents a sample from an n-dimensional distribution and output completely new samples, similar to how one could ap- proximate the normal distribution that best fits a pair of 1-dimensional set of samples and generate new ones. Generator and discriminator train is defined as a minmax game with the following objective function (eq 1) the models converge when they reach the Nash equilibrium which is the optimal point for the given function:
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The place of solar power: an economic analysis of concentrated and distributed solar power

The place of solar power: an economic analysis of concentrated and distributed solar power

At an initial capital cost of $8.20 per watt, a 3.811 kilowatt system will have a total cost of $31,251. Deducting the 30% federal tax credit reduces the capital cost to $21,875. This cost is further reduced by the Cali- fornia Solar Initiative rebate, which reduces the cost to between $14,635 and $17,683 for the consumer. How- ever, since an array of this size will fully meet the annual needs of the consumer (after annual net meter- ing), the present value of 25 years of electricity bills must be considered. Given our constraining assumptions of a 6.7% annual increase in the price of electricity, a 7% discount rate, and a loss to generating capability of .9% per year, the present value of future electricity savings is $22,581. As these future savings are greater than the out-of-pocket costs to the consumer, installing such an array is a revenue-positive action on the part of the homeowner, earning him or her $4,897 to $7,946. After a single inverter replacement halfway through the 25- year lifetime of the array, this present value is reduced to $3,411 to $6,475. However, this consumer surplus came at a loss to federal and state governments of $13,567 to $16,616. This means each grid-neutral home creates a dead weight loss of $10,157 – Of course, this money does not evaporate, it goes to another agent, the photovoltaic array-producing firm. However, it is a loss to the system between consumers and the government.
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A Survey on Analysis of MRI Images of Brain

A Survey on Analysis of MRI Images of Brain

In image processing, image segmentation is the process of partitioning a digital image into multiple segments or sets of pixels, also known as superpixels. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s). When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes. [2]
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An Analysis of Colour Semantics in Art Images

An Analysis of Colour Semantics in Art Images

Different styles in art paintings are connected with the techniques em- ployed on one side and the artist’s aesthetic expression on the other. The process of forming an artist style is a very complicated one, where current fashionable painting styles, the social background and personal character of the artist play significant role. All these factors lead to forming some common trends in art movements and some specific features which distinguish one movement from an- other, one artist style from another, one artist period from another, etc. On the other hand the theme of the paintings also stamps specifics and can be taken into account. The compositions in different types of images (portraits, landscapes, town views, mythological and religious scenes, or everyday scenes) also set some rules, aesthetically imposed for some period.
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Analysis of Motion Velocity in Ultrasound Images

Analysis of Motion Velocity in Ultrasound Images

Typically, ultrasound images have many difficulties in image processing because of existence of high level of noise in the images [18]. Many traditional motion estimation techniques that have been introduced are based on the optical flow and block-matching constraint. In this paper, a simple algorithm using Hexagon-Diamond Displacement Point (HDDP) is designed to estimate the motion velocity displacement point in the ultrasound images sequence. The motion velocity displacement point will be used as reference point to examine the point at the area of interest. Besides that, Gaussian Filter is applied to HDDP algorithm for noise suppression purposes in the ultrasound images. This helps to improve the PSNR point’s performances. Furthermore, HDDP algorithm is suitable for image compression technique to remove the temporal redundancy between the frames for adjacent blocks in the ultrasound images.
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A REVIEW ON ?STUDY AND ANALYSIS OF HYPERSPECTRAL IMAGES?

A REVIEW ON ?STUDY AND ANALYSIS OF HYPERSPECTRAL IMAGES?

of information from the far object without coming it physical contact it is called Remote Sensing. With help of remote sensing it possible to detect the object which made up of different material having different chemical composition according to that they reflect, absorb & emit electromagnetic radiation .we can obtain spectral signature if we use the energy of radiation as function of wavelength it used to identify any material .The measurement & analysis of spectra it which obtained under the subject of spectroscopy. The imaging technology & spectroscopy this two technique are combine to cover large area to acquire detail information it known as imaging spectroscopy it also called as hyperspectral remote sensing. This is the new technique to detect vegetation, minerals.
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Mathematical Analysis of Solar Still With Storage

Mathematical Analysis of Solar Still With Storage

P.I. Cooper determines the maximum efficiency of single effect solar stills, the overall efficiency of a solar still is determined by the product of an efficiency of absorption or retention of radiation and an efficiency of utilization of this absorbed energy. An ideal still has been proposed and a set of curves presented which illustrate the maximum theoretical productivities which could be expected linear relationship has been derived which indicate that the maximum attainable ideal efficiency over a day’s operation will rarely exceed 60 percent.

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Frequency response analysis on solar cell

Frequency response analysis on solar cell

4.11 Equivalent Circuit of mono- Si Solar Cell with 12 V DC V Applied 42 4.12 Equivalent Circuit of p-Si Solar Cell with 12 V DC V Applied 42 4.13 of Real Part of Mono-Si vs. Poly-Si on 6 V DC Bias 44 4.14 Responses of Real Part of Mono-Si vs. Poly-Si on 12 V DC Bias 44

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Analysis of Solar Water Heating System

Analysis of Solar Water Heating System

Temperature gain with respect to time obtained from the FPLSC and FPLIFSC are plotted by experimental values in figure 4, 5&6, that the same graph plotted with CFD results in figure 7, 8&9 by various flow rate it can absorbed that the effect of fins on temperature distribution with influence of solar intensity, the finned material and more surface area helps the increase in temperature than the plain tube. The mass flow rate of 0.4Kg/min provides more outlet temperature than other because of the effect of flow rate on heat distribution through the raiser tube. It can be absorbed that the increase in temperature is to be considerably more with high solar intensity probably we got more output temperatures difference in between the plain and finned if its analyses under the mass flow rate of 0.4Kg/min for the experimental values, but in case of CFD result we got maximum temperature different at noon rather than the mass flow rate of 0.6Kg/min.
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Solar Array Payback Analysis Spreadsheet

Solar Array Payback Analysis Spreadsheet

TS Designs is an S-Corporation, which means its income, and thus its tax burden, is transferred to its owners (Tom Sineath and Eric Henry). Because the federal tax credit will be claimed by Tom and Eric, the AMT (alternative minimum tax, a tax affecting individual income) will affect how much can be claimed in any given year (which is why the credit is spread over several years in varying estimated amounts on our attached payback analysis spreadsheet).

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Crack Analysis in Silicon Solar Cells

Crack Analysis in Silicon Solar Cells

Therefore PL has been used to check the quality of an as-cut wafer before it enters to the solar cell fabrication process. Results show that grain boundaries are brighter for low quality multi-silicon wafer because of the gathering of impurities in the borders of the grains which means that high impurity density results in higher effective lifetimes. Dark spots represent bulk minority carrier lifetimes of <10 µs [53]. The response of the material depends on the temperature resulting in shift in the energy peak of the defect band and confirming the dependence of the silicon band gap to temperature; at room temperature the peak was in 0.77eV and as temperature was decreased band-to-band and defect band shifted to higher energies [33].
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Multifractal Analysis of Histopathological Tissue Images

Multifractal Analysis of Histopathological Tissue Images

The grading of biopsy samples is essentially based on the deviation of cell structures from the normal tissue. Cell structures also have multifractal characteristics that could be directly used for identifying pathological conditions. Therefore, it would be useful to explore the relationship between various multifractal measures of cell structures in tissues and the corresponding pleomorphic scores assigned by pathologists. This paper proposes an approach using local intensity variations in images to identify mitotic cells and also to obtain an estimate of the NP and TF scores based on the multifractal spectra computed from the images.
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Relationship between electroluminescence images and power type parameters of defective silicon solar cells

Relationship between electroluminescence images and power type parameters of defective silicon solar cells

Now, there are three methods for solar cells defect detection in industrial productions, which include artificial visual detection [1], the infrared detection [2] and CCD detection [3]. Infrared image technology was used to the battery components for defect detection, which could achieve four kind of defect detection, such as fragments, virtual welding, broken gate and cracked through the analysis and processing components of electroluminescent image [4]. A detection method was proposed based on cell physical defects, and applied for a patent on crack detection equipment [5]. This equipment was mainly composed of laser source and the laser controller, rotating mirror and controller, reflector, a CCD camera, scanner, cargo and computer composition, it could detect the defect of battery components through the analysis of the infrared image of processing scanning for. But most of the testing equipment only measured single index, and need many even repeat test equipment, it is not conducive to the rapid detection of solar cells defects. As the image of defect detection and power parameters detection equipment integration, the efficiency of cell production testing would improve greatly.
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