Shape Of Histogram Research Articles

Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos.

This paper presents a novel candidate generation algorithm for pedestrian detection in infrared surveillance videos. The proposed method uses a combination of histogram specification and iterative histogram partitioning to progressively adjust the dynamic range and efficiently suppress the background of each video frame. This pairing eliminates the general-purpose nature associated with histogram partitioning where chosen thresholds, although reasonable, are usually not suitable for specific purposes. Moreover, as the initial threshold value chosen by histogram partitioning is sensitive to the shape of the histogram, specifying a uniformly distributed histogram before initial partitioning provides a stable histogram shape. This ensures that pedestrians are present in the image at the convergence point of the algorithm. The performance of the method is tested using four publicly available thermal datasets. Experiments were performed with images from four publicly available databases. The results show the improvement of the proposed method over thresholding with minimum-cross entropy, the robustness across images acquired under different conditions, and the comparable results with other methods in the literature.

Processing Non-Gaussian Data Residuals in Geomagnetism

Some time ago, we considered non-Gaussian shapes of histograms of quantities that were related to residuals in data: we showed at a qualitative level that non-Gaussianity is most likely the result of mixing of Gaussian distributions. In this addendum, we argue that there is a quantitative description that can be used in fairly general situations. Briefly, we present here the same magnetic measurement data that were reported in the original publication: Khokhlov, A.; Hulot, G. On the cause of the non-Gaussian distribution of residuals in geomagnetism. Geophys. J. Int. 2017, 209, 1036–1047.

Luminance Level of Histogram-Based Scene-Change Detection for Frame Rate Up-Conversion

Scene change detection is an essential process of frame rate up-conversion (FRUC). The performance of FRUC highly dependents on the accuracy of scene change detection. This paper proposes a new scene-change detection method that uses analysis of luminance level of the histograms for FRUC. The histogram luminance level refers to the statistical average luminance value obtained from the generated histograms for each region. Existing histogram-based scene change methods calculate the difference between optimal threshold values using an automatic thresholding technique or extract the difference between the histogram shape to detect the scene change. The automatic thresholding method uses iterative operations—the difference between the histogram shape is simply a method of calculating the luminance difference for the current and previous frames. Thus, it requires many computational resources and incorrectly detects a scene change because calculating the histogram shape cannot reflect regional image characteristics. The proposed method addresses these problems using histogram luminance levels for each region in the given frames. It calculates the level differences between the previous and current frames to detect the initial scene change regions. Moreover, the proposed method refines the initial scene change regions by analyzing the distribution of surrounding detected regions and uses refinement to enhance scene-change detection accuracy. In the experimental results, the proposed method increased the average F1 score to 0.4816 (a 122.51% improvement) compared with the benchmark methods. The average computation time per pixel of the proposed method also decreased to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.5323 \,\,\mu \text{s}$ </tex-math></inline-formula> (a 87.06% reduction) compared with the benchmark methods.

Obstacle Avoidance and Environmental Adaptability Analysis of Snake-like Robot Based on Deep Learning

Aiming at the problems of high complexity and low accuracy of visual depth map feature recognition, a graph recognition algorithm based on principal component direction depth gradient histogram (pca-hodg) is designed in this study. In order to obtain high-quality depth map, it is necessary to calculate the parallax of the visual image. At the same time, in order to obtain the quantized regional shape histogram, it is necessary to carry out edge detection and gradient calculation on the depth map, then reduce the dimension of the depth map combined with the principal component, and use the sliding window detection method to reduce the dimension again to realize the feature extraction of the depth map. The results show that compared with other algorithms, the pca-hodg algorithm designed in this study improves the average classification accuracy and significantly reduces the average running time. This shows that the algorithm can reduce the running time by reducing the dimension, extract the depth map features more accurately, and has good robustness.

On the shape of timings distributions in free-text keystroke dynamics profiles

Keystroke dynamics is a soft biometric trait. Although the shape of the timing distributions in keystroke dynamics profiles is a central element for the accurate modeling of the behavioral patterns of the user, a simplified approach has been to presuppose normality. Careful consideration of the individual shapes for the timing models could lead to improvements in the error rates of current methods or possibly inspire new ones. The main objective of this study is to compare several heavy-tailed and positively skewed candidate distributions in order to rank them according to their merit for fitting timing histograms in keystroke dynamics profiles. Results are summarized in three ways: counting how many times each candidate distribution provides the best fit and ranking them in order of success, measuring average information content, and ranking candidate distributions according to the frequency of hypothesis rejection with an Anderson-Darling goodness of fit test. Seven distributions with two parameters and seven with three were evaluated against three publicly available free-text keystroke dynamics datasets. The results confirm the established use in the research community of the log-normal distribution, in its two- and three-parameter variations, as excellent choices for modeling the shape of timings histograms in keystroke dynamics profiles. However, the log-logistic distribution emerges as a clear winner among all two- and three-parameter candidates, consistently surpassing the log-normal and all the rest under the three evaluation criteria for both hold and flight times.

Quantification of Sub-Solar Star Ages from the Symmetry of Conjugate Histograms of Spin Period and Angular Velocity

Empirical laws proposed for the decline in star spin with time have heretofore been tested using ambiguous fitting models. We develop an analytical inverse model that uses histogram data to unequivocally determine the physical law governing how dwarf star spin depends on time (t) and mass (M). We analyze shapes of paired histograms of axial rotation period (П) and angular velocity (ω = 2π/П) to utilize the fact that a variable and its reciprocal are governed by the same physics. Copious data on open clusters are used to test the formula ∂ω/∂t ∝ − ωn where n is unrestricted, and thus covers diverse possibilities. Histogram conjugates for each of 15 clusters with 120 to 812 measurements provide n = 1.13 ± 0.19. Results are independent of initial spin rate, bin size, cluster parameters, and star mass. Notably, 11 large clusters with mostly M-types yield fits with n = 1.07 ± 0.12. Associations behave similarly. Only exponential decay (n = 1) explains the similar shapes of the conjugate histograms for the spin period and angular velocity, despite the asymmetric (inverse) relationship of these variables. This rate law is consistent with viscous dissipation. Forward modeling confirms that n is near unity and further shows that coeval formation of all stars in a cluster does not occur. We therefore explore a constant rate of star production, which is reasonable for tiny stars. Inverse models show that episodic production increases with mass, but is unimportant below ~0.55 MSun. We infer star and cluster ages, and find that star production becomes less regular with time, as interstellar gas and dust are progressively depleted. Our new analytical approach of extracting a physical law from conjugate histograms is general and widely applicable.

On the Shape of Timing Distributions in Free-Text Keystroke Dynamics Profiles

Keystroke dynamics is a soft biometric trait. Although the shape of the timing distributions in keystroke dynamics profiles is a central element for the accurate modeling of the behavioral patterns of the user, a simplified approach has been to presuppose normality. Careful consideration of the individual shapes for the timing models could lead to improvements in the error rates of current methods or possibly inspire new ones. The main objective of this study is to compare several heavy-tailed and positively skewed candidate distributions in order to rank them according to their merit for fitting timing histograms in keystroke dynamics profiles. Results are summarized in three ways: counting how many times each candidate distribution provides the best fit and ranking them in order of success, measuring average information content, and ranking candidate distributions according to the frequency of hypothesis rejection with an Anderson-Darling goodness of fit test. Seven distributions with two parameters and seven with three were evaluated against three publicly available free-text keystroke dynamics datasets. The results confirm the established use in the research community of the log-normal distribution, in its two- and three-parameter variations, as excellent choices for modeling the shape of timings histograms in keystroke dynamics profiles. However, the log-logistic distribution emerges as a clear winner among all two- and three-parameter candidates, consistently surpassing the log-normal and all the rest under the three evaluation criteria for both hold and flight times.

Determining Compositional Variation in Silicon-Metal Alloys by Parsing SEM/EDS Hyperspectral Images.

High-temperature differential scanning calorimetry was used to understand the thermal properties of Si-rich metal–silicon alloys. Insoluble metals (A and B) were found to produce an alloy with discrete ASi2 and BSi2 dispersed phases. In contrast, metals that form a solid solution result in a dispersed phase that has a composition of AxB1−xSi2, where x varies continuously across each inclusion. This complex composition distribution is putatively caused by differences in the solidification temperatures of ASi2 versus BSi2. Though this behavior was observed for several different combinations of metals, we focus here specifically on the Cr/V/Si system. To better understand the range and most probable element concentrations in the dispersed silicide domains, a method was devised to generate histograms of their Cr and V concentrations from energy-dispersive X-ray spectroscopy hyperspectral images. Varying the Cr/V/Si ratio was found to change the shape of the element histograms, indicating that the distribution of silicide compositions that form is controlled by the input composition. Adding aluminum was found to result in dispersed phases that had a single composition rather than a range of Cr and V concentrations. This demonstrates that aluminum can be an effective additive for altering solidification kinetics in silicon alloys.

FET-PET radiomics in recurrent glioblastoma: prognostic value for outcome after re-irradiation?

PurposeThe value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-positron emission tomography (PET)-radiomics in the outcome assessment of patients with recurrent glioblastoma (rGBM) has not been evaluated until now. The aim of this study was to evaluate whether a prognostic model based on FET-PET radiomics features (RF) is feasible and can identify rGBM patients that would most benefit from re-irradiation.MethodsWe prospectively recruited rGBM patients who underwent FET-PET before re-irradiation (GLIAA-Pilot trial, DRKS00000633). Tumor volume was delineated using a semi-automatic method with a threshold of 1.8 times the standardized-uptake-value of the background. 135 FET-RF (histogram parameters, shape and texture features) were extracted. The analysis involved the characterization of tumor and non-tumor tissue with FET-RF and the evaluation of the prognostic value of FET-RF for time-to-progression (TTP), overall survival (OS) and recurrence location (RL).ResultsThirty-two rGBM patients constituted our cohort. FET-RF discriminated significantly between tumor and non-tumor. The texture feature Small-Zone-Low-Gray-Level-Emphasis (SZLGE) showed the best performance for the prediction of TTP (p = 0.001, satisfying Bonferroni-multiple-test significance level). Additionally, two radiomics signatures could predict TTP (TTP-radiomics-signature, p = 0.001) and OS (OS-radiomics-signature, p = 0.038). SZLGE and the TTP-radiomics-signature additionally predicted RL. Specifically, high values for TTP-radiomics-signature and for SZLGE indicated not only earlier progression, but also a RL within the initial FET-PET active volume.ConclusionOur findings suggest that FET-PET radiomics could contribute to the prognostic assessment and selection of rGBM-patients benefiting from re-irradiation.Trial registration DRKS00000633. Registered on 8th of December in 2010.https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00000633.

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

Abstract. The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

Extraction of Trap Energy Distribution in Proton-Irradiated Focal Plane Arrays Using a High-Precision Random Telegraph Noise Detection Algorithm

Random telegraph noise (RTN) is a type of low-frequency noise that degrades the sensitivity of focal plane arrays (FPAs) and other electronics. Careful analysis of RTN provides information about the material defects generating this noise and ultimately may lead to a solution to eliminate this noise source from the optoelectronic imaging system. The focus of this effort is the definition and demonstration of a simple histogram-based detection algorithm, which evaluates the shape of the pixel output histogram in order to detect RTN pixels and extract the energy levels of the defects. The algorithm sensitivity and precision statistics are benchmarked as a function of the detection threshold. Applying the algorithm to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1280\times1024$ </tex-math></inline-formula> infrared FPA irradiated with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.2\times 10^{11}$ </tex-math></inline-formula> protons/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (63 MeV) shows a maximum 84% increase in the number of pixels exhibiting RTN from preradiation to post-300 K anneal. The distribution of preradiation trap energies shows clusters at 13 meV above the Fermi level and 14 and 44 meV below the Fermi level. The radiation-induced defects, however, exhibit a different energy distribution with an increased concentration at the Fermi level, suggesting different energy distributions for the traps created during the manufacturing process compared to those created by proton radiation.

LESH - feature extraction and cognitive machine learning techniques for recognition of lung cancer cells

The novel local energy-based shape histogram (LESH) feature mining strategy was proposed for different cancer predictions. This paper stretches out unique work to apply the LESH system to distinguish lung cancer using machine learning approaches. As the traditional neural network systems are complex and time consuming, machine learning approaches are considered in this work, which atomises the process of tumour identification. Before LESH feature extraction, we upgraded the radiograph pictures utilising a complexity constrained versatile histogram adjustment approach. Subjective machine learning classifiers are chosen specifically extraordinary learning machine approach; support vector machine (SVM) connected utilising the LESH impassive features for effective analysis of right therapeutic state in the X-ray and MRI pictures. The framework comprises of feature extraction stage, including choice stage and order stage. For including extraction/choice distinctive wavelets capacities have been connected to locate the noteworthy exactness. Grouping K-nearest neighbour calculation has been created/used for arrangement. The informational collection used in the proposed work has 114 knob regions and 73 non-knob districts. Precision levels of more than 96% for characterisation that have been accomplished which exhibit the benefits of the proposed approach.

A Novel DIBR 3D Image Hashing Scheme Based on Pixel Grouping and NMF

Most of the traditional 2D image hashing schemes do not take into account the change of viewpoint when constructing the final hash vector. This result in the classification accuracy rate is unsatisfactory when applied for depth-image-based rendering (DBIR) 3D image identification. In this work, pixel grouping based on histogram shape and nonnegative matrix factorization (NMF) are applied to design DIBR 3D image hashing with better robustness resisting to geometric distortions and higher classification accuracy rate for virtual image identification. Experiments show that the proposed hashing is robust against common signal and geometric distortion attacks, such as additive noise, blurring, JPEG compression, scaling, and rotation. Compared with the state-of-art schemes of traditional 2D image hashing, the proposed hashing achieves better performances under above attacks, especially for virtual image identification.

Local elastic moduli of simple random composites computed at different length scales

Techniques like nanoindentation and atomic force microscopy can estimate the local elastic moduli in a region surrounding the probe used. For composites with phase regions much larger than the size of the probe, these procedures can identify the phases via their different elastic moduli but identifying phase regions that are on the same size scale as the indent is more problematic. This paper looks at three random 3D 8003 voxel composite models, each consisting of a matrix and spherical inclusions. One model has non-overlapping spheres and two models have overlapping spheres, with two and three distinct phases. The linear elastic problem is solved for each microstructure, and histograms are made of the local Young’s moduli over a number of sub-volumes (SVs), averaged over progressively larger SVs. The number and shape of histogram peaks change from N delta functions, where N is the number of elastically distinct phases, at the 1 voxel SV limit, to a single delta function located at the value of the effective global Young’s modulus, when the SV equals the unit cell volume. The phase volume fractions are also tracked for each bin in the Young’s modulus histograms, showing the phase make-up of bin in the histogram. There are clear differences seen between the non-overlapping and three-phase overlapping models and the two-phase overlapping sphere model, because of different size microstructural features, characterized by the average value of size as computed by the W(q) function. In the three-phase model, a peak that is originally all phase 3 persists at its same location, but as the size of the SVs increase, it is made up of a mixture of phases, so that it cannot be identified with a single phase even though it remains a clear peak. These results give some guidance as to what probe size might be useful in distinguishing different phases by local elastic moduli measurements, and how the length scales of the probe and the microstructure interact.

A novel and efficient approach for the classification of skin melanoma

Melanoma is a very common type of skin cancer that is an out-of-control growth of the cell. The detection of melanoma is very crucial in clinical practice due to irregularity in the shape of border and difference of tissue. Even though there are several methods proposed in literature, the identification of melanoma is still a complex process. This work proposes a novel Color Local Directional Pattern-based feature extraction technique for the classification of the skin cancer images. Along with the Color Local Directional Pattern this work also uses various descriptors for shape, color and Pyramid Histogram of Oriented Gradients. This method also uses a stacked Restricted Boltzmann Machine in Deep Belief Network, Support Vector Machine and Random Forest classifier for the classification. The experiments used the images from International Skin Imaging Collaboration (ISIC) 2016, ISIC 2017, Pedro Hispano Hospital (PH2) dataset, Dermnet and DermIS datasets. The performance metrics like specificity, sensitivity, F-Score, positive predictive value, accuracy, harmonic mean, and Area Under the Curve (AUC) are used for the experimental analysis. The results show that the proposed work achieves best results while using the combination of texture, color and shape descriptors and stacked RBM in DBN.

Non-stationary signal decomposition approach for harmonic responses detection in operational modal analysis

This paper proposes a non-stationary signal decomposition approach to remove the harmonic responses in the operational modal analysis for time-varying structures. Two time–frequency representations, the non-parametric windowed Fourier transform and the power spectral density estimated by the parametric functional series time-dependent autoregressive moving average model, are combined to decompose the raw multimodal responses into individual unimodal components. According to the histogram shape or the excess Kurtosis value of the components, the harmonic components and structural components can be distinguished and the harmonic ones will be removed in the modal identification. To identify the close modes of a time-varying structure, a time–frequency domain decomposition method based on the power spectral density matrix and singular value decomposition technique is also proposed. The numerical and experimental examples finally demonstrate that the proposed approach can remove the harmonic components effectively and can obtain good modal parameters identification results for time-varying structures with close or even repeated modes.

Optimized image watermarking with artificial neural networks and histogram shape

In today’s era, Digital watermarking is a well-known application in direction of proving the authenticity of digital content. In this concern, a new optimized image watermarking method has been suggested by incorporating the concepts of artificial neural networks and histogram together. In this paper, the histogram shape concept has been kept into practice to implant the watermark information inside the host image with a view to maintain imperceptibility and resistance. In this suggested method, optimization of extraction process has been holded as a substantial problem that helps in enhancing the resistance in front of distinct attacks. So, the problem of strengthening resistance in attacked environment is solved by implementing the artificial neural networks in the proposed method. The optimization of extraction process is accomplished through two distinct neural networks called backpropagation neural networks and autoencoder neural networks. As an outcome, the experimental outcome of suggested image watermarking method is assessed on a set of three images in form of PSNR and NC. The resistance of the suggested method has been checked under different attacks alike rotation, histogram equalization, gaussian noise, cropping, JPEG compression, poisson noise, average filter, speckle noise, median filter and salt and pepper noise validates that proposed watermarking has accomplished its objective. The experimental results in the form of PSNR and NC verify that proposed scheme has accomplished its objective.

Group based emotion recognition from video sequence with hybrid optimization based recurrent fuzzy neural network

Group-based emotion recognition (GER) is an interesting topic in both security and social area. In this paper, a GER with hybrid optimization based recurrent fuzzy neural network is proposed which is from video sequence. In our work, by utilizing the Neural Network the emotion recognition (ER) is performed from group of people. Initially, original video frames are taken as input and pre-process it from multi user video data. From this pre-processed image, the feature extraction is done by Multivariate Local Texture Pattern (MLTP), gray-level co-occurrence matrix (GLCM), and Local Energy based Shape Histogram (LESH). After extracting the features, certain features are selected using Modified Sea-lion optimization algorithm process. Finally, recurrent fuzzy neural network (RFNN) classifier based Social Ski-Driver (SSD) optimization algorithm is proposed for classification process, SSD is used for updating the weights in the RFNN. Python platform is utilized to implement this work and the performance of accuracy, sensitivity, specificity, recall and precision is evaluated with some existing techniques. The proposed method accuracy is 99.16%, recall is 99.33%, precision is 99%, sensitivity is 99.93% and specificity is 99% when compared with other deep learning techniques our proposed method attains good result.

A simple model for PIFs extraction at digital change detection approach

The radiometric normalization using pseudo-invariant features (PIFs) is one of the best methods of image-based atmospheric correction. However, one of the main challenges of this approach is the correct selection of PIFs. So far many efforts have been made to automate radiometric correction using PIFs, although the capability of all these automated methods has been proven in various studies, their application requires intermediate to advanced statistical and computational knowledge and their application is beyond the reach of most conventional remote sensing users. Therefore, in this research, a straightforward simple method is proposed based on the definition of PIFs that automatically identifies these areas and uses them on a regression procedure for automatic normalization. The study area is Hara Protection Area (the Hara international wetland), which has sub-tropical climate and due to surface homogeneity of its mangrove forests can be used as a pilot for natural, and homogeneous ecosystems such as forested areas. To create an automatic PIFs extraction in this area, initially, we masked areas that should not be in the PIFs by applying some thresholds. At this stage, water, vegetation and mountainous areas were filtered. Then, the radiometric resolution of the two different sensor images was identical, and using the differential histogram shape of the two images, unvaried areas were extracted. To validate the accuracy of the proposed method, absolute radiometric correction using atmospheric and topographic correction (ATCOR), fast line-of-sight atmospheric analysis of hypercubes (FLAASH) and atmospheric correction (ATMOSC) methods, and relative radiometric correction using both empirical line calibration method and dark object subtraction method and automatic radiometric correction using QUick atmospheric correction (QUAC) and autonomous atmospheric correction (IMAGINE AAIC) were applied on the data. The output of all atmospheric correction methods and the proposed method was applied in the image algebra change detection in the form of a difference and with a threshold of twice the standard deviation from the mean to be checked by 219 points. The results of validation and qualitative studies of the histogram comparisons proved the proper functioning of the proposed method (κ greater than 0.8), and using cross tables, the performance of the proposed method is similar to that of the empirical line calibration method (more than 76%). Finally, a few unique features in the current research proposal, including simplicity, automation, negligible systematic error, the possibility of using in a biomarker degradation warning system, the independence on the type of sensor used, distinguish it from other radiometric correction methods.

Metallic Iron in Basalts of the Malyi Yenisei Lava River: Results of Thermomagnetic Study

Thermomagnetic analysis of samples from two sections of lava layers of the Late Cenozoic basalt lava river in the Malyi Yenisei valley is carried out. The main magnetization carrier in the studied basalts is titanomagnetite with the Curie points of 100–120°C which is frequently substantially oxidized both single-phase and heterophase up to magnetite. It is likely that some part of metallic iron in the studied samples has also been oxidized and even disintegrated, which resulted in the significant scatter of iron concentration across the flow against which, however, the increasing trend of iron concentration in the lava flow from the top downwards is observed. The relative magnitude of this increase (iron concentration gradient along the vertical of the lava flow) is almost constant for all flows of the lava sequence probably indicating the decisive role of gravity in iron particle precipitation in the lava. Based on the synthesis of iron particle data from different objects and different regions of the world, this correlation with gravity also follows from the very similar shapes of particle size histograms of iron. This is most clearly seen from the same particle size modes (10–20 μm). Another important finding is that this constant mode of iron particle size does not depend on age and origin of rock as well as on the type of particle source (terrestrial or extraterrestrial).