scholarly journals VOLUME TENSOR ESTIMATION USING A VIRTUAL LINE GRID: STUDY OF A DEVELOPING PHEASANT BRAIN

2019 ◽  
Author(s):  
Jiri Janacek ◽  
Daniel Jirak
Keyword(s):  
Systematic Sampling ◽  
Data Set ◽  
Robust Estimate ◽  
Virtual Line ◽  
3D Data

The volume tensor provides a robust estimate of the shape and orientation of an object in space. In this paper, we introduce Fakir method for estimating the tensor of an object in 3D data set based on the intersections of objects boundary with virtual lines. We calculate the precision of shape estimates by predicting the variance of estimators of integrals based on systematic sampling. To demonstrate the ability of the Fakir method, we measure changes in shape and orientation of compartments in the pheasant brain during development.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

The Variation of Ionospheric O+ and H+ Outflow during Sawtooth Oscillations

2021 ◽  
Author(s):  
Niloufar Nowrouzi ◽  
Lynn Kistler ◽  
Eric Lund ◽  
Kai Zhao
Keyword(s):  
Plasma Sheet ◽  
Statistical Study ◽  
Onset Time ◽  
Direct Access ◽  
Data Set ◽  
Superposed Epoch Analysis ◽  
3D Data ◽  
Before And After ◽  
Epoch Analysis ◽  
Sawtooth Oscillations

<p>Sawtooth events are repeated injections of energetic particles at geosynchronous orbit. Although studies have shown that 94% of sawtooth events occur during  magnetic storm times, the main factor that causes a sawtooth event is unknown. Simulations have suggested that heavy ions like O<sup>+</sup> may play a role in driving the sawtooth mode by increasing the magnetotail pressure and causing the magnetic tail to stretch. O<sup>+</sup> ions located in the nightside auroral region have a direct access to the near-earth plasma-sheet. O<sup>+</sup> in the dayside cusp can reach to the midtail plasma-sheet when the convection velocity is sufficiently strong. Whether the dayside or nightside source is more important is not known.</p><p>We show results of a statistical study of the variation of the O+ and H+ outflow flux during sawtooth events for SIR and ICME sawtooth events. We perform a superposed epoch analysis of the ion outflow using the TEAMS (Time-of-Flight Energy Angle Mass Spectrograph) instrument on the FAST spacecraft. TEAMS measures the ion composition over the energy range of 1 eV e<sup>-1</sup> to 12 keV e<sup>-1</sup>.  We have done major corrections and calibrations (producing 3D data set, anode calibration, mass classification, removing ram effect and incorporating dead time corrections) on TEAMS data and produced a data set for four data species (H<sup>+</sup>, O<sup>+</sup>, and He<sup>+</sup>). From 1996 to 2007, we have data for 133 orbits of CME-driven and for 103 orbits of SIR-driven sawtooth events with an altitude above 1500 km. We found that:</p><ul><li>the averaged O<sup>+</sup> outflow flux is more intense in the cusp dayside than in the nightside, before and after onset time.</li> <li><span>Before onset, an intense averaged outflow flux in the dawnside of CME events is seen. This outflow decreases after onset time.</span></li> <li><span>In both CME-driven and SIR-driven, the averaged O</span><sup>+</sup><span> outflow increases after onset time, in the nightside, cusp dayside. This increase is greater on the nightside than in the cusp.</span></li> </ul><p>We will develop this study by performing a similar statistical study for H<sup>+</sup> outflow and finally will compare the H<sup>+</sup> result with the O<sup>+ </sup>result.</p>

MSE optimal bit-rate allocation in JPEG2000 Part 2 compression applied to a 3D data set

2004 ◽  
Author(s):  
Olga M. Kosheleva ◽  
Sergio D. Cabrera ◽  
Bryan E. Usevitch ◽  
Alberto Aguirre ◽  
Edward Vidal, Jr.
Keyword(s):  
Rate Allocation ◽  
Bit Rate ◽  
Data Set ◽  
3D Data

scholarly journals PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 126 ◽  
Author(s):  
Feiyang Chen ◽  
Ying Jiang ◽  
Xiangrui Zeng ◽  
Jing Zhang ◽  
Xin Gao ◽  
...  
Keyword(s):  
Training Data ◽  
Data Sets ◽  
Local Similarity ◽  
Biomedical Data ◽  
Data Set ◽  
Biomedical Image ◽  
Feature Representations ◽  
3D Data ◽  
Supervised Methods ◽  
Data Driven Decisions

Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and proficiency in related background knowledge. In contrast, unsupervised learning makes data-driven decisions by obtaining insights directly from the data themselves. In this paper, we propose a completely unsupervised self-aware network based on pre-training and attentional backpropagation for biomedical salient segmentation, named as PUB-SalNet. Firstly, we aggregate a new biomedical data set from several simulated Cellular Electron Cryo-Tomography (CECT) data sets featuring rich salient objects, different SNR settings, and various resolutions, which is called SalSeg-CECT. Based on the SalSeg-CECT data set, we then pre-train a model specially designed for biomedical tasks as a backbone module to initialize network parameters. Next, we present a U-SalNet network to learn to selectively attend to salient objects. It includes two types of attention modules to facilitate learning saliency through global contrast and local similarity. Lastly, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by self-aware attentional backpropagation. We apply PUB-SalNet for analysis of 2D simulated and real images and achieve state-of-the-art performance on simulated biomedical data sets. Furthermore, our proposed PUB-SalNet can be easily extended to 3D images. The experimental results on the 2d and 3d data sets also demonstrate the generalization ability and robustness of our method.

scholarly journals Reconstruction of Axial Tomographic High Resolution Data from Confocal Fluorescence Microscopy: A Method for Improving 3D FISH Images

2000 ◽  
Vol 20 (1) ◽  
pp. 7-15 ◽  
Author(s):  
R. Heintzmann ◽  
G. Kreth ◽  
C. Cremer
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Axial Direction ◽  
Laser Scanning Microscopy ◽  
Three Dimensions ◽  
Confocal Laser ◽  
Data Sets ◽  
Data Set ◽  
High Resolution Data ◽  
3D Data

Fluorescent confocal laser scanning microscopy allows an improved imaging of microscopic objects in three dimensions. However, the resolution along the axial direction is three times worse than the resolution in lateral directions. A method to overcome this axial limitation is tilting the object under the microscope, in a way that the direction of the optical axis points into different directions relative to the sample. A new technique for a simultaneous reconstruction from a number of such axial tomographic confocal data sets was developed and used for high resolution reconstruction of 3D‐data both from experimental and virtual microscopic data sets. The reconstructed images have a highly improved 3D resolution, which is comparable to the lateral resolution of a single deconvolved data set. Axial tomographic imaging in combination with simultaneous data reconstruction also opens the possibility for a more precise quantification of 3D data. The color images of this publication can be accessed from http://www.esacp.org/acp/2000/20‐1/heintzmann.htm. At this web address an interactive 3D viewer is additionally provided for browsing the 3D data. This java applet displays three orthogonal slices of the data set which are dynamically updated by user mouse clicks or keystrokes.

Residual moveout in anisotropic angle-domain common-image gathers

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. S93-S103 ◽  
Author(s):  
Biondo Biondi
Keyword(s):  
Real Data ◽  
Velocity Estimation ◽  
Aperture Angle ◽  
Data Set ◽  
3D Data ◽  
Wavefield Continuation ◽  
Transverse Isotropic ◽  
Phase Angles ◽  
The Relationship ◽  
General Method

I develop the fundamental concepts for quantitatively relating perturbations in anisotropic parameters to the corresponding reflector movements in angle-domain common-image gathers (ADCIGs) after anisotropic wavefield-continuation migration. The proposed theory potentially enables the application of residual moveout (RMO) analysis of ADCIGs to velocity estimation in realistic anisotropic conditions. I demonstrate that linearization of the relationship between anisotropic velocity parameters and reflector movements can be derived by assuming stationary raypaths. This assumption leads to a fairly simple analytical derivation. I then apply the general method to the particular case of RMO analysis of reflections from flat reflectors in a vertical transverse isotropic (VTI) medium. This analysis yields expressions to predict RMO curves in migrated ADCIGs. These RMO expressions are functions of both the phase aperture angle and the group aperture angle. Several numerical examples demonstrate the accuracy of the RMO curves predicted by my kinematic analysis. The synthetic examples also show that approximating the group angles with the phase angles in the application of the RMO expressions may lead to substantial errors for events reflected at wide aperture angles. The results obtained by migrating a 2D line extracted from a Gulf of Mexico 3D data set confirm the accuracy of the proposed method. The RMO curves predicted by the theory match the RMO function observed in the ADCIGs computed from the real data.

scholarly journals Estimation of Population Mean in Chain Ratio-Type Estimator under Systematic Sampling

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Mursala Khan ◽  
Rajesh Singh
Keyword(s):  
Finite Population ◽  
Real Data ◽  
Survey Sampling ◽  
Systematic Sampling ◽  
Auxiliary Variables ◽  
Data Set ◽  
Population Mean ◽  
First Order ◽  
The Mean ◽  
A Chain

A chain ratio-type estimator is proposed for the estimation of finite population mean under systematic sampling scheme using two auxiliary variables. The mean square error of the proposed estimator is derived up to the first order of approximation and is compared with other relevant existing estimators. To illustrate the performances of the different estimators in comparison with the usual simple estimator, we have taken a real data set from the literature of survey sampling.

scholarly journals THE CONTRIBUTION OF GEOMATIC TECHNOLOGIES TO BIM

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 85-89
Author(s):  
K. Pavelka ◽  
E. Matoušková ◽  
K. Pavelka jr.
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Modern Technology ◽  
Data Conversion ◽  
Digital Information ◽  
Historical Building ◽  
Data Set ◽  
Common Definition ◽  
3D Data ◽  
Building Information

Abstract. There are many definitions of the commonly used abbreviation BIM, but one can say that each user or data supplier has different idea about it. There can be an economic view, or other aspects like surveying, material, engineering, maintenance, etc. The common definition says that Building Information Modelling or Building Information Management (BIM) is a digital model representing a physical and functional object with its characteristics. The model serves as a database of object information for its design, construction and operation over its life cycle, i.e. from the initial concept to the removal of the building. BIM is a collection of interconnected digital information in both protected and open formats, recording graphical and non-graphical data on model elements. There are two facets: a) BIM created simultaneously with the project, or project designed directly in BIM (it is typical of new objects designed in CAD systems - for example in the Revit software) or b) BIM for old or historical objects. The former is a modern technology, which is nowadays used worldwide. From the engineer’s perspective, the issue is the creation of BIM for older objects. In this case, it is crucial to obtain a precise 3D data set - complex 3D documentation of an object is needed and it is created using various surveying techniques. The most popular technique is laser scanning or digital automatic photogrammetry, from which a point cloud is derived. But this is not the main result. While classical geodesy gives selective localized information, the above-mentioned technologies give unselected information and provide huge datasets. Fully automatic technologies that would select important information from the point cloud are still under development. This seems to be a task for the coming years. Large amounts of data can be acquired automatically and quickly, but getting the expected information is another matter. These problems will be analysed in this paper. Data conversion to BIM, especially for older objects, will be shown on several case studies. The first is an older technical building complex transferred to BIM, the second one is a historical building, and the third one will be a historic medieval bridge (Charles Bridge in Prague). The last part of this paper will refer to aspects and benefits of using Virtual Reality in BIM.

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