scholarly journals An Interactive 3D Visualization Tool for Large Scale Data Sets for Quantitative Atom Probe Tomography

2011 ◽  
Vol 1349 ◽  
Author(s):  
Hari Dahal ◽  
Michael Stukowski ◽  
Matthias J. Graf ◽  
Alexander V. Balatsky ◽  
Krishna Rajan
Keyword(s):  
User Interface ◽  
Open Source ◽  
Real Time ◽  
Atom Probe Tomography ◽  
Large Scale ◽  
3D Visualization ◽  
Atom Probe ◽  
Parallel Rendering ◽  
Data Sets ◽  
Process Data

ABSTRACTSeveral visualization schemes have been developed for imaging materials at the atomic level through atom probe tomography. The main shortcoming of these tools is their inability to parallel process data using multi-core computing units to tackle the problem of larger data sets. This critically handicaps the ability to make a quantitative interpretation of spatial correlations in chemical composition, since a significant amount of the data is missed during subsequent analysis. In addition, since these visualization tools are not open-source software there is always a problem with developing a common language for the interpretation of data. In this contribution we present results of our work on using an open-source advanced interactive visualization software tool, which overcomes the difficulty of visualizing larger data sets by supporting parallel rendering on a graphical user interface or script user interface and permits quantitative analysis of atom probe tomography data in real time. This advancement allows materials scientists a codesign approach to making, measuring and modeling new and nanostructured materials by providing a direct feedback to the fabrication and designing of samples in real time.

scholarly journals V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets

2010 ◽  
Vol 28 (4) ◽  
pp. 348-353 ◽  
Author(s):  
Hanchuan Peng ◽  
Zongcai Ruan ◽  
Fuhui Long ◽  
Julie H Simpson ◽  
Eugene W Myers
Keyword(s):  
Quantitative Analysis ◽  
Real Time ◽  
Large Scale ◽  
3D Visualization ◽  
Image Data ◽  
Data Sets ◽  
Biological Image

scholarly journals On Strong Scaling Open Source Tools for Mining Atom Probe Tomography Data

2019 ◽  
Vol 25 (S2) ◽  
pp. 298-299 ◽  
Author(s):  
Markus Kühbach ◽  
Priyanshu Bajaj ◽  
Andrew Breen ◽  
Eric A. Jägle ◽  
Baptiste Gault
Keyword(s):  
Open Source ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Strong Scaling ◽  
Tomography Data

Tracking Nanostructural Evolution in Alloys: Large-Scale Analysis of Atom Probe Tomography Data on Blue Gene/L

2008 ◽  
Author(s):  
Sudip Seal ◽  
Michael Moody ◽  
Anna Ceguerra ◽  
Simon Ringer ◽  
Krishna Rajan ◽  
...  
Keyword(s):  
Atom Probe Tomography ◽  
Large Scale ◽  
Atom Probe ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
Blue Gene ◽  
Tomography Data

scholarly journals Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

2021 ◽  
pp. 47-59
Author(s):  
Sacha J. van Albada ◽  
Jari Pronold ◽  
Alexander van Meegen ◽  
Markus Diesmann
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Network Models ◽  
Macaque Monkey ◽  
Source Model ◽  
Model Specification ◽  
Data Sets ◽  
Neural Network Models ◽  
Wide Range ◽  
Ict Infrastructure

AbstractWe are entering an age of ‘big’ computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other’s work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of an ICT infrastructure for neuroscience.

A Real-Time Log Analyzer Based on MongoDB

2014 ◽  
Vol 571-572 ◽  
pp. 497-501 ◽  
Author(s):  
Qi Lv ◽  
Wei Xie
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Performance Comparison ◽  
Log Analysis ◽  
Data Sets ◽  
Time Data ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Implementation Approach ◽  
And Performance

Real-time log analysis on large scale data is important for applications. Specifically, real-time refers to UI latency within 100ms. Therefore, techniques which efficiently support real-time analysis over large log data sets are desired. MongoDB provides well query performance, aggregation frameworks, and distributed architecture which is suitable for real-time data query and massive log analysis. In this paper, a novel implementation approach for an event driven file log analyzer is presented, and performance comparison of query, scan and aggregation operations over MongoDB, HBase and MySQL is analyzed. Our experimental results show that HBase performs best balanced in all operations, while MongoDB provides less than 10ms query speed in some operations which is most suitable for real-time applications.

Let the Data Do the Talking: Hypothesis Discovery from Large-Scale Data Sets in Real Time

2012 ◽  
pp. 235-257
Author(s):  
Christopher Oehmen ◽  
Scott Dowson ◽  
Wes Hatley ◽  
Justin Almquist ◽  
Bobbie-Jo Webb-Robertson ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Scale Data ◽  
Large Scale Data Sets

scholarly journals AMi: a GUI-based, open-source system for imaging samples in multi-well plates

2019 ◽  
Vol 75 (8) ◽  
pp. 531-536 ◽  
Author(s):  
Andrew Bohm
Keyword(s):  
User Interface ◽  
Open Source ◽  
Real Time ◽  
Graphical User Interface ◽  
Field Enhancement ◽  
Depth Of Field ◽  
Raspberry Pi ◽  
Real Time Imaging ◽  
Translation Stage ◽  
Multiple Sample

Described here are instructions for building and using an inexpensive automated microscope (AMi) that has been specifically designed for viewing and imaging the contents of multi-well plates. The X, Y, Z translation stage is controlled through dedicated software (AMiGUI) that is being made freely available. Movements are controlled by an Arduino-based board running grbl, and the graphical user interface and image acquisition are controlled via a Raspberry Pi microcomputer running Python. Images can be written to the Raspberry Pi or to a remote disk. Plates with multiple sample wells at each row/column position are supported, and a script file for automated z-stack depth-of-field enhancement is written along with the images. The graphical user interface and real-time imaging also make it easy to manually inspect and capture images of individual samples.

scholarly journals MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

2018 ◽  
Author(s):  
Li Chen ◽  
Bai Zhang ◽  
Michael Schnaubelt ◽  
Punit Shah ◽  
Paul Aiyetan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Open Source ◽  
High Performance ◽  
Large Scale ◽  
Rapid Development ◽  
Data File ◽  
Data Sets ◽  
Proteomics Data ◽  
Amazon Web Services

ABSTRACTRapid development and wide adoption of mass spectrometry-based proteomics technologies have empowered scientists to study proteins and their modifications in complex samples on a large scale. This progress has also created unprecedented challenges for individual labs to store, manage and analyze proteomics data, both in the cost for proprietary software and high-performance computing, and the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI) support, for LC-MS/MS data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignment, false discovery rate estimation, protein inference, determination of protein post-translation modifications, and quantitation of specific (modified) peptides and proteins. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale iTRAQ/TMT LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at: https://bitbucket.org/mschnau/ms-pycloud/downloads/

scholarly journals Real Time System for Human Identification and Tracking from Surveillance Videos

2018 ◽  
Vol 7 (3.12) ◽  
pp. 244 ◽  
Author(s):  
D Vishaka Gayathri ◽  
Shrutee Shree ◽  
Taru Jain ◽  
K Sornalakshmi
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Training Data ◽  
Live Streaming ◽  
Data Sets ◽  
Surveillance Systems ◽  
Real Time System ◽  
Detection Techniques ◽  
Current Location ◽  
Viable System

The need for intelligent surveillance systems has raised the concerns of security. A viable system with automated methods for person identification to detect, track and recognize persons in real time is required. The traditional detection techniques have not been able to analyze such a huge amount of live video generated in real-time. So, there is a necessity for live streaming video analytics which includes processing and analyzing large scale visual data such as images or videos to find content that are useful for interpretation. In this work, an automated surveillance system for real-time detection, recognition and tracking of persons in video streams from multiple video inputs is presented. In addition, the current location of an individual can be searched with the tool bar provided. A model is proposed, which uses a messaging queue to receive/transfer video feeds and the frames in the video are analyzed using image processing modules to identify and recognize the person with respect to the training data sets. The main aim of this project is to overcome the challenges faced in integrating the open source tools that build up the system for tagging and searching people.  

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