Interactive Analysis for Large Volume Data from Fluorescence Microscopy at Cellular Precision

2021 ◽  
Author(s):  
Yong Wan ◽  
Holly A. Holman ◽  
Charles Hansen
Keyword(s):  
Fluorescence Microscopy ◽  
Large Volume ◽  
Volume Data ◽  
Interactive Analysis

scholarly journals Interactive Visualization and On-Demand Processing of Large Volume Data: A Fully GPU-Based Out-of-Core Approach

2020 ◽  
Vol 26 (10) ◽  
pp. 3008-3021 ◽  
Author(s):  
Jonathan Sarton ◽  
Nicolas Courilleau ◽  
Yannick Remion ◽  
Laurent Lucas
Keyword(s):  
Large Volume ◽  
Interactive Visualization ◽  
Volume Data ◽  
On Demand

APPHi: An automated photometry pipeline for high cadence, large volume data

2019 ◽  
Vol 26 ◽  
pp. 1-13 ◽  
Author(s):  
E. Sánchez ◽  
J.H. Castro-Chacón ◽  
J.S. Silva ◽  
J.B. Hernández-Águila ◽  
M. Reyes-Ruiz ◽  
...  
Keyword(s):  
Large Volume ◽  
Volume Data ◽  
High Cadence

scholarly journals VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Hisham A. Kholidy ◽  
Abdelkarim Erradi
Keyword(s):  
Power Systems ◽  
Large Volume ◽  
High Speed ◽  
Detection System ◽  
Heterogeneous Data ◽  
Area Measurement ◽  
Volume Data ◽  
Detection Systems ◽  
Key Events ◽  
Reduction Approach

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.

Cost-Efficient and Automatic Large Volume Data Acquisition Method for On-Chip Random Process Variation Measurement

2015 ◽  
Vol 15 (2) ◽  
pp. 184-193 ◽  
Author(s):  
Sooeun Lee ◽  
Seungho Han ◽  
Ikho Lee ◽  
Jae-Yoon Sim ◽  
Hong-June Park ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Random Process ◽  
Large Volume ◽  
Process Variation ◽  
Volume Data ◽  
Cost Efficient ◽  
On Chip ◽  
Acquisition Method

scholarly journals Efficiently Rendering Large Volume Data Using Texture Mapping Hardware

1999 ◽  
pp. 121-132 ◽  
Author(s):  
Xin Tong ◽  
Wenping Wang ◽  
Waiwan Tsang ◽  
Zesheng Tang
Keyword(s):  
Large Volume ◽  
Texture Mapping ◽  
Volume Data
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