The high-speed compression of large data streams in ultrasonic diagnostics

2006 ◽  
Vol 16 (1) ◽  
pp. 68-70 ◽  
Author(s):  
S. A. Sharov ◽  
Yu. V. Orlov ◽  
I. G. Persiantsev
Keyword(s):  
Data Streams ◽  
High Speed ◽  
Large Data ◽  
Ultrasonic Diagnostics

Top-down Fabricated Gold Nanostrip on Silicon-on-insulator Wafer: A Promising Building Block towards Ultra-compact Optical Device

Nanoscale ◽  
2020 ◽  
Author(s):  
Fuping Zhang ◽  
Weikang Liu ◽  
Li Chen ◽  
Zhiqiang Guan ◽  
Hongxing Xu
Keyword(s):  
Optical Communication ◽  
Data Transmission ◽  
Building Block ◽  
High Speed ◽  
Large Data ◽  
Low Energy ◽  
Silicon On Insulator ◽  
Fundamental Building Block ◽  
Low Energy Consumption ◽  
Controllable Fabrication

he plasmonic waveguide is the fundamental building block for high speed, large data transmission capacity, low energy consumption optical communication and sensing. Controllable fabrication and simultaneously optimization of the propagation...

A proposal to leverage high-quality veterinary diagnostic laboratory large data streams for animal health, public health, and One Health

2021 ◽  
pp. 104063872110030
Author(s):  
Craig N. Carter ◽  
Jacqueline L. Smith
Keyword(s):  
Public Health ◽  
Data Streams ◽  
One Health ◽  
Animal Health ◽  
Disease Outbreaks ◽  
Large Data ◽  
Health Impact ◽  
The United States ◽  
Zoonotic Diseases ◽  
Diagnostic Laboratory

Test data generated by ~60 accredited member laboratories of the American Association of Veterinary Laboratory Diagnosticians (AAVLD) is of exceptional quality. These data are captured by 1 of 13 laboratory information management systems (LIMSs) developed specifically for veterinary diagnostic laboratories (VDLs). Beginning ~2000, the National Animal Health Laboratory Network (NAHLN) developed an electronic messaging system for LIMS to automatically send standardized data streams for 14 select agents to a national repository. This messaging enables the U.S. Department of Agriculture to track and respond to high-consequence animal disease outbreaks such as highly pathogenic avian influenza. Because of the lack of standardized data collection in the LIMSs used at VDLs, there is, to date, no means of summarizing VDL large data streams for multi-state and national animal health studies or for providing near-real-time tracking for hundreds of other important animal diseases in the United States that are detected routinely by VDLs. Further, VDLs are the only state and federal resources that can provide early detection and identification of endemic and emerging zoonotic diseases. Zoonotic diseases are estimated to be responsible for 2.5 billion cases of human illness and 2.7 million deaths worldwide every year. The economic and health impact of the SARS-CoV-2 pandemic is self-evident. We review here the history and progress of data management in VDLs and discuss ways of seizing unexplored opportunities to advance data leveraging to better serve animal health, public health, and One Health.

Distributed and collaborative visualization of large data sets using high-speed networks

2006 ◽  
Vol 22 (8) ◽  
pp. 1004-1010 ◽  
Author(s):  
Andrei Hutanu ◽  
Gabrielle Allen ◽  
Stephen D. Beck ◽  
Petr Holub ◽  
Hartmut Kaiser ◽  
...  
Keyword(s):  
High Speed ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
High Speed Networks ◽  
Collaborative Visualization

scholarly journals A KNOWLEDGE-BASED METHOD FOR GENERATING SUMMARIES OF SPATIAL MOVEMENT IN GEOGRAPHIC AREAS

2010 ◽  
Vol 19 (04) ◽  
pp. 393-415 ◽  
Author(s):  
MARTIN MOLINA ◽  
AMANDA STENT
Keyword(s):  
Mobile Phones ◽  
Data Streams ◽  
Large Data ◽  
Gps Data ◽  
Knowledge Representations ◽  
Knowledge Based ◽  
Spatial Movement ◽  
Present Evaluation ◽  
Main Components

In this article we describe a method for automatically generating text summaries of data corresponding to traces of spatial movement in geographical areas. The method can help humans to understand large data streams, such as the amounts of GPS data recorded by a variety of sensors in mobile phones, cars, etc. We describe the knowledge representations we designed for our method and the main components of our method for generating the summaries: a discourse planner, an abstraction module and a text generator. We also present evaluation results that show the ability of our method to generate certain types of geospatial and temporal descriptions.

Lane Detection Algorithm Based on Genetic Algorithm and its Parallel Computing Realization

2012 ◽  
Vol 479-481 ◽  
pp. 65-70
Author(s):  
Xiao Hui Zhang ◽  
Liu Qing ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Data Transmission ◽  
High Speed ◽  
High Performance ◽  
Large Data ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Road ◽  
Time Problem ◽  
High Speed Data

Based on the target detection of alignment template, the paper designs a lane alignment template by using correlation matching method, and combines with genetic algorithm for template stochastic matching and optimization to realize the lane detection. In order to solve the real-time problem of lane detection algorithm based on genetic algorithm, this paper uses the high performance multi-core DSP chip TMS320C6474 as the core, combines with high-speed data transmission technology of Rapid10, realizes the hardware parallel processing of the lane detection algorithm. By Rapid10 bus, the data transmission speed between the DSP and the DSP can reach 3.125Gbps, it basically realizes transmission without delay, and thereby solves the high speed transmission of the large data quantity between processor. The experimental results show that, no matter the calculated lane line, or the running time is better than the single DSP and PC at the parallel C6474 platform. In addition, the road detection is accurate and reliable, and it has good robustness.

scholarly journals Survey on Feature Transformation Techniques for Data Streams

2020 ◽  
Author(s):  
Maroua Bahri ◽  
Albert Bifet ◽  
Silviu Maniu ◽  
Heitor Murilo Gomes
Keyword(s):  
Machine Learning ◽  
Data Streams ◽  
Large Data ◽  
High Dimensional ◽  
Feature Transformation ◽  
Transformation Techniques ◽  
Computational Costs ◽  
The Past ◽  
Fundamental Challenge ◽  
Mining Algorithms

Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task's performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes over large data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.

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