scholarly journals MUSiiC ToolKit 2.0: Bidirectional Real-time Software Framework for Advanced Interventional Ultrasound Research

2012 ◽  
Author(s):  
Hyun-jae Kang ◽  
Alexis Cheng ◽  
Emad Boctor
Keyword(s):  
Medical Imaging ◽  
Data Processing ◽  
Real Time ◽  
Data Communication ◽  
Ease Of Use ◽  
Data Synchronization ◽  
Software Framework ◽  
Multiple Sources ◽  
Bidirectional Communication ◽  
Executable Programs

Ultrasound (US) imaging is a popular and convenient medical imaging modality thanks to its mobility, non-ionizing radiation, ease-of-use, and real-time acquisition. Conventional US imaging is frequently integrated with tracking systems and robotic systems in Image Guided Therapy (IGT) systems. Recently, these systems are also incorporating advanced US imaging such as US elasticity imaging, photoacoustic imaging, and thermal imaging. Real-time synchronous data from multiple sources and bidirectional data communication are essential for integrating components in advanced US IGT research. We previously proposed the MUSiiC ToolKit [1], a modular real-time software toolkit, and OpenIGTLinkMUSiiC [2], a standard communication protocol extended from the OpenIGTLink library [3, 4]. However, this software framework only supported real-time synchronous data from at most two sources and unidirectional communication at the software module level and class level.In this paper, we propose MUSiiC ToolKit 2.0, an upgraded software framework for interventional advanced US research sup-porting bidirectional communication, real-time US data processing, and real-time data synchronization from multiple sources. MUSiiC ToolKit 2.0 consists of OpenIGTLink 2.0, OpenIGTlinkMUSiiC 2.0, MUSiiCNotes 2.0, and a collection of executable programs designed for US research. OpenIGTLink 2.0 is a standard TCP/IP-based protocol for the integration of medical imaging and IGT systems. OpenIGTLinkMUSiiC 2.0 is the upgraded version of OpenIGTLinkMUSiiC with new active multi-task classes, data interfaces for supporting bidirectional communication and parallel data processing. MUSiiCNotes 2.0 provides US research-oriented task classes, such as US data acquisition, beamforming, envelope detection, scan conversion, and data synchronization. Graphic User Interface (GUI) units are also available for the executable programs in MUSiiCNotes 2.0. Finally, we introduce advanced US applications based on this new software framework.

The Design of Data Security Synchronization in the Network of Satellite and Ground Security Management Instrument

2010 ◽  
Vol 439-440 ◽  
pp. 208-214 ◽  
Author(s):  
Qian Mu Li ◽  
Rui Wang ◽  
Jie Yin ◽  
Jun Hou
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Network Performance ◽  
High Efficiency ◽  
Security Management ◽  
Direct Access ◽  
Data Synchronization ◽  
Buffer Size ◽  
Synchronization Mechanism ◽  
Management Instrument

Network security, data management, data synchronization Abstract. The aim of Network of Satellite and Ground Security Management Instrument (NSGMI) is to increase network availability, improve network performance and control operation costs. After analyzing the shortcomings of traditional data synchronization mechanism, this paper reconstructs data processing method to improve communication ability of NSGMI, and provides a solution to guarantee real time and reliable on the application layer. The new mechanism provides buffer areas and operation flows to satisfy the high efficiency of data processing demand and the strong real time request. It solves how to open the buffer size when a direct access changes records.

scholarly journals Evolution of the ALICE Software Framework for Run 3

2019 ◽  
Vol 214 ◽  
pp. 05010 ◽  
Author(s):  
Giulio Eulisse ◽  
Piotr Konopka ◽  
Mikolaj Krzewicki ◽  
Matthias Richter ◽  
David Rohr ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Model ◽  
Message Passing ◽  
Software Framework ◽  
Distributed Software ◽  
Central Collisions ◽  
Modular Software ◽  
Level Trigger ◽  
Data Throughput ◽  
High Level

ALICE is one of the four major LHC experiments at CERN. When the accelerator enters the Run 3 data-taking period, starting in 2021, ALICE expects almost 100 times more Pb-Pb central collisions than now, resulting in a large increase of data throughput. In order to cope with this new challenge, the collaboration had to extensively rethink the whole data processing chain, with a tighter integration between Online and Offline computing worlds. Such a system, code-named ALICE O2, is being developed in collaboration with the FAIR experiments at GSI. It is based on the ALFA framework which provides a generalized implementation of the ALICE High Level Trigger approach, designed around distributed software entities coordinating and communicating via message passing. We will highlight our efforts to integrate ALFA within the ALICE O2 environment. We analyze the challenges arising from the different running environments for production and development, and conclude on requirements for a flexible and modular software framework. In particular we will present the ALICE O2 Data Processing Layer which deals with ALICE specific requirements in terms of Data Model. The main goal is to reduce the complexity of development of algorithms and managing a distributed system, and by that leading to a significant simplification for the large majority of the ALICE users.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

Application of the Real-Time EtherCAT Technology in Wind Power Control System

2012 ◽  
Vol 580 ◽  
pp. 155-159
Author(s):  
Xiang Ming Wang ◽  
Jin Chao Wang ◽  
Dong Hua Sun
Keyword(s):  
Control System ◽  
Real Time ◽  
Wind Power ◽  
High Speed ◽  
Communication Protocol ◽  
Data Communication ◽  
Data Acquisition System ◽  
Slave System ◽  
The Real ◽  
Wind Power System

In this paper, the real-time EtherCAT technology is introduced in detail, which including operating principle, communication protocol and the superiority performance of EtherCAT i.e. synchronicity, simultaneousness and high speed. To show how to design a slave system that considering the characteristics of application, the method of developing systems based no EtherCAT technology are proposed. Finally, a data acquisition system based on EtherCAT technology is designed. Application of EtherCAT technology can improve the real-time characteristics of data communication in wind power system.

A New Approach of Expanding Data Processing Ability for Configuration Monitoring Software MCGS Based on OLE

2011 ◽  
Vol 65 ◽  
pp. 295-298 ◽  
Author(s):  
Fan Yang ◽  
Cai Li Zhang
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Monitoring System ◽  
Programming Language ◽  
Configuration Software ◽  
Time Data ◽  
Software Configuration ◽  
Processing Ability ◽  
Significant Performance ◽  
Monitoring Software

Considering the insufficient ability of data processing existed in configuration software, a scheme integrated both advantages of advanced programming language and configuration software is provided. In this scheme real-time data acquisition and complex processing are achieved by advanced programming language, the human-computer interface and other functions of the monitoring system are achieved by configuration software. Configuration software achieves the purpose of expanding data processing ability by data communications between advanced programming language and configuration software based on OLE technology. The practical application result indicates that the data processing ability of configuration software can be effectively expanded based on OLE technology, which has well stability and real-time, and can play significant performance in complex parameters and data processing related monitoring system.

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