scholarly journals Fuzzy Matching: Hardware Accelerated MPI Communication Middleware

2019 ◽  
Author(s):  
Matthew G. F. Dosanjh ◽  
Whit Schonbein ◽  
Ryan E. Grant ◽  
Patrick G. Bridges ◽  
S. Mahdieh Gazimirsaeed ◽  
...  
Keyword(s):  
Communication Middleware ◽  
Fuzzy Matching

scholarly journals Spatial beam shaping of a focused laser with quasi-near-field characteristics based on a concatenated fuzzy matching algorithm

Optik ◽  
2021 ◽  
pp. 166991
Author(s):  
Chengyu Zhu ◽  
Yuxin Li ◽  
Hang Yuan ◽  
Yulei Wang ◽  
Lingxi Liang ◽  
...  
Keyword(s):  
Near Field ◽  
Beam Shaping ◽  
Matching Algorithm ◽  
Spatial Beam ◽  
Fuzzy Matching

A Design for Fault-Tolerant Communication Middleware Based on Time-Triggered

2014 ◽  
Vol 548-549 ◽  
pp. 1326-1329
Author(s):  
Juan Jin ◽  
Qing Fan Gu
Keyword(s):  
Fault Tolerance ◽  
Real Time ◽  
Fault Location ◽  
Fault Tolerant ◽  
Application Software ◽  
Tolerance Mechanisms ◽  
The Real ◽  
Communication Middleware ◽  
Fault Mechanism ◽  
Health Diagnosis

Against to the unsustainable problems of health diagnosis, fault location and fault tolerance mechanisms that existing in the current avionics applications, we proposed a fault-tolerant communication middleware which is based on time-triggered in this paper. This middleware is designed to provide a support platform for applications of the real-time based on communication middleware. From the communication middleware level and also combined with time-triggered mechanism and fault-tolerant strategy, it diagnoses the general faults first, and then routes them to the appropriate fault mechanism to process it. So the middleware completely separates fault-tolerant process from the application software functions.

Fuzzy matching and direct revocation: a new CP-ABE scheme from multilinear maps

2017 ◽  
Vol 22 (7) ◽  
pp. 2267-2274 ◽  
Author(s):  
Hao Wang ◽  
Debiao He ◽  
Jian Shen ◽  
Zhihua Zheng ◽  
Xiaoyan Yang ◽  
...  
Keyword(s):  
Multilinear Maps ◽  
Fuzzy Matching

Health diagnosis robot based on healthcare big data and fuzzy matching

2017 ◽  
Vol 33 (5) ◽  
pp. 2961-2970 ◽  
Author(s):  
Jun Huang ◽  
Dingju Zhu ◽  
Yong Tang
Keyword(s):  
Big Data ◽  
Fuzzy Matching ◽  
Health Diagnosis

scholarly journals Filtering of Interferometric SAR Phase Images as a Fuzzy Matching-Pursuit Blind Estimation

2005 ◽  
Vol 2005 (20) ◽  
Author(s):  
Bruno Aiazzi ◽  
Stefano Baronti ◽  
Massimo Bianchini ◽  
Alessandro Mori ◽  
Luciano Alparone
Keyword(s):  
Matching Pursuit ◽  
Blind Estimation ◽  
Fuzzy Matching ◽  
Phase Images ◽  
Interferometric Sar

scholarly journals A Component-Based C++ Communication Middleware for an Autonomous Robotic Sailboat

2018 ◽  
pp. 39-54 ◽  
Author(s):  
Francisco J. Santana-Jorge ◽  
Antonio C. Domínguez-Brito ◽  
Jorge Cabrera-Gámez
Keyword(s):  
Communication Middleware

Fuzzy Matching Based on Gray-scale Difference for Quantum Images

2018 ◽  
Vol 57 (8) ◽  
pp. 2447-2460 ◽  
Author(s):  
GaoFeng Luo ◽  
Ri-Gui Zhou ◽  
XingAo Liu ◽  
WenWen Hu ◽  
Jia Luo
Keyword(s):  
Gray Scale ◽  
Fuzzy Matching ◽  
Scale Difference ◽  
Quantum Images

A Semi-Automated Term Harmonization Pipeline Applied to Pulmonary Arterial Hypertension Clinical Trials

2021 ◽  
Author(s):  
Ryan J. Urbanowicz ◽  
John H. Holmes ◽  
Dina Appleby ◽  
Vanamala Narasimhan ◽  
Stephen Durborow ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
System Organ Class ◽  
High Level Group ◽  
Fuzzy Matching ◽  
Pulmonary Arterial ◽  
High Level ◽  
Level Group ◽  
System Organ

Abstract Objective Data harmonization is essential to integrate individual participant data from multiple sites, time periods, and trials for meta-analysis. The process of mapping terms and phrases to an ontology is complicated by typographic errors, abbreviations, truncation, and plurality. We sought to harmonize medical history (MH) and adverse events (AE) term records across 21 randomized clinical trials in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension. Methods We developed and applied a semi-automated harmonization pipeline for use with domain-expert annotators to resolve ambiguous term mappings using exact and fuzzy matching. We summarized MH and AE term mapping success, including map quality measures, and imputation of a generalizing term hierarchy as defined by the applied Medical Dictionary for Regulatory Activities (MedDRA) ontology standard. Results Over 99.6% of both MH (N = 37,105) and AE (N = 58,170) records were successfully mapped to MedDRA low-level terms. Automated exact matching accounted for 74.9% of MH and 85.5% of AE mappings. Term recommendations from fuzzy matching in the pipeline facilitated annotator mapping of the remaining 24.9% of MH and 13.8% of AE records. Imputation of the generalized MedDRA term hierarchy was unambiguous in 85.2% of high-level terms, 99.4% of high-level group terms, and 99.5% of system organ class in MH, and 75% of high-level terms, 98.3% of high-level group terms, and 98.4% of system organ class in AE. Conclusion This pipeline dramatically reduced the burden of manual annotation for MH and AE term harmonization and could be adapted to other data integration efforts.

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