Reduced fusion systems over 2-groups of sectional rank at most 4

2016 ◽  
Vol 239 (1131) ◽  
pp. 0-0 ◽  
Author(s):  
Bob Oliver
Keyword(s):  
Fusion Systems ◽  
Sectional Rank

scholarly journals Fusion systems on p-groups of sectional rank 3

2019 ◽  
Vol 537 ◽  
pp. 39-78
Author(s):  
Valentina Grazian
Keyword(s):  
Fusion Systems ◽  
Sectional Rank

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

scholarly journals Information Quality Assessment for Data Fusion Systems

Data ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 60
Author(s):  
Miguel A. Becerra ◽  
Catalina Tobón ◽  
Andrés Eduardo Castro-Ospina ◽  
Diego H. Peluffo-Ordóñez
Keyword(s):  
Data Fusion ◽  
Information Quality ◽  
Engineering Process ◽  
Comprehensive Description ◽  
Research Issues ◽  
Fusion Systems ◽  
General Data ◽  
Fusion Engineering ◽  
And Performance ◽  
Key Aspects

This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered.

Heavy Ion Fusion Systems Assessment - An Overview

1986 ◽  
Vol 10 (3P2B) ◽  
pp. 1437-1437
Author(s):  
Lester M. Waganer ◽  
Daniel E. Driemeyer ◽  
David S. Zuckerman ◽  
Kenneth W. Billman
Keyword(s):  
Heavy Ion ◽  
Fusion Systems ◽  
Heavy Ion Fusion

A Critical Review of Fusion Systems for Radiolytic Conversion of Inorganics to Gaseous Fuels

1982 ◽  
Vol 2 (2) ◽  
pp. 143-164 ◽  
Author(s):  
Mark A. Prelas ◽  
Jacob B. Romero ◽  
Earl F. Pearson
Keyword(s):  
Critical Review ◽  
Fusion Systems ◽  
Gaseous Fuels

Exploring the Role of Agent and Multi-Agent in Data Fusion Systems

2021 ◽  
Author(s):  
Shafiza Ariffin Kashinath ◽  
Salama A. Mostafa ◽  
Aida Mustapha ◽  
David Lim ◽  
Mohd Farhan Md Fudzee ◽  
...  
Keyword(s):  
Data Fusion ◽  
Fusion Systems ◽  
Multi Agent
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