Simplexity: A Hybrid Framework for Managing System Complexity

2020 ◽  
Author(s):  
Michael Reiss
Keyword(s):  
System Complexity ◽  
Hybrid Framework ◽  
Managing System

Tax Governance Issues: Managing System Complexity*

2012 ◽  
Vol 31 (1) ◽  
pp. 30-35 ◽  
Author(s):  
Chris Evans
Keyword(s):  
System Complexity ◽  
Managing System

Towards Designing FPGA-Based Systems by Refinement in B

2012 ◽  
pp. 92-112
Author(s):  
Sergey Ostroumov ◽  
Elena Troubitsyna ◽  
Linas Laibinis ◽  
Vyacheslav Kharchenko
Keyword(s):  
System Development ◽  
General Pattern ◽  
Smooth Transition ◽  
Formal Approach ◽  
System Complexity ◽  
Synchronous Systems ◽  
System Representation ◽  
Managing System

In this chapter, we propose a formal approach to designing FPGA-based systems. In particular, we introduce a general pattern for specifying synchronous systems and components as well as their typical interconnections. The proposed methodology for developing FPGA-based systems is based on the notion of refinement in the Event-B formalism. System development by refinement and proof-based verification provide the designers with powerful techniques for managing system complexity and achieving higher degree of system dependability. We aim at enabling a smooth transition from a formal Event-B specification to an implementable VHDL system representation. The proposed approach is illustrated by a case study – a development of an aircraft anti-icing system.

scholarly journals Managing System Complexity Through Integrated TD Design Tools

2021 ◽  
Author(s):  
Atila Ertas ◽  
Utku Gulbulak
Keyword(s):  
Design Tools ◽  
System Complexity ◽  
Managing System

ChartOCR: Data Extraction from Charts Images via a Deep Hybrid Framework

2021 ◽  
Author(s):  
Junyu Luo ◽  
Zekun Li ◽  
Jinpeng Wang ◽  
Chin-Yew Lin
Keyword(s):  
Data Extraction ◽  
Hybrid Framework

scholarly journals Photonic Reservoir Computer with Output Expansion for Unsupervized Parameter Drift Compensation

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 955
Author(s):  
Jaël Pauwels ◽  
Guy Van der Sande ◽  
Guy Verschaffelt ◽  
Serge Massar
Keyword(s):  
Electronic System ◽  
Nonlinear Functions ◽  
System Complexity ◽  
Trade Off ◽  
Parameter Drift ◽  
Output Layer ◽  
Drift Compensation ◽  
Output Neurons ◽  
Performance Gains

We present a method to improve the performance of a reservoir computer by keeping the reservoir fixed and increasing the number of output neurons. The additional neurons are nonlinear functions, typically chosen randomly, of the reservoir neurons. We demonstrate the interest of this expanded output layer on an experimental opto-electronic system subject to slow parameter drift which results in loss of performance. We can partially recover the lost performance by using the output layer expansion. The proposed scheme allows for a trade-off between performance gains and system complexity.

scholarly journals A Hybrid Framework for Effective Prediction of Online Streaming Data

2021 ◽  
Vol 1767 (1) ◽  
pp. 012016
Author(s):  
K Kanagaraj ◽  
S Geetha
Keyword(s):  
Streaming Data ◽  
Hybrid Framework ◽  
Online Streaming
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