Error Estimation Model for Managing Embedded Software Development

2009 ◽  
pp. 181-190
Author(s):  
Yoshiyuki Anan ◽  
Kazunori Iwata ◽  
Toyoshiro Nakashima ◽  
Naohiro Ishii
Keyword(s):  
Software Development ◽  
Error Estimation ◽  
Embedded Software ◽  
Estimation Model

Application of Error Estimating Model to Control Quality in Embedded Software Development

2010 ◽  
Vol 130 (3) ◽  
pp. 496-502
Author(s):  
Yoshiyuki Anan ◽  
Toyoshiro Nakashima ◽  
Kazunori Iwata ◽  
Hiroshi Yonemitsu ◽  
Tetsu Yoshioka ◽  
...  
Keyword(s):  
Software Development ◽  
Embedded Software ◽  
Control Quality

Design Patterns Reuse for Real Time Embedded Software Development

2009 ◽  
Author(s):  
Gabriel de Souza Pereira Moreira ◽  
Denis Ávila Montini ◽  
Daniela América da Silva ◽  
Felipe Rafael Motta Cardoso ◽  
Luiz Alberto Vieira Dias ◽  
...  
Keyword(s):  
Software Development ◽  
Real Time ◽  
Design Patterns ◽  
Embedded Software

Successfully outsourcing embedded software development

Computer ◽  
2006 ◽  
Vol 39 (1) ◽  
pp. 55-61 ◽  
Author(s):  
J.W. Rottman
Keyword(s):  
Software Development ◽  
Embedded Software

Estimating Interval of the Number of Errors for Embedded Software Development Projects

2014 ◽  
Vol 2 (3) ◽  
pp. 40-50 ◽  
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakasima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Software Development ◽  
Previous Investigation ◽  
Interval Estimation ◽  
Embedded Software ◽  
Point Estimation ◽  
Support Vector ◽  
Development Projects ◽  
Artificial Neural Network Ann

Previous investigation focused on the prediction of total and errors for embedded software development projects using an artificial neural network (ANN). However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. This paper proposes a method for predicting the number of errors for embedded software development projects using interval estimation provided by a support vector machine and ANN.

scholarly journals SAR Observation Error Estimation Based on Maximum Relative Projection Matching

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Y. Zhang ◽  
B. P. Wang ◽  
Y. Fang ◽  
Z. X. Song
Keyword(s):  
Error Estimation ◽  
Estimation Method ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Reconstruction Method ◽  
Observation Error ◽  
Estimation Model ◽  
Reconstruction Process ◽  
Single Observation ◽  
Imaging Results

The existing sparse imaging observation error estimation methods are to usually estimate the error of each observation position by substituting the error parameters into the iterative reconstruction process, which has a huge calculation cost. In this paper, by analysing the relationship between imaging results of single-observation sampling data and error parameters, a SAR observation error estimation method based on maximum relative projection matching is proposed. First, the method estimates the precise position parameters of the reference position by the sparse reconstruction method of joint error parameters. Second, a relative error estimation model is constructed based on the maximum correlation of base-space projection. Finally, the accurate error parameters are estimated by the Broyden–Fletcher–Goldfarb–Shanno method. Simulation and measured data of microwave anechoic chambers show that, compared to the existing methods, the proposed method has higher estimation accuracy, lower noise sensitivity, and higher computational efficiency.

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