Aliasing Probability in Multiple Input Signature Registers under Q-ary Symmetric Errors Model

2008 ◽  
Author(s):  
Zheng Wenrong ◽  
Wang Shuzong ◽  
Ye Huijuan
Keyword(s):  
Multiple Input ◽  
Aliasing Probability

Aliasing probability for multiple input signature analyzer

1990 ◽  
Vol 39 (4) ◽  
pp. 586-591 ◽  
Author(s):  
D.K. Pradhan ◽  
S.K. Gupta ◽  
M.G. Karpovsky
Keyword(s):  
Signature Analyzer ◽  
Multiple Input ◽  
Aliasing Probability

scholarly journals Closed Form Aliasing Probability For Q-ary Symmetric Errors

VLSI Design ◽  
1996 ◽  
Vol 4 (3) ◽  
pp. 199-205
Author(s):  
Geetani Edirisooriya
Keyword(s):  
Closed Form ◽  
Circuit Complexity ◽  
Propagation Delay ◽  
Linear Feedback ◽  
Closed Form Expression ◽  
Primitive Polynomials ◽  
Test Circuit ◽  
Signature Analyzer ◽  
Multiple Input ◽  
Aliasing Probability

In Built-In Self-Test (BIST) techniques, test data reduction can be achieved using Linear Feedback Shift Registers (LFSRs). A faulty circuit may escape detection due to loss of information inherent to data compaction schemes. This is referred to as aliasing. The probability of aliasing in Multiple-Input Shift-Registers (MISRs) has been studied under various bit error models. By modeling the signature analyzer as a Markov process we show that the closed form expression derived for aliasing probability previously, for MISRs with primitive polynomials under q-ary symmetric error model holds for all MISRs irrespective of their feedback polynomials and for group cellular automata signature analyzers as well. If the erroneous behaviour of a circuit can be modelled with q-ary symmetric errors, then the test circuit complexity and propagation delay associated with the signature analyzer can be minimized by using a set of m single bit LFSRs without increasing the probability of aliasing.

An Effective Model of the Overshooting Effect for Multiple-Input Gates in Nanometer Technologies

2014 ◽  
Vol E97.A (5) ◽  
pp. 1059-1074
Author(s):  
Li DING ◽  
Zhangcai HUANG ◽  
Atsushi KUROKAWA ◽  
Jing WANG ◽  
Yasuaki INOUE
Keyword(s):  
Effective Model ◽  
Multiple Input

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Transfer Function Model ◽  
Function Model ◽  
Jones Index ◽  
Multiple Input ◽  
Identification Stage ◽  
Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

Sign in / Sign up

Export Citation Format

Share Document