Information Bounds for Automated Object Recognition

2006 ◽  
Author(s):  
Ulf Grenander ◽  
Michael I. Miller
Keyword(s):  
Object Recognition ◽  
Mutual Information ◽  
Mean Squared Error ◽  
Rigid Object ◽  
Matrix Groups ◽  
Dimensional Matrix ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Low Dimensional

Examine information bounds for understanding rigid object recognition involving the low-dimensional matrix groups. Minimum-mean-squared error bonds and mutual information bounds are derived for recognition and identification.

Estimation Bounds for Automated Object Recognition

2006 ◽  
Author(s):  
Ulf Grenander ◽  
Michael I. Miller
Keyword(s):  
Object Recognition ◽  
Error Bounds ◽  
Mean Squared Error ◽  
Rigid Object ◽  
Matrix Groups ◽  
Dimensional Matrix ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Low Dimensional

Thus far we have only studied representations of the source. Now we add the channel, pushing us into the frameworks of estimate then examine estimation bounds for understanding rigid object recognition involving the low-dimensional matrix groups. Minimum-mean-squared error bounds are derived for recognition and identification.

scholarly journals Attack-resilient minimum mean-squared error estimation

2014 ◽  
Author(s):  
James Weimer ◽  
Nicola Bezzo ◽  
Miroslav Pajic ◽  
Oleg Sokolsky ◽  
Insup Lee
Keyword(s):  
Error Estimation ◽  
Mean Squared Error ◽  
Minimum Mean Squared Error ◽  
Squared Error

scholarly journals Target and Equalizer Design for High-Density Bit-Patterned Media Recording

1970 ◽  
Vol 6 (2) ◽  
pp. 128-135
Author(s):  
Santi Koonkarnkhai ◽  
Phongsak Keeratiwintakorn ◽  
Piya Kovintavewat
Keyword(s):  
Partial Response ◽  
Mean Squared Error ◽  
Bit Patterned Media ◽  
Patterned Media ◽  
Media Noise ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Cross Track ◽  
Partial Response Maximum Likelihood ◽  
Better Than

In bit-patterned media recording (BPMR) channels, the inter-track interference (ITI) is extremely severe at ultra high areal densities, which significantly degrades the system performance. The partial-response maximum-likelihood (PRML) technique that uses an one-dimensional (1D) partial response target might not be able to cope with this severe ITI, especially in the presence of media noise and track mis-registration (TMR). This paper describes the target and equalizer design for highdensity BPMR channels. Specifically, we proposes a two-dimensional (2D) cross-track asymmetric target, based on a minimum mean-squared error (MMSE) approach, to combat media noise and TMR. Results indicate that the proposed 2D target performs better than the previously proposed 2D targets, especially when media noise and TMR is severe.

Two-Run Genetic Programming for Predicting Slump Flow of Concrete

2014 ◽  
Vol 590 ◽  
pp. 321-325
Author(s):  
Li Chen ◽  
Chang Huan Kou ◽  
Kuan Ting Chen ◽  
Shih Wei Ma
Keyword(s):  
Nonlinear Systems ◽  
Genetic Programming ◽  
High Performance ◽  
Mean Squared Error ◽  
High Performance Concrete ◽  
Local Optimum ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Slump Flow ◽  
Low Dimensional

A two-run genetic programming (GP) is proposed to estimate the slump flow of high-performance concrete (HPC) using several significant concrete ingredients in this study. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover relationships between nonlinear systems. Basic-GP usually suffers from premature convergence, which cannot acquire satisfying solutions and show satisfied performance only on low dimensional problems. Therefore it was improved by an automatically incremental procedure to improve the search ability and avoid local optimum. The results demonstrated that two-run GP generates an accurate formula through and has 7.5 % improvement on root mean squared error (RMSE) for predicting the slump flow of HPC than Basic-GP.

scholarly journals New robust iterative minimum mean squared error-based interference alignment algorithm

2014 ◽  
Vol 2014 (2) ◽  
pp. 49-50 ◽  
Author(s):  
Sara Teodoro ◽  
Adão Silva ◽  
Rui Dinis ◽  
Daniel Castanheira ◽  
Atílio Gameiro
Keyword(s):  
Mean Squared Error ◽  
Interference Alignment ◽  
Alignment Algorithm ◽  
Minimum Mean Squared Error ◽  
Squared Error
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