An Automated Pattern Recognition Based Approach for Classification of Soiled Paper Currency Using Textural and Geometrical Features

2017 ◽  
Vol 45 (6) ◽  
pp. 20160213
Author(s):  
Z. Altaf ◽  
S. Farhan ◽  
M. Abuzar Fahiem
Keyword(s):  
Pattern Recognition ◽  
Paper Currency ◽  
Geometrical Features ◽  
Automated Pattern Recognition

GAIT ANALYSIS THROUGH FOOT PATTERN RECOGNITION FOR CHILDREN WITH CEREBRAL PALSY

1999 ◽  
Vol 03 (01) ◽  
pp. 71-81 ◽  
Author(s):  
Alice M. K. Wong ◽  
Chia-Ling Chen ◽  
Wei-Hsien Hong ◽  
Wen-Ko Chiou ◽  
Hsieh-Ching Chen ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Cerebral Palsy ◽  
Gait Analysis ◽  
Gait Patterns ◽  
Children With Cerebral Palsy ◽  
Automated Pattern Recognition ◽  
Age Range ◽  
Cyclic Characteristic

In this study, we simplified the analysis of kinetic gait data using pattern recognition. Gait patterns were studied in 42 spastic children with cerebral palsy (age range: 3 to 17 years old), and 24 age- and sex-matched children. Gait analysis was performed using the DynoGraphy (CDG) system (Infortronic, Holland). The foot enrollment and the role of the heel or forefoot were assessed to form the gaitline. The bipedal phase was examined using a cyclogram, which is a cyclic characteristic formed by the changing position of the application point of the resultant normal force on a vertical supporting horizontal plane during motion. Based on the pattern recognition, the gait patterns of the subjects could be classified into 4 different patterns in both the gaitline and the cyclogram. The classification of the gait was parallel to the clinical evaluation of cerebral palsy obtained based on Minear's classification of daily activity (p<0.05). The correlation between the gaitline and cyclogram was also highly significant (p<0.05). The results of this study suggest that an automated pattern recognition program might provide an additional method for comprehensive gait evaluation in children with cerebral palsy.

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

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