Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
VLSI-Compatible Immplementations for Artificial Neural Networks
VLSI-Compatible Immplementations for Artificial Neural Networks
Learning on Silicon: Adaptive VLSI Neural Systems
Learning on Silicon: Adaptive VLSI Neural Systems
Neural Information Processing and VLSI
Neural Information Processing and VLSI
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Glove-Based Approach to Online Signature Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-line signature verification and forgery detection using fuzzy modeling
Pattern Recognition
CSFNN synapse and neuron design using current mode analog circuitry
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Signature verification using conic section function neural network
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Warping-Based Offline Signature Recognition
IEEE Transactions on Information Forensics and Security - Part 1
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In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.