The how and why of electronic noses
IEEE Spectrum
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computing and Applications
Application of the Gaussian mixture model to drug dissolution profiles prediction
Neural Computing and Applications
On transforming statistical models for non-frontal face verification
Pattern Recognition
A genetic classification method for speaker recognition
Engineering Applications of Artificial Intelligence
User authentication via adapted statistical models of face images
IEEE Transactions on Signal Processing
Bayesian classification for data from the same unknown class
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A compact 3D VLSI classifier using bagging threshold network ensembles
IEEE Transactions on Neural Networks
Bandwidth adaptive hardware architecture of K-Means clustering for video analysis
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
FPGA implementation for GMM-based speaker identification
International Journal of Reconfigurable Computing - Special issue on selected papers from the southern programmable logic conference (SPL2010)
Fast Algorithm and Efficient Implementation of GMM-Based Pattern Classifiers
Journal of Signal Processing Systems
FPGA-based architecture for real time segmentation and denoising of HD video
Journal of Real-Time Image Processing
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Gaussian mixture models (GMM)-based classifiers have shown increased attention in many pattern recognition applications. Improved performances have been demonstrated in many applications, but using such classifiers can require large storage and complex processing units due to exponential calculations and a large number of coefficients involved. This poses a serious problem for portable real-time pattern recognition applications. In this paper, first the performance of GMM and its hardware complexity are analyzed and compared with a number of benchmark algorithms. Next, an efficient digital hardware implementation is proposed. A number of design strategies are proposed in order to achieve the best possible tradeoffs between circuit complexity and real-time processing. First, a serial-parallel vector-matrix multiplier combined with an efficient pipelining technique is used. A novel exponential calculation circuit based on a linear piecewise approximation is proposed to reduce hardware complexity. The precision requirement of the GMM parameters in our classifier are also studied for various classification problems. The proposed hardware implementation features programmability and flexibility offering the possibility to use the proposed architecture for different applications with different topologies and precision requirements. To validate the proposed approach, a prototype was implemented in 0.25-µm CMOS technology and its operation was successfully tested for gas identification application.