Search Results

1 - 10 of 771 items :

  • "pattern recognition" x
Clear All
New Digital Architecture of CNN for Pattern Recognition

New Digital Architecture of CNN for Pattern Recognition

The paper deals with the design of a new digital CNN (Cellular Neural Network) architecture for pattern recognition. The main parameters of the new design were the area consumption of the chip and the speed of calculation in one iteration. The CNN was designed as a digital synchronous circuit. The largest area of the chip belongs to the multiplication unit. In the new architecture we replaced the parallel multiplication unit by a simple AND gate performing serial multiplication. The natural property of this method of multiplication is rounding. We verified some basic properties of the proposed CNN such as edge detection, filling of the edges and noise removing. At the end we compared the designed network with other two CNNs. The new architecture allows to save till 86% gates in comparison with CNN with parallel multipliers.

Open access
Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Abstract

Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.

Open access
New Mixed Kernel Functions of SVM Used in Pattern Recognition

References 1. Megri, A. C., I. El Naqa. Prediction of the Thermal Comfort Indices Using Improved Support Vector Machine Classifiers and Nonlinear Kernel Functions. - Indoor and Built Environment, 2014, 1420326X14539693. 2. Ozer, S., C. H. Chen, H. A. Cirpan. A Set of New Chebyshev Kernel Functions for Support Vector Machine Pattern Classification. - Pattern Recognition, Vol. 44, 2011, No 7, pp. 1435-1447. 3. Yoon, C., D. Kim, W. Jung et al. AppScope: Application Energy Metering Framework for Android Smartphone

Open access
Optimization of Artificial Neural Networks with Genetic Algorithms for Biometric Pattern Recognition

Abstract

The process of pattern recognition in the biometrics is particularly important. The patterns can differ from each other a lot, and even the samples can be significantly different from the templates. The Artificial Neural Networks can be applied as a universal approximator to recognize the patterns with more flexibility. However the topology of the networks determines the processing time and complexity of the hardware of the physical environments. The Genetic Algorithms can be used with success in optimization problems like in this situation, the topology of the neural network is more optimal if we apply the Genetic Algorithms. This study introduce an algorithm in which a tailor made algorithm correcting the topology to enhance the effectiveness of the process.

Open access
Closest Paths in Graph Drawings under an Elastic Metric

References Bernal, J., Dŏgan, G. and Hagwood, C.R. (2016). Fast dynamic programming for elastic registration of curves, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA, pp. 1066-1073, DOI: 10.1109/CVPRW.2016.137. Cootes, T.F., Edwards, G.J. and Taylor, C.J. (2001). Active appearance models, IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6): 681-685, DOI: 10.1109/34.927467. Cootes, T.F., Taylor, C.J., Cooper, D.H. and Graham, J. (1995

Open access
Review of Printed Fabric Pattern Segmentation Analysis and Application

] Hann, M. (2003). Conceptual developments in the analysis of patterns part two: the application of the principles of symmetry. [15] Hann, M. (2003). Conceptual developments in the analysis of patterns part one: the identification of fundamental geometrical elements. [16] Bezdek, J. C. (1981). Objective function clustering, in Pattern recognition with fuzzy objective function algorithms. Springer, 43-93. [17] Bezdek, J. C., Hall, L., Clarke, L. P. (1993). Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20

Open access
Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

References Auger F., Flandrin P., Goncalves P. and Lemoine O. (1996). Time-Frequency Toolbox for Matlab , CNRS, Rice University, Houston, TX http://iut-saint-nazaire.univ-nantes.fr/~{}auger/tftb.html Basri R., Costa L., Geiger D. and Jacobs D. (1998). Determining the similarity of deformable shapes, Vision Research   38 (15-16): 2365-2385. Basseville M. (1989). Distance measures for signal processing and pattern recognition, Signal Processing   35 (3): 349

Open access
Recognition of Thermal Images of Direct Current Motor with Application of Area Perimeter Vector and Bayes Classifier

. Eksploatacja i Niezawodnosc–Maintenance and Reliability , 16 (3), 377-382. [39] Kundegorski, M., Jackson, P.J.B., Ziolko, B. (2014). Two-microphone dereverberation for automatic speech recognition of Polish. Archives of Acoustics , 39 (3), 411-420. [40] Murty, M.N., Devi, V.S. (2011). Bayes classifier. Pattern Recognition: An Algorithmic Approach. Springer, 86-102. [41] Krolczyk, G.M., Krolczyk, J.B., Legutko, S., Hunjet, A. (2014). Effect of the disc processing technology on the vibration level of the chipper during operations. Tehnicki Vjesnik

Open access
New Method of Visibility Network and Statistical Pattern Network Recognition Usage in Terrain Surfaces

, K. (2016): Definition of appropriate geodetic datum using robust statistical. Geodetski vestnik , 60(2), pp. 212–226. [11] Bishop, C.M. (2006): Pattern Recognition and Machine Learning . New York: Springer-Verlag, 738 p. [12] Babič, M. (2014): Analiza kaljenih materialov s pomočjo fraktalne geometrije . Ph. D. Thesis. Maribor: University of Maribor 2014; 167 p. [13] De Wouter, N., Mrvar, A., Batagelj, V. (2005): Exploratory Social Network Analysis with Pajek . New York: Cambridge University Press; 334 p. [14] Babič, M., Kokol, P., Guid

Open access
Perceptron Architecture Ensuring Pattern Description Compactness

://www.research.att.com/~njas/sequences/A002884 Encyclopedia of Integer Sequences. "Number of regular n x n matrices with rational entries" equal to 0 or 1. http://www.research.att.com/~njas/sequences/A055165 Cover T.M. "Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition", IEEE Transactions on Electronic Computers, Vol. EC-14, 1965, p.326-334. Joseph R.D. "The number of orthants in n-space intersected by an s-dimensional subspace", Technical Memo 8, Project PARA

Open access