Description

Monopole Antenna for Quad Band

• This is the method we find the motion of the particular object we going to draw or write the exact mean of that particular motion. To achieve this exact process we will go to use the same algorithm, that algorithm is Hidden Markov Algorithm. If we get motion in front of our sensor it will recognize by using this algorithm it generates and draws the exact mean of the motion. In the existing system of this concept, they use it to analyze the motion and draw approximate output on the screen. But our method we draw the exact motion and we also used this method for the virtual key generation and this output we show like 2-D trajectory and also we will recognize the word error of the exact drawing. We will analyze the 6-DOF (Degree Of Freedom) motion of the recognition. Monopole Antenna for Quad Band

EXISTING SYSTEM:

• SVM classifier
• K means clustering

DRAWBACKS:

• High Computational load
• Poor discriminatory power
• Less accuracy in classification

Monopole Antenna for Quad Band

PROPOSED SYSTEM:

• In this concept, we can use it to make a virtual keyboard also.
• The output shows like virtual reality.
• More than the accuracy of the key recognition.

Monopole Antenna for Quad Band

BLOCK DIAGRAM:

Monopole Antenna for Quad Band

APPLICATION:

• Desktop computer
• Mobile computer

SOFTWARE REQUIREMENTS:

• Open CV
• Python language

Monopole Antenna for Quad Band

REFERENCE:

[1] Ting, R., S. Chun-lin, and D. Jian, Handwritten character recognition using principal component analysis. MINI-MICRO Systems, 2005. 26(2): p. 289-292.

[2] Walid, R. and A. Lasfar. Handwritten digit recognition using sparse deep architectures. in Intelligent Systems: Theories and Applications (SITA-14), 2014 9th International Conference on. 2014. IEEE.

[3] Li, Z., et al. Handwritten digit recognition via active belief decision trees. in Control Conference (CCC), 2016 35th Chinese. 2016. IEEE.

[4] Schmidhuber, J., Deep learning in neural networks: An overview. Neural Networks, 2015. 61: p. 85-117.

[5] LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. Nature, 2015. 521(7553): p. 436-444.

[6] Hinton, G.E. and R.R. Salakhutdinov Reducing the dimensionality of data with neural networks. Science, 2006. 313(5786): p. 504-507.

[7] Yu, K., et al., Deep learning: yesterday, today, and tomorrow. Journal of Computer Research and Development, 2013. 50(9): p. 1799-1804.

[8] Sun, Z.-J., et al., Overview of deep learning. Jisuanji Yingyong Yanjiu, 2012. 29(8): p. 2806-2810.

[9] Bengio, Y., Learning deep architectures for AI. Foundations and trends? in Machine Learning, 2009. 2(1): p. 1-127.

[10] LeCun, Y., C. Cortes, and C.J. Burges, The MNIST database of handwritten digits. 1998.

[11] Bouchain, D., Character recognition using convolutional neural networks. Institute for Neural Information Processing, 2006. 2007.

[12] Hinton, G.E., S. Osindero, and Y.-W. , A fast learning algorithm for deep belief nets. Neural computation, 2006. 18(7): p. 1527-1554.

[13] Wu, M. and L. Chen. Image recognition is based on deep learning. in Chinese Automation Congress (CAC), 2015. 2015. IEEE.

[14] Fischer, A. and C. Igel. An introduction to restricted Boltzmann machines. in Iberoamerican Congress on Pattern Recognition. 2012. Springer.

[15] Ciregan, D., U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. 2012. IEEE.