Monopole Antenna for Indoor MIMO

SKU: PAN_ANT_016 Category: Tags: , , ,

Description

Monopole Antenna for Indoor MIMO

To measure the calorie of food, which are varied depending on its ingredients and volume in each cooking time, it is required to calculate the calories of food before consumption. Based on nutrition knowledge, ingredients that are components of food naturally have different calories. This paper proposes a method of ingredient-based food calorie estimation using nutrition knowledge and information. In this method, an image of the food is first recognized as a type of food, and ingredients of the recognized food are retrieved from the database with their nutrition knowledge and pattern of brightness and thermal images. Simultaneously, the image is segmented into boundaries of ingredient candidates, and all boundaries are then classified into ingredients using neural networks. Monopole Antenna for Indoor MIMO

EXISTING SYSTEM:

  • SVM Classifier
  • Random tree classifier
  • K-means clustering

DISADVANTAGES:

  • Inaccurate results
  • Need a large number of training datasets
  • Performance is less

PROPOSED SYSTEM:

  • Pre-processing
  • DWT
  • GLCM Feature extraction
  • Neural networks

ADVANTAGES:

  1. No need for manual interaction
  2. Accurate results

Software Requirements:-

  • python and above 3.0
  • Deep learning toolbox

REFERENCES:

[1] M. Bosch, F. Zhu, N. Khanna, C. Boushey, and E. Delp. Combining global and local features for food identification dietary assessment. image processing (ICIP), 201118th IEEE International Conference on, pages 1789? 1792.IEEE, 2011.

[2] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines.ACM Transactions on Intelligent Systems and Technology, 2:27:1?27:27, 2011. Software available athttp://www.csie.ntu.edu.tw/ chain/libsvm.

[3] M. Chen, K. Dhingra, W. Wu, L. Yang, R. Sukthankar, andJ. Yang. Paid: Pittsburgh fast-food image dataset. in-age Processing (ICIP), 2009 16th IEEE International Conference on, pages 289? 292. IEEE, 2009.

[4] G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray.Visual categorization with bags of key points. InWorkshopon statistical learning in computer vision, ECCV, volume 1, page 22, 2004.

[5] H. Hoashi, T. Joutou, and K. Yanai. Image recognition of 85food categories by feature fusion. multimedia (ISM), 2010IEEE International Symposium on, pages 296? 301. IEEE,2010.

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