Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kavitha, K. R.
- Energy Level Determination and Performance Analysis of Quantum Dot Photo Detector
Abstract Views :171 |
PDF Views:0
Authors
Affiliations
1 Mahendra Engineering College, IN
2 Department of Electronics and Communication Engineering, Sona College of Technology, IN
1 Mahendra Engineering College, IN
2 Department of Electronics and Communication Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 4, No 3 (2013), Pagination: 766-777Abstract
The theoretical estimation of dark and illumination characteristics of InGaAs quantum dot photo detector is developed and presented in this paper. The exact potential and energy profile of the Quantum Dot is computed by obtaining the solution of 3D Poisson and Schrodinger equations using Homotopy analysis. The dark current, photo current, responsivity, detectivity and efficiency of the model are calculated by considering the structural parameters Quantum Dot density, applied voltage, length of quantum dot array, number of quantum dot array, light intensity and temperature. The results obtained show that the dark current and photo current are strongly influenced by Quantum Dot density and applied voltage. The developed model is purely physics based one and overcomes the limitations of the existing analytical models. The model is validated by comparing the results obtained with the existing models.Keywords
Quantum Dots, Poisson Equation, Homotopy Analysis, Dark Current, Photo Current.
- Wear Rate Analysis of Ceramic Coated Brake Lining Material
Abstract Views :251 |
PDF Views:115
Authors
K. R. Kavitha
1,
S. Prakash
1
Affiliations
1 Dept. of Automobile Engg., Sathyabama University, Chennai, IN
1 Dept. of Automobile Engg., Sathyabama University, Chennai, IN
Source
International Journal of Vehicle Structures and Systems, Vol 9, No 4 (2017), Pagination: 238-240Abstract
Brake lining is a layer of hard material attached to a brake shoe or brake pad to increase friction against the drum or disc. Asbestos, which was the conventional brake material, has an optimal performance in wear rate and thermal resistance but due to serious health-related hazards, asbestos became obsolete. This led to the development of eco-friendly brake materials. Lot of research has been going on with Non-Asbestos Organic materials (NAO) including non-metallic, semi-metallic, fully metallic and ceramic materials. Among that, Ceramic materials are nowadays popularly used as brake friction materials. This is because that the ceramic material possesses high strength and hardness, superior wear and abrasive resistance, withstands high temperature and thermal shock. In this paper the effect of ceramic coating on asbestos brake lining is investigated. The role of ceramic abrasives including Alumina (Al2O2) and Zirconium oxide (ZrO2) as a ceramic coating on the surface of asbestos brake lining has been carried out experimentally. The ceramic material was deposited on the layer of asbestos brake lining by plasma coating process. Several samples were prepared with different thickness (25μ, 35μ and 45μ) and wear characteristic were analyzed using abrasive wheel testing machine which is commonly used to test the abrasive resistance of solid materials. The results were compared with the standard sample (asbestos brake lining) and it was found that the ZrO2 coating on the surface of the asbestos brake lining of 45 microns thickness coating has shown reduced wear than the other.Keywords
Ceramic Material, Wear Test, Friction Properties, Brake Lining.Full Text
References
- I. Taymaz. 2007. The effect of thermal barrier coatings on diesel engine performance, Surface and Coatings Tech., 201(9-11), 5249-5252. https://doi.org/10.1016/j.surfcoat.2006.07.123.
- K.W. Hee and P. Filip. 2005. Performance of ceramic enhanced phenolic matrix brake lining materials for automotive brake linings, Wear, 259(7-12), 1088-1096. https://doi.org/10.1016/j.wear.2005.02.083.
- A. Posmyk. 2003. Influence of material properties on the wear of composite coatings, Wear, 254(5-6), 399-407. https://doi.org/10.1016/S0043-1648(03)00130-3.
- S. Yugeswaran, V. Selvarajan, D. Seo and K. Ogawa. 2008. Effect of critical plasma spray parameter on properties of hollow cathode plasma sprayed alumina coatings, Surface Coatings and Tech., 203(1-2), 129-136. https://doi.org/10.1016/j.surfcoat.2008.08.030.
- E. Pfender. 1994. Plasma jet behavior and modelling associated with the plasma spray process, Thin Solid Films, 238(2), 228-241. https://doi.org/10.1016/0040-6090(94)90060-4.
- P. Fauchasis and A. Vardelle. 2000. Heat, mass and momentum transfer in coating formation by plasma spraying, 39(9-11), 852-870.
- P. Filip, L. Koverik and M. Wright. 1997. Automotive Brake Lining Characterization, Proc. 15th Annual SAE Brake Colloquium, SAE Int., 41-61. https://doi.org/10.4271/973024.
- L. Gorjan, M. Boretius, G. Blugan, F. Gili, D. Mangherini, X. Lizzarralde, M. Ferraris, T. Graule, A. Lgartua, G. Mendoza and J. Kuebler. 2016. Ceramic protection plates brazed to aluminum brake discs, 42(14), 15739-15746.
- M. Nakada. 1994. Trends in engine technology and tribology, Tribology Int., 27(1), 3-8. https://doi.org/10.1016/0301-679X(94)90056-6.
- A. Posmyk. 2003. Influence of material properties on the wear of composite coatings, Wear, 254(5-6), 399-407. https://doi.org/10.1016/S0043-1648(03)00130-3.
- Segmentation, Feature Extraction and Classification of Brain Tumor Through MRI Image
Abstract Views :252 |
PDF Views:1
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 1 (2021), Pagination: 2517-2524Abstract
In biomedical, tumor detection and removal is one of the major medical issue. Brain tumor is a disease of the brain where cancer cells arise in the brain tissue to form a mass of cancer tissue that interferes with brain functions such as manage muscle, sense, memory and other body functions. Tumors composed of cancerous cells are called Malignant tumors and those composed of non-cancerous cells are called Benign tumors. There are so many ways to diagnose tumor in brain include Neurologic exam, MRI, CT scan, Angiogram, Spinal tap and Biopsy. Medical imaging has tremendous advantage in diagnosis of the disease where Magnetic Resonance Imaging plays an important role. This paper aims to enhance the accuracy level in the detection of brain tumor and provides better performance than existing method based on high accuracy rate and low computing time. The process of tumor detection comprises three steps (i) Segmentation (ii) Feature extraction (iii) Classification. Various algorithms are developed for image processing in which we take Histogram thresholding for image segmentation and Support Vector Machine (SVM) for classify the image as Benign or Malignant.Keywords
Brain Tumor, MRI, Histogram Thresholding, Support Vector Machine.References
- Akansha Singh and Krishna Kant Singh, “A Study of Image Segmentation Algorithms for Different Types of Images”, International Journal of Computer Science, Vol. 7, No. 5, pp. 414-417,2010.
- Mansur Rozmin, Chhaya Suratwala and Vandana Shah, “Implementation of Hard C-Means Clustering Algorithm for Medical Image Segmentation”, Journal of Information Knowledge and Research in Electronics and Communication Engineering, Vol. 2, No. 2, pp. 436-440, 2013.
- Rajesh Kumar Rai and Trimbak R. Sontakke, “Implementation of Image Denoising using Thresholding Techniques”, International Journal of Computer Technology and Electronics Engineering, Vol. 1, No. 2, pp 6-10, 2017.
- T. Kalaiselvi, S. Vijayalakshmi and K. Somasundara, “Segmentation of Brain Portion from MRI of Head Scans using Kmeans Cluster”, International Journal of Computational Intelligence and Informatics, Vol. 1, No. 1, pp. 75-79,2011.
- S.S. Mankikar, “A Novel Hybrid Approach using K means Clustering and Threshold Filter for Brain Tumor Detection”, International Journal of Computer Trends and Technology, Vol. 4, No. 3, pp. 206-209, 2013.
- M.C. Jobin Christ and R.M.S. Parvathi, “Segmentation of Medical Image using Clustering and Watershed Algorithms”, American Journal of Applied Sciences, Vol. 8, pp. 1349-1352, 2011.
- Manali Patil, Prachi Kshirsagar, Samata Prabhu, Sonal Patil and Sunilka Patil, “Brain Tumor Identification using K-Means Clustering”, International Journal of Engineering Trends and Technology, Vol. 4, No. 3, pp. 354-357,2013.
- P. Dhanalakshmi and T.Kanimozhi, “Automatic Segmentation of Brain Tumor using K-Means Clustering and its Area Calculation”, International Journal of Advanced Electrical and Electronics Engineering, Vol. 2, No. 2, pp. 130-134, 2013.
- Sanjay Kumar Dubey and Soumi Ghosh, “Comparative Analysis of K-Means and Fuzzy C Means Algorithms”, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 4, pp 35-39, 2013.
- M. Masroor Ahmed and Dzulkifli Bin Mohamad, “Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model”, International Journal of Image Processing, Vol. 2, No. 1, pp. 27-34, 2008.
- Anam Mustaqeem, Ali Javed and Tehseen Fatima, “An Efficient Brain Tumor Detection Algorithm using Watershed and Thresholding Based Segmentation”, International Journal of Image, Graphics and Signal Processing, Vol. 10, No. 5, pp. 34-39, 2012.
- Afnan M. Alhassan and Wan Mohd Nazmee Wan Zainon, “Algorithm With fuzzy C-Ordered Means (BAFCOM) Clustering Segmentation and Enhanced Capsule Networks (ECN) for Brain Cancer MRI Images Classification, Digital Object Identifier”, IEEE Access, Vol. 8, pp. 201741-201751, 2020.
- Abdu Gumaei, Mohammad Mehedi Hassan, Md Rafiul Hassan, Abdulhameed Alelaiwi and Giancarlo Fortino, “A Hybrid Feature Extraction Method with Regularized Extreme Learning Machine for Brain Tumor Classification, Digital Object Identifier”, IEEE Access, Vol. 7, pp. 36266-36273, 2019.
- Tamjid Imtiaz, Shahriar Rifat, Shaikh Anowarul Fattah and Khan A. Wahid, “Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted from 3D MR Images, Digital Object Identifier”, IEEE Access, Vol. 8, pp. 25335-25349, 2019.
- Mahnoor Ali, Syed Omer Gilani, Asim Waris, Kashan Zafar and Mohsin Jamil, “Brain Tumor Image Segmentation using Deep Networks”, IEEE Access, Vol. 8, pp. 153589-153598, 2020.
- Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama and Hideaki Hayashi, “Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection”, IEEE Access, Vol. 7, pp. 53589-153598, 2019.
- Khan Muhammad, Salman Khan, Javier Del Ser and Victor Hugo C. De Albuquerque, “Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 2, pp. 507-522, 2020.
- Hossam H. Sultan, Nancy M. Salem and Walid Al-Atabany, “Multi-Classification of Brain Tumor Images using Deep Neural Network”, Digital Object Identifier”, IEEE Access, Vol. 7, pp. 69215-69225, 2019.
- P. Mohamed Shakeel, Tarek E. El. Tobely, Haytham Al-Feel, Gunasekaran Manogaran and S. Baskar, “Neural Network Based Brain Tumor Detection using Wireless Infrared Imaging Sensor”, IEEE Access, Vol. 7, pp. 5577-5588, 2019.