Refine your search
Collections
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
Shanmuga Priya, D.
- Implementation of Transmitter and Receiver Architecture for Physical Hybrid Indicator Channel of LTE-Advanced Using Partial Reconfiguration in ML605 Virtex-6 Device
Abstract Views :169 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, IN
2 Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, IN
1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, IN
2 Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, IN
Source
ICTACT Journal on Communication Technology, Vol 5, No 3 (2014), Pagination: 987-994Abstract
LTE-A (Long Term Evolution-Advanced) is the fourth generation technology to increase the speed of wireless data network. The LTE-A Physical layer provides both data and control information between an enhanced base station and mobile user equipment which is quite complex and consists of a mixture of technologies. Since there is requirement for more resources to accommodate all the channels in a single FPGA, Partial Reconfiguration (PR) technique is introduced to configure the total hardware into sub modules that configure and operate in different instants of time. PR enables a part of FPGA to be reconfigured, while the rest continues to function without any interruptions and reduces the hardware resource power and fabric area. This work proposes the realization of transmitter and receiver architecture of Physical Hybrid Indicator Channel (PHICH) channel for LTE-A using partial reconfiguration on xc6vlx240tff1156-1 FPGA. The receiver architecture for PHICH is to report the correct reception of uplink user data to the User Equipment (UE) in the form of Acknowledgment (ACK), or Negative ACK (NACK) in a 1 millisecond duration sub-frame of Long Term Evolution (LTE) System. The modules for the different diversities are reconfigured based on the control signals from the transmitter.Keywords
Diversity, LTE, PHICH, Partial Reconfiguration.- Multi Disease Prediction Using Data Mining Techniques
Abstract Views :218 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore, Tamil Nadu, IN
2 Department of Information Technology, Karpagam University, Coimbatore, Tamil Nadu, IN
1 Department of Computer Science, Karpagam University, Coimbatore, Tamil Nadu, IN
2 Department of Information Technology, Karpagam University, Coimbatore, Tamil Nadu, IN
Source
International Journal of System & Software Engineering, Vol 4, No 2 (2016), Pagination: 12-14Abstract
Data mining techniques are used for a variety of applications. In healthcare industry, data mining plays an important role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance. This paper analyzes data mining techniques which can be used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrate on predicting heart disease, Diabetes and Breast cancer etc.Keywords
Data Mining, Classification, Naive Bayes, J48, Decision Tree.References
- http://www.mayoclinic.org/diseases-conditions/heart-disease/basics/definition/con-20034056
- http://www.idf.org/about-diabetes
- http://www.cancer.org/cancer/breastcancer/detailedguide/breast-cancer-what-is-breast-cancer
- Kumara, M., Vohra, R., Arora, A. (2014). Prediction of diabetes using Bayesian network. International Journal of Computer Science and Information Technologies, 5(4), 5174-5178.
- Thirumal, P. C., & Nagarajan, N. (2015). Utilization of data mining techniques for diagnosis of diabetes mellitus - A case study. ARPN Journal of Engineering and Applied Sciences, January, 10(1), 8-13.
- Gomathi, K. (2012). An empirical study on breast cancer using data mining techniques. International Journal of Research in Computer Application & Management, July, 2(7), 97-102.
- Witten, H. I., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, (2nd Ed), Morgan Kaufmann Publishers.
- Witten, I. H. & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques, (2nd Ed.) San Fransisco: Morgan Kaufmann.
- WEKA: Data Mining Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ml/weka/
- Delen, D., Walker, G., & Kadam, A. (2005). Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine, June, 34(2), 113-127.