DocumentCode :
117590
Title :
Machine learning plug-ins for GNU Radio Companion
Author :
Anil, R. ; Danymol, R. ; Gawande, Harsha ; Gandhiraj, R.
Author_Institution :
Centre for Excellence in Comput. Eng. & Networking, Amrita VishwaVidyapeetham, Coimbatore, India
fYear :
2014
fDate :
6-8 March 2014
Firstpage :
1
Lastpage :
5
Abstract :
This paper gives an insight about how to create classifier plug-ins (signal processing blocks) using hard-code input for GNU Radio Companion (GRC). GNU Radio Companion is an open source Visual programming language for any real time signal processing applications. At present there is no classifier block available inside this GRC tool. Here we are introducing a low cost classifier which utilizes the basic machine learning algorithms:linear regression and logistic regression. The creation of classifier plug-ins in an open source software enables easy manipulation of real time classification problems during the transmission and reception of signals in Software Defined Radios. So this workdescribes the development of signal processing block that can be done by changing the Python code and C++ codes of the `gr-modtool´ package. It is highly cost effective and with great potential since GNU Radio software is open source and free.
Keywords :
C++ language; learning (artificial intelligence); public domain software; regression analysis; signal classification; software radio; telecommunication computing; visual programming; C++ codes; GNU Radio Companion; GNU radio software; GRC tool; Python code; classifier plug-ins; grmodtool package; hard-code input; linear regression; logistic regression; machine learning plug-ins; open source software; open source visual programming language; real time signal processing applications; software defined radios; Linear regression; Logistics; Real-time systems; Signal processing; Signal processing algorithms; Software; Software radio; Gradient Descent method; Linear Regression; Logistic Regression; Machine Learning; Real Time Classification; Signal Processing Blocks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on
Conference_Location :
Coimbatore
Type :
conf
DOI :
10.1109/ICGCCEE.2014.6922250
Filename :
6922250
Link To Document :
بازگشت