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IEEE Signal Processing Cup 2016 - Team Ravan

Abstract: 

Electric Network Frequency (ENF), the supply frequency in a power distribution network has been used as forensic analysis tool for over the past few decades. Since the fluctuation of the ENF signal differs from one power distributor to another, ENF signal can be used as a tool to identify the location where a particular signal has been recorded. Therefore this technology can be used as a powerful tool in forensic applications such as terrorist attacks, ransom demands etc.

First task of this project was to build a system which can extract the ENF signals from multimedia signals accurately and identify the location or power grid which it belonged to. For this entire process, accuracy and the reliability would be the main concern since these identification systems will be used in forensic applications.

Second task of the project was to build a hardware device which can record power signals from a standard power outlet. Recorded power signals were further analyzed and results were tabulated.

This report presents the design details for both software based grid identification system and hardware device which is used to record power signals. Designed grid identification system is presented as a MATLAB based Graphical User Interface (GUI). For the first task, the system has two main parts, train the system from given known signals using a machine learning algorithm and identify a given signal from and unknown power grid using the trained system. Each of this part contains three main steps for a given signal,
• Identifying the nominal frequency (50Hz or 60Hz).
• Extracting the ENF signal.
• Training /Classification.
Machine learning system was trained using some statistical features of extracted ENF signals. That trained model was then used for the identification of a given signal.
A description of the ENF extracting algorithm and machine learning algorithms, their corresponding accuracies are stated in the report.

Paper Details

Authors:
Dr. K.C.B. Wavegedara, D.L Dampahalage, M.P Pathegama, H.A.S.P Gunasekara, A.D.N De Zoysa, D.D.M De Soysa, P.T.N Mahesha
Submitted On:
17 June 2016 - 10:37am
Short Link:
Type:
Report
Event:
Document Year:
2015
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Document Files

IEEE-Signal-Processing-Cup-2016_FINAL(2).pdf

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[1] Dr. K.C.B. Wavegedara, D.L Dampahalage, M.P Pathegama, H.A.S.P Gunasekara, A.D.N De Zoysa, D.D.M De Soysa, P.T.N Mahesha, "IEEE Signal Processing Cup 2016 - Team Ravan", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1125. Accessed: Aug. 20, 2017.
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author = {Dr. K.C.B. Wavegedara; D.L Dampahalage; M.P Pathegama; H.A.S.P Gunasekara; A.D.N De Zoysa; D.D.M De Soysa; P.T.N Mahesha },
publisher = {IEEE SigPort},
title = {IEEE Signal Processing Cup 2016 - Team Ravan},
year = {2016} }
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AU - Dr. K.C.B. Wavegedara; D.L Dampahalage; M.P Pathegama; H.A.S.P Gunasekara; A.D.N De Zoysa; D.D.M De Soysa; P.T.N Mahesha
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Dr. K.C.B. Wavegedara, D.L Dampahalage, M.P Pathegama, H.A.S.P Gunasekara, A.D.N De Zoysa, D.D.M De Soysa, P.T.N Mahesha. (2016). IEEE Signal Processing Cup 2016 - Team Ravan. IEEE SigPort. http://sigport.org/1125
Dr. K.C.B. Wavegedara, D.L Dampahalage, M.P Pathegama, H.A.S.P Gunasekara, A.D.N De Zoysa, D.D.M De Soysa, P.T.N Mahesha, 2016. IEEE Signal Processing Cup 2016 - Team Ravan. Available at: http://sigport.org/1125.
Dr. K.C.B. Wavegedara, D.L Dampahalage, M.P Pathegama, H.A.S.P Gunasekara, A.D.N De Zoysa, D.D.M De Soysa, P.T.N Mahesha. (2016). "IEEE Signal Processing Cup 2016 - Team Ravan." Web.
1. Dr. K.C.B. Wavegedara, D.L Dampahalage, M.P Pathegama, H.A.S.P Gunasekara, A.D.N De Zoysa, D.D.M De Soysa, P.T.N Mahesha. IEEE Signal Processing Cup 2016 - Team Ravan [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1125