Emitter Individual Identification Based on Nonlinearity Analysis of Oscillators

2019 ◽  
Vol 13 (4) ◽  
pp. 424-432
Author(s):  
Han Bao ◽  
Hongyan Yao
Keyword(s):  
Power Spectrum ◽  
Phase Noise ◽  
Recognition Performance ◽  
Classification Performance ◽  
Noise Model ◽  
Individual Identification ◽  
Noise Power ◽  
Spectrum Feature ◽  
The Stability ◽  
Stable Classification

Background: According to the characteristics of phase noise, phase noise power spectrum feature was used for emitter individual identification. Methods: For different emitter individuals, we established a phase noise model with the influence of both transmitter and receiver based on the research of its characteristics. Using power spectrum of phase noise, the corresponding scattered information entropy was proposed. The same type of communication equipments can be identified by Minimum Error Minimax Probability Machine (MEMPM) classifier through extracting this feature at a different frequency offset. Results: Simulation results show that the new features can be effectively used to classify emitter individuals with stable classification performance. Conclusion: According to the simulation, when the SNR was higher than 10dB, the accuracy rate was higher than 90%. It proved that the method is useful and effective. In addition, the recognition performance of the proposed method is very stable, showing the stability of the device phase noise. Therefore, it can be used in practice.

Phase Noise Measurement and Testing with Delta-Sigma TDC

2015 ◽  
Vol 643 ◽  
pp. 149-155 ◽  
Author(s):  
Yusuke Osawa ◽  
Daiki Hirabayashi ◽  
Naohiro Harigai ◽  
Haruo Kobayashi ◽  
Osamu Kobayashi ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Phase Noise ◽  
High Performance ◽  
Phase Measurement ◽  
Phase Fluctuation ◽  
Noise Measurement ◽  
Measurement Time ◽  
Noise Power ◽  
Delta Sigma ◽  
Measurement And Testing

This paper describes a phase noise measurement and testing technique for a clock using a delta-sigma time-to-digital converter (TDC) and verifies its effectiveness with MATLAB simulations. The proposed technique can be implemented with relatively small circuitry, based on the following: (i) The clock under test (CUT) is a repetitive signal. (ii) The time resolution with CUT and a reference clock can be finer with longer measurement time with the delta-sigma TDC. (iii) The phase noise power spectrum can be calculated from the delta-sigma TDC output data using FFT. High performance spectrum analyzers with long measurement time (several ten seconds order due to average of several-time phase measurement results), which are very costly in mass production testing, are not be needed for phase noise measurement with the proposed technique. Our simulation used the input clock of 1 MHz in several phase fluctuation cases, and we observed that the phase fluctuation spectrum at the expected frequency from TDC output power spectrum obtained by FFT. We also investigated the amount of phase fluctuation with our theoretical calculation, which agrees with the simulation results.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

Extension of the LTV Phase Noise Model of Electrical Oscillators for the Output Harmonics

2012 ◽  
Vol E95.C (12) ◽  
pp. 1846-1856 ◽  
Author(s):  
Seyed Amir HASHEMI ◽  
Hassan GHAFOORIFARD ◽  
Abdolali ABDIPOUR
Keyword(s):  
Phase Noise ◽  
Noise Model ◽  
Electrical Oscillators

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

scholarly journals Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3267
Author(s):  
Ramon C. F. Araújo ◽  
Rodrigo M. S. de Oliveira ◽  
Fernando S. Brasil ◽  
Fabrício J. B. Barros
Keyword(s):  
Image Denoising ◽  
Recognition Performance ◽  
Partial Discharge ◽  
Classification Performance ◽  
Partial Discharges ◽  
Denoising Method ◽  
Internal Parameters ◽  
On Line ◽  
Automatic Removal ◽  
The Impact

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

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