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This paper proposes a novel specific emitter identification (SEI) method for communication emitter individual identification based on the 3D-Hilbert energy spectrum and multi-scales segmentation (3D-HESMS). First, the time-frequency energy spectrum is derived via Hilbert-Huang Transform (HHT), which can be defined as a complicated curved surface in the three dimension space, namely the 3D-HUbert energy...
Time series classification is an important task in data mining that has been traditionally addressed with the use of similarity-based classifiers. The 1-NN DTW is typically considered the most accurate model for temporal data. Nevertheless, some authors have recently proposed ingenious alternatives to the 1-NN DTW by using diversity of time series representation or by using DTW for feature extraction...
The work addresses classification of EEG signals into seizure and non-seizure by applying EMD and SVM with proposal of new feature Root Mean Square (RMS) frequency and feature using Hilbert marginal spectrum which overcomes the drawback of feature instantaneous bandwidth. We have success in achieving the consistency with the new features which shows classification average accuracy of 97.72% and highest...
We propose a sound analysis system for the detection of audio events in surveillance applications. The method that we propose combines short- and long-time analysis in order to increase the reliability of the detection. The basic idea is that a sound is composed of small, atomic audio units and some of them are distinctive of a particular class of sounds. Similarly to the words in a text, we count...
This paper presents recent time-frequency analysis technique called Hilbert Huang Transform (HHT) used for non-stationary and nonlinear signals. This technique is applied in combination with a pattern recognition method to detect defects in induction motor. Studied defects in our case are one broken rotor bar, two broken rotor bars and broken ring on the rotor. The aim of this study is to improve...
A novel approach for specific emitter identification using Hilbert spectrum is proposed for both single-hop and relaying scenarios. In particular, two features, i.e., the energy entropy and color moments, are extracted from the Hilbert spectrum of the signal of interest as identification features. The spectrum is obtained through the Hilbert-Huang transform, which is a powerful tool for the analysis...
Music transcription consists in transforming the musical content of audio data into a symbolic representation. The objective of this study is to investigate a transcription system for polyphonic piano. The proposed method focuses on temporal musical structures, note events and their main characteristics: the attack instant and the pitch. Onset detection exploits a time-frequency representation of...
In this paper a new pattern recognition based algorithm is presented to detect high impedance fault (HIF) in distribution networks. In this method, using wavelet transform (WT), the time-frequency based features of the current waveform up to 6.25 kHz are calculated. To extract the best feature set of the generated time frequency features, two methods including principle component analysis (PCA) and...
The method of fault diagnosis of rolling bearings based on wavelet packet transform and support vector machine is presented. The key to fault bearings diagnosis is feature extracting and feature classifying. Wavelet packet transform, as a new technique of signal processing, possesses excellent characteristic of time-frequency localization and is suitable for analyzing the time-varying or transient...
Doppler ultrasound blood flow signal (DUBFS) is a non-stationary signal that is widely used in the study of the clinical diagnosis of cardiovascular diseases. According to the theory of pulse diagnosis of traditional Chinese medicine, in this paper, a feature extraction method based on Hilbert_Huang transform is proposed in order to investigate the relationship between the DUBFS of wrist radial artery...
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