This paper investigates P300 features extracted through wavelet transform for BCI systems. Feature extraction is one of the key issues of signal processing for P300 based brain-computer interface systems (BCI). This paper examines and highlights the significance of using wavelets in P300 based BCI systems. We also mention various methods of feature extraction from P300 signals. The analysis sug...

Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the...

ـــ ـ Automatic signature verification is a wellestablished and an active area for research with numerous applications such as bank check verification, ATM access, etc. Most off-Line signature verification systems depend on pixels intensity in feature extraction process which is sensitive to noise and any scale or rotation process on signature image. This paper proposes an off-line handwritten ...

In pattern recognition applications, finding compact and efficient feature set is important in overall problem solving. In this paper, feature analysis using wavelet coefficient histogram for the musical instrument recognition has been presented and compared with traditional features. The new proposed wavelet coefficient histograms features found compact and efficient with existing traditional ...

In our previous works, the wavelet-based feature extraction algorithms have been developed to explore the useful information for the hyperspectral image classification. On the other hand, the idea of using artificial neural network (ANNs) has also proved useful for hyperspectral image classification. To combine the advantages of ANNs with wavelet-based feature extraction methods, the wavelet ne...

This paper aims to provide a report regarding the seminar presentation given on 23.02.2011 as a part of the postgraduate seminar course S-88.4223 Wavelets in Communications 1 lectured by Dr. Sumesh Parameswaran at Aalto University School of Electrical Engineering. In particular, the topic on " wavelets for single carrier communications " has been considered in this paper. I. Introduction Wavele...

“Fault diagnosis of the roller bearings” as pattern classification problem has three main steps namely, feature extraction, feature selection, and classification. A number of machine-learning algorithms have been successfully used to solve the problem with the help of vibration signals. Feature extraction is one of the most important activities in the whole process, as the strength of the featu...

In his/her daily round each man feels the necessity to use a database containing images. The user’s basic requirements reduce to the following. The image features extraction rate to be as much as possible, the accuracy degree to be high as well as the time for comparison between the image features to be minimum. Besides, for the needs of the modern image databases it is important the extracted ...

in recent years, sub-band speech recognition has been found useful in addressing the need for robustness in speech recognition, especially for the speech contaminated by band-limited noise. in sub-band speech recognition, the full band speech is divided into several frequency sub-bands, with the result of the recognition task given by the combination of the sub-band feature vectors or their lik...

Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. This paper presents a probabilistic neural network (PNN) and new feature selection technique for fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-fa...