Feature Extraction Identification Research Articles

Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting.

This paper presents a novel approximation unit added to the conventional spike processing chain which provides an appreciable reduction of complexity of the high-hardware cost feature extractors. The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting. Inclusion of the approximation unit can provide 3X compression (i.e. from 66 to 22 samples) to the spike waveforms while preserving their shapes. Detailed spike waveform sequences based on in-vivo measurements have been generated using a customized neural simulator for performance assessment of the approximation unit tested on six published feature extractors. For noise levels σN between 0.05 and 0.3 and groups of 3 spikes in each channel, all the feature extractors provide almost same sorting performance before and after approximation. The overall implementation cost when including the approximation unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the hardware costly and more accurate feature extractors, offering a substantial improvement in feature extraction design.

Slow feature extraction for hyperspectral image classification

ABSTRACT Recent research on hyperspectral image (HSI) classification has primarily focused on deep learning methods. Although these methods can automatically mine HSI classification features, they typically require many labelled samples to ensure sufficient classification performance. When there are fewer labelled training samples, the manual feature extraction design rules are critical for HSI classification. Considering the small sample problem, a slow spatial–spectral feature extraction method is proposed for HSI classification in this study. The proposed method can achieve high classification accuracy by using slow spatial–spectral features. The extracted slow feature dimension is much lower than that of the original spectral feature. Thus, the feature dimension for the HSI classification task is reduced, improving the classification efficiency. The experimental results of two real HSI datasets demonstrate that the proposed method can significantly reduce the feature dimensions and improve HSI classification accuracy.

Chatter Identification of Three-Dimensional Elliptical vibration Cutting Process Based on Empirical Mode Decomposition and Feature Extraction

Three-dimensional elliptical vibration cutting (3D-EVC) is one of the machining methods with the most potential in ultra-precision machining; its unique characteristics of intermittent cutting, friction reversal, and ease of chip removal can improve the machinability of materials in the cutting processes. However, there is still not much research about the chattering phenomenon in the 3D-EVC process. Therefore, based on the empirical mode decomposition (EMD) technique and feature extraction, a chatter identification method for 3D-EVC is proposed. In 3D-EVC operations, the vibration signal is collected by the displacement sensors and converted to frequency domain signal by fast Fourier transform (FFT). To identify tool cutting state using the vibration frequency signal, the vibration signals are decomposed using empirical mode decomposition (EMD), a series of intrinsic mode functions (IMFs), so the instantaneous frequency can be reflected by the vibration signals at any point. Then, selecting the primary IMFs which contain rich chatter information as the object in feature extraction identification, and two identification indexes, that is, the mean square frequency and self-correlation coefficient, are calculated for the primary IMFs by MATLAB software, to judge the chatter phenomenon. The experimental results showed that the mean square frequency and self-correlation coefficient of the three cutting states increase with the increase in the instability of the cutting state. The effectiveness of the improved chatter recognition method in 3D-EVC machining is verified.

Design of Feature Extraction in Content Based Image Retrieval (CBIR) using Color and Texture

Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tammura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background.