Anomaly detection in big data from UWB radars

EURASIP J. Adv. Signal Process.

Chen

et al. 2014

Self Cite

Classification in large-scale data is a key problem in big data domain. The theory of compressive sensing enables the recovery of a sparse signal from a small set of linear, random projections which provides a compressive classification method operating directly on the compressed data without reconstructing for big data. In this paper, we collected the compressed vowel /a:/ and /i:/ voice signals using compressive sensing for throat polyp detection. The throat polyp prediction procedure based on wavelet packet transform and support vector machine intelligent algorithm was deduced. The experiments for throat polyp prediction with the proposed classification algorithm were carried out. The results showed that the correct rate of prediction was stable under different number of samples and different random measurement matrices.

“…It can enable the extraction of features from signals which combine stationary and nonstationary characteristics with an arbitrary time-frequency resolution [22].…”

Section: Acquire the Frequency Component By Wptmentioning

confidence: 99%

Throat polyp detection based on compressed big data of voice with support vector machine algorithm

EURASIP J. Adv. Signal Process.

Chen

et al. 2014

Self Cite

“…In matrix notation, it can be described as follows [14][15][16][17][18][19][20][21][22][23][24][25]:…”

Section: Background On Separable Compressive Sensingmentioning

confidence: 99%

Intelligent throat polyp detection with separable compressive sensing

EURASIP J. Adv. Signal Process.

et al. 2014

Self Cite

Compressive sensing can minimize the collection of redundant data in the acquisition step. However, it requires a huge amount of storage and creates a tremendous computation burden due to the size of random measurement matrix in compressive sensing theory for big data collection. The separable compressive sensing theory uses two-dimensional separable random measurement matrixes instead of a huge size of random matrix to remedy sensing matrix storage and computation complexity. In this paper, we proposed an intelligent throat polyp detection with singular value decomposition and support vector machine algorithms based on the vowel/a:/and/i:/ voices. We compared the detection effects of the proposed intelligent detection method between original voice signals and compressed signals which were collected by separable compressive sensing theory. The experimental results showed that the matrix size of original vowel voices signal could affect the correct rate of prediction. Also, the correct rate of prediction was stable under different random measurement matrix and different compressed ratio.

“…These tiny human motions would cause the scattering and reflection changes of an electromagnetic wave which is emitted by the UWB radar and passes through walls to reach the human body target. Furthermore, human target detection, location, and tracking can be achieved through signal extraction and analysis of human characteristics from the radar echo signal [19][20][21]. Qilian Liang from the University of Texas at Arlington has investigated these topics deeply [22][23][24][25][26][27][28][29].…”

Section: Through-wall Human Detection Based On Uwb Radarmentioning

confidence: 99%

Statistical wavelet-based anomaly detection in big data with compressive sensing

Zhou

et al. 2013

J Wireless Com Network

Self Cite

Anomaly detection in big data is a key problem in the big data analytics domain. In this paper, the definitions of anomaly detection and big data were presented. Due to the sampling and storage burden and the inadequacy of privacy protection of anomaly detection based on uncompressed data, compressive sensing theory was introduced and used in the anomaly detection algorithm. The anomaly detection criterion based on wavelet packet transform and statistic process control theory was deduced. The proposed anomaly detection technique was used for through-wall human detection to demonstrate the effectiveness. The experiments for detecting humans behind a brick wall and gypsum based on ultra-wideband radar signal were carried out. The results showed that the proposed anomaly detection algorithm could effectively detect the existence of a human being through compressed signals and uncompressed data.