scholarly journals Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 95 ◽  
Author(s):  
Farnaz Shikhsarmast ◽  
Tingting Lyu ◽  
Xiaolin Liang ◽  
Hao Zhang ◽  
Thomas Gulliver
Keyword(s):  
Time Window ◽  
Human Subjects ◽  
Wavelet Packet ◽  
Random Noise ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Multiple Time ◽  
Selection Algorithm ◽  
Human Respiration ◽  
Window Selection

This paper considers vital signs (VS) such as respiration movement detection of human subjects using an impulse ultra-wideband (UWB) through-wall radar with an improved sensing algorithm for random-noise de-noising and clutter elimination. One filter is used to improve the signal-to-noise ratio (SNR) of these VS signals. Using the wavelet packet decomposition, the standard deviation based spectral kurtosis is employed to analyze the signal characteristics to provide the distance estimate between the radar and human subject. The data size is reduced based on a defined region of interest (ROI), and this improves the system efficiency. The respiration frequency is estimated using a multiple time window selection algorithm. Experimental results are presented which illustrate the efficacy and reliability of this method. The proposed method is shown to provide better VS estimation than existing techniques in the literature.

scholarly journals An automated time-window selection algorithm for seismic tomography

2009 ◽  
Vol 178 (1) ◽  
pp. 257-281 ◽  
Author(s):  
Alessia Maggi ◽  
Carl Tape ◽  
Min Chen ◽  
Daniel Chao ◽  
Jeroen Tromp
Keyword(s):  
Seismic Tomography ◽  
Time Window ◽  
Selection Algorithm ◽  
Window Selection

Principal component transforms of triaxial recordings by singular value decomposition

Geophysics ◽  
1991 ◽  
Vol 56 (4) ◽  
pp. 528-533 ◽  
Author(s):  
G. M. Jackson ◽  
I. M. Mason ◽  
S. A. Greenhalgh
Keyword(s):  
Singular Value Decomposition ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Principal Component ◽  
Singular Value ◽  
Data Matrix ◽  
Single Station ◽  
Window Selection ◽  
Value Decomposition

Polarization analysis can be achieved efficiently by treating a time window of a single‐station triaxial recording as a matrix and doing a singular value decomposition (SVD) of this seismic data matrix. SVD of the triaxial data matrix produces an eigenanalysis of the data covariance (cross‐energy) matrix and a rotation of the data onto the directions given by the eigenanalysis (Karhunen‐Loève transform), all in one step. SVD provides a complete principal components analysis of the data in the analysis time window. Selection of this time window is crucial to the success of the analysis and is governed by three considerations: the window should contain only one arrival; the window should be such that the signal‐to‐noise ratio is maximized; and the window should be long enough to be able to discriminate random noise from signal. The SVD analysis provides estimates of signal, signal polarization directions, and noise. An F‐test is proposed which gives the confidence level for the hypothesis of rectilinear polarization. This paper illustrates the analysis and interpretation of synthetic rectilinearly and elliptically polarized arrivals at a single triaxial station by SVD.

scholarly journals 1-Tx/5-Rx Through-Wall UWB Switched-Antenna-Array Radar for Detecting Stationary Humans

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6828 ◽  
Author(s):  
Artit Rittiplang ◽  
Pattarapong Phasukkit
Keyword(s):  
Antenna Array ◽  
Solid Wall ◽  
Human Subjects ◽  
Ultra Wideband ◽  
Projection Algorithm ◽  
Concrete Wall ◽  
Band Frequency ◽  
Narrow Angle ◽  
Solid Objects ◽  
Human Respiration

This research proposes a through-wall S-band ultra-wideband (UWB) switched-antenna-array radar scheme for detection of stationary human subjects from respiration. The proposed antenna-array radar consists of one transmitting (Tx) and five receiving antennas (Rx). The Tx and Rx antennas are of Vivaldi type with high antenna gain (10 dBi) and narrow-angle directivity. The S-band frequency (2–4 GHz) is capable of penetrating non-metal solid objects and detecting human respiration behind a solid wall. Under the proposed radar scheme, the reflected signals are algorithmically preprocessed and filtered to remove unwanted signals, and 3D signal array is converted into 2D array using statistical variance. The images are reconstructed using back-projection algorithm prior to Sinc-filtered refinement. To validate the detection performance of the through-wall UWB radar scheme, simulations are carried out and experiments performed with single and multiple real stationary human subjects and a mannequin behind the concrete wall. Although the proposed method is an odd concept, the interest of this paper is applying the 1-Tx/5-Rx UWB switched-antenna array radar with the proposed method that is capable of distinguishing between the human subjects and the mannequin behind the concrete wall.

scholarly journals Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion

2021 ◽  
Vol 13 (18) ◽  
pp. 3791
Author(s):  
Xiuzhu Yang ◽  
Xinyue Zhang ◽  
Yi Ding ◽  
Lin Zhang
Keyword(s):  
Data Fusion ◽  
Vital Sign ◽  
Continuous Wave ◽  
Short Term Memory ◽  
Wavelet Packet ◽  
Vital Signs ◽  
Radar Data ◽  
Activity Monitoring ◽  
Ultra Wideband ◽  
Additional Information

The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework.

scholarly journals Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 83
Author(s):  
Hongqiang Xu ◽  
Malikeh P. Ebrahim ◽  
Kareeb Hasan ◽  
Fatemeh Heydari ◽  
Paul Howley ◽  
...  
Keyword(s):  
Heart Rate ◽  
Signal Processing ◽  
Respiration Rate ◽  
Body Position ◽  
Random Noise ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Spectrum Estimation ◽  
Respiration Activity ◽  
Uwb Radar

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

Optimized Correlation-Based Time Window Selection Algorithm for Motor Imagery Based BCIs

2021 ◽  
pp. 27-36
Author(s):  
Zongmei Chen ◽  
Cili Zuo ◽  
Hak-Keung Lam ◽  
Yangyang Miao ◽  
Xingyu Wang ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Time Window ◽  
Selection Algorithm ◽  
Window Selection

Research of Wavelet Packet Analysis in the De-Noising of Ultrasonic Echo Signal

2011 ◽  
Vol 403-408 ◽  
pp. 1817-1822
Author(s):  
Xi Feng Zhou ◽  
Xiao Wu ◽  
Qian Gang Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Random Noise ◽  
Flaw Detection ◽  
Low Frequency ◽  
Local Analysis ◽  
Echo Signal ◽  
Wavelet Packet Analysis ◽  
Ultrasonic Flaw

The quality of ultrasonic flaw echo signal is the foundation of achieving qualitative and quantitative analysis in the in ultrasonic flaw detection. In practice, the flaw echo signals are often contaminated or even annihilation by random noise. According to the characteristics of ultrasonic flaw echo signal, wavelet packet has more accurate local analysis ability in low frequency and high frequency part. This paper discusses de-noising in ultrasonic signals based on wavelet packet analysis, and proposes an improved threshold approach for de-noising. The results show that: It remarkably raises the signal-to-noise ratio of ultrasonic flaw echo signal and improves the quality of signal with improved wavelet packet threshold.

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