Wavelet denoising of multiframe optical coherence tomography data using similarity measures

Habib, Wajiha; Sarwar, Tabinda; Siddiqui, Adil Masood; Touqir, Imran

doi:10.1049/iet-ipr.2016.0160

Cited by 25 publications

(15 citation statements)

References 34 publications

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“…It is necessary to determine the zero velocity intervals accurately to improve the accuracy of the pedestrian inertial navigation and positioning algorithm. When the pedestrian’s foot is in full contact with the ground, the angular rate and horizontal acceleration of the foot are approximately equal to zero, while the vertical acceleration is approximately equal to gravitational acceleration g. The information about the acceleration and angular rate is used to determine the zero velocity intervals of a pedestrian’s movement cycle [ 33 , 34 , 35 ]. This paper uses a multicondition threshold discriminant algorithm to determine the zero velocity intervals as follows:

where | a t | and | ω t | are the amplitudes of acceleration and angular rate at time t.

represents the acceleration variance at time t and can be expressed as

where

is the average value of acceleration within the window and n is the width of the window.…”

Section: Methodsmentioning

confidence: 99%

“…However, these algorithms are vulnerable to false positives, false negatives, and delays. The threshold-based wavelet denoising algorithm is designed to detect outliers [ 33 ]. Aimed at the outliers in the dynamic measurement process, a self-adaptive five-point linear prediction data detection method was introduced, in which only the data of a single measurement can be selected and the error of slow change in the system cannot be effectively identified [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan

Zhang

Sun

et al. 2018

Sensors

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Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

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where | a t | and | ω t | are the amplitudes of acceleration and angular rate at time t.

represents the acceleration variance at time t and can be expressed as

where

is the average value of acceleration within the window and n is the width of the window.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan

Zhang

Sun

et al. 2018

Sensors

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show abstract

“…The microseismic search engine can accurately and quickly determine the microseismic focal point location. The signal waveform generated by microseism changes with time, which is similar to sound signals [32]. In the determination of the focal point position, it is assumed that a waveform very similar to the input signal is found in the waveform database; then, the found waveform microseismic location is the place of this microseismic occurrence.…”

Section: Selection Of Eemd Algorithmmentioning

confidence: 99%

The Use of Intelligent Sensing Algorithm for Internet of Things and Hash Spatial Information Location Technology

Wang

Liu

2021

Wireless Communications and Mobile Computing

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This exploration is aimed at quickly obtaining the spatial position information of microseismic focal points and increasing the accuracy of microseismic rapid positioning, to take timely corresponding measures. A microseismic focal point location system completely different from the traditional microseismic location method is proposed. The search engine technology is introduced into the system, which can locate the microseismic focal point quickly and accurately. First, the propagation characteristics of microseismic signals in coal and rock layers are analyzed, and the focal position information is obtained. However, the collected microseismic signal of the coal mine contains noise, so it is denoised at first. Then, a waveform database is established for the denoised waveform data and focal point position. The structure and mathematical model of the location-sensitive hash (LSH) based on P stable distribution are introduced and improved, and the optimized algorithm multiprobe LSH is obtained. The microseismic location model is established according to the characteristics of microseismic data. The values of three parameters, hash table number, hash function family dimension, and interval size, are determined. The experimental data of the parameters of the search engine algorithm are analyzed. The results show that when the number of hash tables is 6, the dimension k of the hash function family is 14, and the interval size W is 8000, the retrieval time reaches a relatively small value, the recall rate reaches a large value, and the proportion of retrieved candidates is large; the parameters of the search engine algorithm of the measured coal mine microseismic data are analyzed. It is obtained that when the number of hash tables is 4, the dimension k of the hash function family is 6, and the interval size W is 500, the retrieval time reaches a relatively small value, the recall rate obtains a large value, and the proportion of retrieved candidates is large. The contents studied are of great significance to the evaluation of destructive mine earthquakes and impact risk.

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“…Currently, the most widely used is the Mallat algorithm [68], which can improve the speed of image wavelet decomposition and reconstruction. Mallat uses two onedimensional filters to decompose two-dimensional images.…”

Section: Band Fusionmentioning

confidence: 99%

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

et al. 2020

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Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this paper, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the 1 L norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimizationbased methods, this method has better detection performance and accuracy.

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Wavelet denoising of multiframe optical coherence tomography data using similarity measures

Cited by 25 publications

References 34 publications

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

The Use of Intelligent Sensing Algorithm for Internet of Things and Hash Spatial Information Location Technology

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

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