Self-Adaptive Multi-Peak Detection Algorithm for FBG Sensing Signal

Chen, Yong; Yang, Kai; Liu, Huanlin

doi:10.1109/jsen.2016.2516038

Cited by 50 publications

(14 citation statements)

References 19 publications

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“…There are several methods for multiple peak detection, depending on the signal origin and physical meaning eg. [18][19][20]. Also online can be found many solutions for multiple peak detection; some examples are given in [21,22].…”

Section: Determination Of Remaining Dominant Frequencies -Multipeak Dmentioning

confidence: 99%

An Overview and Efficiency Analysis of Dominant Frequencies Estimation Methods with the Application of DIRECT Global Minimization Method

2020

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This report discusses the application of the following methods that are used to calculate maximum frequency of a spectrum: parabolic, modified parabolic by Rife and Vincent, the method according to Quinn from 1994 and the method according to Quinn from 1997. After introducing DIRECT global minimization method, the results obtained using previously mentioned methods are compared with the results obtained using DIRECT global minimization of the Lipschitz function. Determining vibration frequency and comparing to previously collected data or knowledge base data is a common procedure. In engineering practice it is often necessary to determine not one, but several so-called dominant frequencies or local maxima; the procedure is sometimes called multi peak detection and here we propose an algorithm for computerised multi peak detection.

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Section: Determination Of Remaining Dominant Frequencies -Multipeak Dmentioning

confidence: 99%

An Overview and Efficiency Analysis of Dominant Frequencies Estimation Methods with the Application of DIRECT Global Minimization Method

2020

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“…The following techniques were used to deal with the trajectory tracking process based on PDR. An accelerator based on three axes and a bandpass filter were used for step detection and noise removal, respectively, while the peak detection method [39] was used for step detection. Besides, Weinberg algorithm [40] was applied to calculate the step length.…”

Section: Experiments Setupmentioning

confidence: 99%

Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Guo

Qin

Zhao

et al. 2020

Sensors

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The crowdsourcing-based wireless local area network (WLAN) indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps. Aiming at the problem of the diverse terminal devices and the inaccurate location annotation of the crowdsourced samples, which will result in the construction of the wrong radio map, an effective indoor radio map construction scheme (RMPAEC) is proposed based on position adjustment and equipment calibration. The RMPAEC consists of three main modules: terminal equipment calibration, pedestrian dead reckoning (PDR) estimated position adjustment, and fingerprint amendment. A position adjustment algorithm based on selective particle filtering is used by RMPAEC to reduce the cumulative error in PDR tracking. Moreover, an inter-device calibration algorithm is put forward based on receiver pattern analysis to obtain a device-independent grid fingerprint. The experimental results demonstrate that the proposed solution achieves higher localization accuracy than the peer schemes, and it possesses good effectiveness at the same time.

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“…In addition, due to the time jitter of the dual-axis galvo, photon arrival timing signal and TCSPSC module, the fine time of the arrival photon in a pixel is floating. Depth estimation mainly uses centroid algorithm (CA) [29], Gaussian fitting (GF) [31], [32], exponential broadening Gaussian fitting (EBGF) [33] and so on. The number of photons detected per pixel is very small due to imaging in extremely weak environments.…”

Section: Estimation Algorithm Of Reflectivity and Depthmentioning

confidence: 99%

Single-Photon Reflectivity and Depth Imaging by Continuous Measurement of Arrival Time of Photons

Yan

Wang

et al. 2019

IEEE Photonics J.

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We demonstrate a reflectivity and depth imaging Lidar system based on a novel photon arrival time measurement method. In this method, the arrival time of photons in a scanning position is continuously measured with a common starting point. The number of laser pulses is counted by a specially designed field programmable gate array (FPGA) control module as the coarse time of arrival photon. Time interval between arrival photon and the nearest coming laser pulse is measured by a time-correlated single-photon counting (TCSPC) module as the fine time of arrival photon. Using the system, not only the singlephoton counting imaging can be realized, but also the first photon imaging, the first two photons imaging, etc. can be realized. A photon statistical model based on the doubly stochastic Poisson point processes, a time-gated filtering algorithm, and the reflectivity algorithm based on maximum likelihood estimation are derived. High-sensitivity reflectivity and depth imaging with a resolution of 512 × 512 pixels are achieved. The experimental results show that the horizontal spatial resolution is 2 mm, the vertical depth resolution is 5.375 cm, and the average number of photons per pixel is less than 1.3 photons.

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Self-Adaptive Multi-Peak Detection Algorithm for FBG Sensing Signal

Cited by 50 publications

References 19 publications

An Overview and Efficiency Analysis of Dominant Frequencies Estimation Methods with the Application of DIRECT Global Minimization Method

An Overview and Efficiency Analysis of Dominant Frequencies Estimation Methods with the Application of DIRECT Global Minimization Method

Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Single-Photon Reflectivity and Depth Imaging by Continuous Measurement of Arrival Time of Photons

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