Application of Gaussian fitting to the fast search of pulsar periodic

Optik ◽  
2019 ◽  
Vol 198 ◽  
pp. 163253
Author(s):  
Neng Xu ◽  
Lizhi Sheng ◽  
Chen Chen ◽  
Yao Li ◽  
Tong Su ◽  
...  
Keyword(s):  
Fast Search ◽  
Gaussian Fitting

scholarly journals High-Order Polynomial Fitting Assistance for Fast Double-Peak Finding in Brillouin-Distributed Sensing

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 187
Author(s):  
Marcelo A. Soto ◽  
Alin Jderu ◽  
Dorel Dorobantu ◽  
Marius Enachescu ◽  
Dominik Ziegler
Keyword(s):  
Optical Fibers ◽  
High Order ◽  
Polynomial Fitting ◽  
Gaussian Fitting ◽  
Peak Finding ◽  
Fitting Method ◽  
Fold Reduction ◽  
Order Polynomial ◽  
Fitting In ◽  
Distributed Brillouin Sensor

A high-order polynomial fitting method is proposed to accelerate the computation of double-Gaussian fitting in the retrieval of the Brillouin frequency shifts (BFS) in optical fibers showing two local Brillouin peaks. The method is experimentally validated in a distributed Brillouin sensor under different signal-to noise ratios and realistic spectral scenarios. Results verify that a sixth-order polynomial fitting can provide a reliable initial estimation of the dual local BFS values, which can be subsequently used as initial parameters of a nonlinear double-Gaussian fitting. The method demonstrates a 4.9-fold reduction in the number of iterations required by double-Gaussian fitting and a 3.4-fold improvement in processing time.

scholarly journals Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1970
Author(s):  
Jun-Kyu Park ◽  
Suwoong Lee ◽  
Aaron Park ◽  
Sung-June Baek
Keyword(s):  
Search Algorithm ◽  
Principal Component ◽  
Search Method ◽  
Reference Spectrum ◽  
Search Range ◽  
Fast Search ◽  
Input Spectrum ◽  
Computationally Intensive ◽  
Multiple Clusters ◽  
Full Search

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

An Auto-Focus Algorithm of Fast Search Based on Combining Rough and Fine Adjustment

2012 ◽  
Vol 468-471 ◽  
pp. 534-537
Author(s):  
Zhao Hua Lin ◽  
Xin Liu ◽  
Yu Liang Zhang ◽  
Shu Mei Zhang
Keyword(s):  
Focal Plane ◽  
Narrow Range ◽  
Evaluation Function ◽  
Small Step ◽  
Uniform Sampling ◽  
Fast Search ◽  
Optical Measure ◽  
Fine Adjustment ◽  
Auto Focusing ◽  
Image Definition

A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing, and then the wavelet transform based image definition evaluation function, which is sensitivity to the variation in image definition, was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing.

scholarly journals Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 364 ◽  
Author(s):  
Hao Wu ◽  
Bo Pang ◽  
Dahai Dai ◽  
Jiani Wu ◽  
Xuesong Wang
Keyword(s):  
Decomposition Methods ◽  
Polarization Property ◽  
Geometrical Structures ◽  
Fast Search ◽  
Vehicle Recognition ◽  
Radar Images ◽  
Density Peaks ◽  
Aerial Vehicle ◽  
Polarization Characteristics

Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in “Science”, one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods.

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