Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm

2019 ◽  
Vol 39 (2) ◽  
pp. 0228003 ◽  
Author(s):  
张子鹏 Zhang Zipeng ◽  
丁建丽 Ding Jianli ◽  
王敬哲 Wang Jingzhe
Keyword(s):  
Harmonic Analysis ◽  
Spectral Characteristics ◽  
Arid Area ◽  
Analysis Algorithm

scholarly journals An extended stochastic harmonic analysis algorithm: Application for dissolved oxygen

2007 ◽  
Vol 43 (8) ◽  
Author(s):  
Omar I. Abdul-Aziz ◽  
Bruce N. Wilson ◽  
John S. Gulliver
Keyword(s):  
Dissolved Oxygen ◽  
Harmonic Analysis ◽  
Analysis Algorithm

scholarly journals The VPF Harmonic Analysis Algorithm Based on Quasi-Synchronous DFT

2017 ◽  
Vol 11 (1) ◽  
pp. 114-124 ◽  
Author(s):  
Zhongjun Fu ◽  
Jianyu Wang ◽  
Yun Ou ◽  
Genyuan Zhou ◽  
Feng'e Bai ◽  
...  
Keyword(s):  
Harmonic Analysis ◽  
Frequency Domain ◽  
Discrete Spectrum ◽  
Short Range ◽  
Frequency Drift ◽  
Signal Frequency ◽  
Spectral Leakage ◽  
Analysis Algorithm ◽  
Sampling Position ◽  
Picket Fence

Background: The main source of spectral leakage in Quasi-synchronous DFT (QSDFT) harmonic analysis is the short-range leakage resulting from the fluctuating signals. Objective: The QSDFT discrete spectrum stretching theorem, as is demonstrated in this paper, shows that the peak in frequency domain changes synchronously with the signal frequency drift. Based on QSDFT, an improved algorithm—Variable Picket Fence (VPF) is proposed. Method: The VPF means the sampling position in the frequency domain will synchronously change with the signal frequency drift. In this way, we can accurately capture the spectrum peak, thereby inhibiting short-range spectral leakage. Hence signal frequency drift can be obtained by measuring the fundamental phase difference between the adjacent sample points. Results: Simulation and application results prove that this proposed algorithm increases the accuracy of harmonic analysis by inhibiting short-range spectral leakage efficiently in the range of 45 to 55HZ.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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