Car Parking Data Processing Technique for Smart Parking System as Part of Smart City

2020 ◽  
pp. 183-194
Author(s):  
Svitlana Popereshnyak ◽  
Iryna Yurchuk
Keyword(s):  
Data Processing ◽  
Smart City ◽  
Processing Technique ◽  
Parking System ◽  
Smart Parking ◽  
Data Processing Technique

A NEW DATA‐PROCESSING TECHNIQUE FOR THE ELIMINATION OF GHOST ARRIVALS ON REFLECTION SEISMOGRAMS

Geophysics ◽  
1964 ◽  
Vol 29 (5) ◽  
pp. 783-805 ◽  
Author(s):  
William A. Schneider ◽  
Kenneth L. Larner ◽  
J. P. Burg ◽  
Milo M. Backus
Keyword(s):  
Data Processing ◽  
Filter Design ◽  
Random Noise ◽  
Wide Band ◽  
Processing Technique ◽  
Linear Filter ◽  
Least Mean Square ◽  
Near Surface ◽  
Surface Reflection ◽  
Data Processing Technique

A new data‐processing technique is presented for the separation of initially up‐traveling (ghost) energy from initially down‐traveling (primary) energy on reflection seismograms. The method combines records from two or more shot depths after prefiltering each record with a different filter. The filters are designed on a least‐mean‐square‐error criterion to extract primary reflections in the presence of ghost reflections and random noise. Filter design is dependent only on the difference in uphole time between shots, and is independent of the details of near‐surface layering. The method achieves wide‐band separation of primary and ghost energy, which results in 10–15 db greater attenuation of ghost reflections than can be achieved with conventional two‐ or three‐shot stacking (no prefiltering) for ghost elimination. The technique is illustrated in terms of both synthetic and field examples. The deghosted field data are used to study the near‐surface reflection response by computing the optimum linear filter to transform the deghosted trace back into the original ghosted trace. The impulse response of this filter embodies the effects of the near‐surface on the reflection seismogram, i.e. the cause of the ghosting. Analysis of these filters reveals that the ghosting mechanism in the field test area consists of both a surface‐ and base‐of‐weathering layer reflector.

A novel data processing technique for image reconstruction of penumbral imaging

2011 ◽  
Author(s):  
Hongwei Xie ◽  
Hongyun Li ◽  
Zeping Xu ◽  
Guzhou Song ◽  
Faqiang Zhang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Processing ◽  
Processing Technique ◽  
Penumbral Imaging ◽  
Data Processing Technique

To: “A new data‐processing technique for multiple attenuation exploiting differential normal moveout,” by William A. Schneider, E. R. Prince, Jr., and Ben F. Giles, June, 1965, p. 348–362

Geophysics ◽  
1965 ◽  
Vol 30 (5) ◽  
pp. 932-932
Keyword(s):  
Data Processing ◽  
Processing Technique ◽  
Proper Place ◽  
Multiple Attenuation ◽  
The Cross ◽  
Data Processing Technique

In the article entitled “A new data‐processing technique for multiple attenuation exploiting differential normal moveout,” by William A. Schneider, E. R. Prince, Jr., and Ben F. Giles, June, 1965, p. 348–362, page 361, equation (A‐3) should read: [Formula: see text], (A‐3) and the sentence immediately following should read: where [Formula: see text] and [Formula: see text] are the cross‐spectral…. In the last two equations on the bottom of the page, the π was dropped down from its proper place in the exponent.

Understanding the intention to use medical big data processing technique from the perspective of medical data analyst

2017 ◽  
Vol 45 (4) ◽  
pp. 194-201 ◽  
Author(s):  
Shanyong Wang ◽  
Jun Li ◽  
Dingtao Zhao
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Processing Technique ◽  
Medical Data ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Big Data Processing ◽  
Content Type ◽  
Medical Big Data ◽  
Data Processing Technique

Purpose The purpose of this paper is to apply an extended technology acceptance model to examine the medical data analyst’s intention to use medical big data processing technique. Design/methodology/approach Questionnaire survey method was used to collect data from 293 medical data analysts and analyzed with the assistance of structural equation modeling. Findings The results indicate that the perceived usefulness, social influence and attitude are important to the intention to use medical big data processing technique, and the direct effect of perceived usefulness on intention to use is greater than social influence and attitude. The perceived usefulness is influenced by perceived ease of use. Attitude is influenced by perceived usefulness, and attitude acts as a mediator between perceived usefulness and usage intention. Unexpectedly, attitude is not influenced by perceived ease of use and social influence. Originality/value This research examines the medical data analyst’s intention to use medical big data processing technique and provides several implications for using medical big data processing technique.

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