scholarly journals The method of automatic identification of motor vehicle users

2020 ◽  
Vol 2 (2) ◽  
pp. 63-71
Author(s):  
Tadeusz Niedziela
Keyword(s):  
Fourier Transform ◽  
Physical Properties ◽  
Motor Vehicle ◽  
Automatic Recognition ◽  
Automatic Identification ◽  
Image Function ◽  
Characteristic Features ◽  
The Fourier Transform

This paper presents a method of automatic recognition of fingerprint diffraction images of motor vehicle users. The proposed method is based on the basic physical properties of the Fourier transform. It creates the possibility of reducing the problem of recognition to the Fourier transform of the image function, extraction of characteristic features vector and classification of input images.

Developments in the processing of electron energy-loss spectra

1987 ◽  
Vol 45 ◽  
pp. 80-83
Author(s):  
R.F. Egerton
Keyword(s):  
Fourier Transform ◽  
Energy Loss ◽  
Fourier Transforms ◽  
Mean Free Path ◽  
Single Scattering ◽  
Specimen Thickness ◽  
Additional Information ◽  
Scattering Spectrum ◽  
Characteristic Features ◽  
The Fourier Transform

Because the total-inelastic mean free path is generally comparable to the specimen thickness, energy-loss spectra recorded in a TEM contain appreciable contributions from plural (or multiple) scattering, which imparts no additional information but may distort or submerge characteristic features. Happily, the single-scattering spectrum S(E) can be derived from a recorded spectrum by the method of Fourier-log deconvolution; if j(f) and z(f) are the Fourier transforms of the recorded data J(E) and of the zero-loss peak Z(E), the Fourier transform s(f) of the single-scattering distribution S(E) is given by:s(f) = r(f) loge [j(f)/z(f)] (1)Here, r(f) is the Fourier transform of a bell-shaped reconvolution function R(E); if r(f) were omitted from Eq.(l), s(f) would correspond to an ‘ideal’ single-scattering distribution, unbroadened by the instrumental resolution △E.

Investigation of the Fourier Transform for Analyzing Spectroscopic Data by Computerized Learning Machines

1971 ◽  
Vol 25 (2) ◽  
pp. 203-207 ◽  
Author(s):  
L. E. Wangen ◽  
N. M. Frew ◽  
T. L. Isenhour ◽  
P. C. Jurs
Keyword(s):  
Fourier Transform ◽  
Data Storage ◽  
Spectroscopic Data ◽  
Weighted Average ◽  
Learning Machines ◽  
Frequency Representation ◽  
Data Points ◽  
The Fourier Transform ◽  
Time Required

This paper investigates the use of the fast Fourier transform as an aid in the analysis and classification of spectroscopic data. The pattern obtained after transformation is viewed as a weighted average and/or as a frequency representation of the original spectroscopic data. In pattern recognition the Fourier transform allows a different (i.e., a frequency) representation of the data which may prove more amenable to linear separation according to various categories of the patterns. The averaging property means that the information in each dimension of the original pattern is distributed over all dimensions in the pattern resulting from the Fourier transformation. Hence the arbitrary omission or loss of data points in the Fourier spectrum has less effect on the original spectrum. This property is exploited for reducing the dimensionality of the Fourier data so as to minimize data storage requirements and the time required for development of pattern classifiers for categorization of the data. Examples of applications are drawn from low resolution mass spectrometry.

Hard and light gamification in education: Which one to choose?

2020 ◽  
Vol 1 (1) ◽  
pp. 20-27
Author(s):  
E. V. Karmanova ◽  
V. A. Shelemetyeva
Keyword(s):  
Computer Games ◽  
Educational Process ◽  
Course Development ◽  
Learning Technology ◽  
Training Course ◽  
Resource Requirements ◽  
E Learning ◽  
Characteristic Features ◽  
Game Elements

The article is devoted to the implementation of gamification methods in the educational process. The characteristic features of light and hard gamification are presented. The appropriateness of using gamification when applying e-learning technology is considered. Classification of courses based on hard gamification taking into account the technological features of development is proposed: courses-presentations, courses — computer games, VR/AR courses. The article also illustrates the use of various game elements of easy gamification using the example of the module “Level up! — Gamification” of the Moodle LMS. The capabilities of this module can be used in an electronic course by any teacher who has the skills of working with the Moodle.The authors present the analysis of the development of a training course in sales techniques using hard and light gamification technologies, where the course development was assessed for its complexity, manufacturability, and resource requirements. The results of the analysis showed that the development of courses using hard gamification requires much more financial and time-consuming than the development of courses using light gamification.The article evaluates the results of the educational intensiveness intense “Island 10–22”, held in July 2019 in Skolkovo, in which 100 university teams, teams of research and educational centers, teams of schoolchildren — winners of competitions, olympiads, hackathons (“Young Talents”) participated. The results of the intense confirmed the effectiveness of the use of light gamification methods in adult training. Thus, the conclusions presented in the article reveal a number of advantages that light gamification has in comparison with hard gamification.

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

Determination of starch crystallinity with the Fourier-transform terahertz spectrometer

2021 ◽  
Vol 262 ◽  
pp. 117928
Author(s):  
Shusaku Nakajima ◽  
Shuhei Horiuchi ◽  
Akifumi Ikehata ◽  
Yuichi Ogawa
Keyword(s):  
Fourier Transform ◽  
The Fourier Transform ◽  
Starch Crystallinity

Translation uniqueness of phase retrieval and magnitude retrieval of band-limited signals

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lung-Hui Chen
Keyword(s):  
Fourier Transform ◽  
Entire Function ◽  
Convex Hull ◽  
Scattering Theory ◽  
Phase Retrieval ◽  
Indicator Function ◽  
Band Limited ◽  
The Fourier Transform ◽  
Complex Valued ◽  
Spectral Invariant

Abstract In this paper, we discuss how to partially determine the Fourier transform F ⁢ ( z ) = ∫ - 1 1 f ⁢ ( t ) ⁢ e i ⁢ z ⁢ t ⁢ 𝑑 t , z ∈ ℂ , F(z)=\int_{-1}^{1}f(t)e^{izt}\,dt,\quad z\in\mathbb{C}, given the data | F ⁢ ( z ) | {\lvert F(z)\rvert} or arg ⁡ F ⁢ ( z ) {\arg F(z)} for z ∈ ℝ {z\in\mathbb{R}} . Initially, we assume [ - 1 , 1 ] {[-1,1]} to be the convex hull of the support of the signal f. We start with reviewing the computation of the indicator function and indicator diagram of a finite-typed complex-valued entire function, and then connect to the spectral invariant of F ⁢ ( z ) {F(z)} . Then we focus to derive the unimodular part of the entire function up to certain non-uniqueness. We elaborate on the translation of the signal including the non-uniqueness associates of the Fourier transform. We show that the phase retrieval and magnitude retrieval are conjugate problems in the scattering theory of waves.

Sign in / Sign up

Export Citation Format

Share Document