Agile frequency transformations for dense wavelength-multiplexed communications

The results of the analysis of time-frequency transformations based on the systematization of their main characteristics in the tasks of processing and analyzing patterns of non-stationary quasi-periodic signals are presented, the advantages and disadvantages of using each of the transformations are specified

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Design of Modified Second-Order Frequency Transformations Based Variable Digital Filters With Large Cutoff Frequency Range and Improved Transition Band Characteristics

IEEE Transactions on Very Large Scale Integration (VLSI) Systems ◽

10.1109/tvlsi.2015.2411620 ◽

2016 ◽

Vol 24 (2) ◽

pp. 413-420 ◽

Cited By ~ 2

Author(s):

Sumedh Dhabu ◽

Vinod Achutavarrier Prasad

Keyword(s):

Digital Filters ◽

Cutoff Frequency ◽

Second Order ◽

Frequency Range ◽

Transition Band ◽

Frequency Transformations

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Transfer of Learning in Transformed Random-Dot Stereostimuli

Perception ◽

10.1068/p110409 ◽

1982 ◽

Vol 11 (4) ◽

pp. 409-414 ◽

Cited By ~ 2

Author(s):

Nigel R Long

Keyword(s):

Spatial Frequency ◽

High Frequency ◽

Transfer Of Learning ◽

Low Frequency ◽

Frequency Characteristics ◽

Frequency Transformations

The transfer of learning between normal and monocularly-transformed small-disparity, random-dot stereostimuli has been examined under extended viewing conditions. When the disparity value was constant, transfer of learning between normal and monocularly-transformed stereostimuli was disrupted by both low-frequency and high-frequency transformations. These results suggest that stereolearning is restricted to disparity units that are selective to the same spatial-frequency characteristics.

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Vibration Parameters Study of Ship Structures Using Wavelet Analysis of Vibrogram

Journal of Siberian Federal University Engineering & Technologies ◽

10.17516/http://1999-494x-0310 ◽

2021 ◽

pp. 292-305

Author(s):

Pavel A. Starodubtsev ◽

Grigory V. Dorofeev ◽

Andrey O. Lipovetskiy

Keyword(s):

Fourier Transform ◽

Wavelet Analysis ◽

Measurement Data ◽

Noise Signal ◽

Vibration Measurement ◽

Ship Structures ◽

Average Value ◽

Signal Synthesis ◽

Frequency Transformations ◽

Vibration Parameters

The study of the vibration parameters of ship structures is important for developing measures to ensure their reliable operation on ships. The commonly used analysis of vibrograms using the Continuous Fourier Transform (CFT) to accurately represent non-stationary functions in general and noise source signals in particular is unsuitable due to a number of drawbacks. The problems of spectral analysis and time-limited signal synthesis can be partially solved by switching to the Window Fourier Transform (WFT). The disadvantage of the WFT is that its calculation uses a fixed window, which cannot be adapted to the local properties of the signal. In order to get rid of this shortcoming for the analysis of vibrogram you can use wavelet transform. It also solves a number of other problems related to the processing of a noise signal. The word “wavelet” means small waves following each other (some sources have introduced the concept of “splash”). In a narrow sense, wavelets are a family of functions obtained by scaling and shifting a single, parent function. In a broad sense, wavelets are functions with frequency localization, whose average value is zero. The article shows the signs of a wavelet. Examples of the most common wavelet functions are given. The use of wavelet functions is proposed not only on the basis of time, but also frequency transformations. The implementation of the algorithm for analyzing vibration measurement data is proposed. An example of vibration measurement data and the results of their processing based on frequency wavelet analysis are given

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