scholarly journals Robust spectral phase reconstruction of time-frequency entangled bi-photon states

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
Ilaria Gianani
Keyword(s):  
Spectral Phase ◽  
Phase Reconstruction ◽  
Time Frequency ◽  
Photon States

scholarly journals Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3204
Author(s):  
Robert Cruickshank ◽  
Gregor Henze ◽  
Rajagopalan Balaji ◽  
Bri-Mathias Hodge ◽  
Anthony Florita
Keyword(s):  
Energy Use ◽  
Time Series Data ◽  
Supply And Demand ◽  
Electric Utility ◽  
Series Data ◽  
Stochastic Methods ◽  
Electric Load ◽  
Phase Reconstruction ◽  
Time Frequency ◽  
Reconstruction Methods

Electric utility residential demand response programs typically reduce load a few times a year during periods of peak energy use. In the future, utilities and consumers may monetarily and environmentally benefit from continuously shaping load by alternatively encouraging or discouraging the use of electricity. One way to shape load and introduce elasticity is to broadcast forecasts of dynamic electricity prices that orchestrate electricity supply and demand in order to maximize the efficiency of conventional generation and the use of renewable resources including wind and solar energy. A binary control algorithm that influences the on and off states of end uses was developed and applied to empirical time series data to estimate price-based instantaneous opportunities for shedding and adding electric load. To overcome the limitations of traditional stochastic methods in quantifying diverse, non-Gaussian, non-stationary distributions of observed appliance behaviour, recent developments in wavelet-based analysis were applied to capture and simulate time-frequency domain behaviour. The performance of autoregressive and spectral reconstruction methods was compared, with phase reconstruction providing the best simulation ensembles. Results show spatiotemporal differences in the amount of load that can be shed and added, which suggest further investigation is warranted in estimating the benefits anticipated from the wide-scale deployment of continuous automatic residential load shaping. Empirical data and documented software code are included to assist in reproducing and extending this work.

scholarly journals Effects of noise on spectral phase reconstruction with wavelet analysis

2005 ◽  
Vol 54 (9) ◽  
pp. 4176
Author(s):  
Deng Yu-Qiang ◽  
Zhang Zhi-Gang ◽  
Chai Lu ◽  
Wang Qing-Yue
Keyword(s):  
Wavelet Analysis ◽  
Spectral Phase ◽  
Phase Reconstruction

scholarly journals Generalized spectral phase-only time-domain ptychographic phase reconstruction applied in nonlinear microscopy

2020 ◽  
Vol 37 (11) ◽  
pp. A285
Author(s):  
George Dwapanyin ◽  
Dirk Spangenberg ◽  
Alexander Heidt ◽  
Thomas Feurer ◽  
Gurthwin Bosman ◽  
...  
Keyword(s):  
Time Domain ◽  
Spectral Phase ◽  
Nonlinear Microscopy ◽  
Phase Reconstruction

Fractional Fourier Transform Image Analysis

2012 ◽  
Vol 198-199 ◽  
pp. 288-293
Author(s):  
Hui Yan Xu
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Fractional Fourier Transform ◽  
Point Of View ◽  
Fourier Domain ◽  
Phase Reconstruction ◽  
Time Frequency ◽  
Space Frequency ◽  
The Fourier Transform ◽  
Simulation Point

As a generalized form of the Fourier transform, fractional Fourier transform (FRFT) ,which is integrated the signal in time domain and frequency domain, is a new time-frequency analysis. From the simulation point of view to image the distribution of energy in the fractional Fourier domain, the amplitude and phase characteristics, simulation results show that any fractional Fourier domain, can reflect the image of the space-frequency domain characteristics, with the order, the image The distribution of the space-frequency domain characteristics will change. The image of the fractional Fourier transform amplitude and phase reconstruction of the image information with the original image also showed some important conclusions for the fractional Fourier transform applied to image recognition and edge detection is of great significance.

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