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Properties of Quantum Transport
Published in Jian-Bai Xia, Duan-Yang Liu, Wei-Dong Sheng, Quantum Waveguide in Microcircuits, 2017
Jian-Bai Xia, Duan-Yang Liu, Wei-Dong Sheng
The double‐barrier resonant tunneling diode (DBRTD) is a typical device of quantum mechanical effect. The Wigner distribution function is calculated from the density matrix through the Fourier transform, often called a Weyl transform. The Wigner distribution has been used to model the DBRTD. It is found that the Wigner function shows a depletion region in the cathode area, which arises from a contact potential drop and the tendency to form a bound state in this area. Such contact potential drops are typical of most open systems, whether classical or quantum. Generally the cathode “barrier” will develop when there is a mismatch between the injection characteristics of the cathode reservoir and the dissipative nature of the active device region. It is largely eliminated if a lightly doped region is introduced adjacent to the barrier layers.
Wigner Distribution Function
Published in Toyohiko Yatagai, Fourier Theory in Optics and Optical Information Processing, 2022
The Fourier transform analysis can describe the relation between a real signal domain and its spectral domain. The Wigner distribution function (WDF) is a 2-D function, which can represent the signal together in the real and frequency domains. Its projection to the real or frequency axis gives the power in a spectral or real domain. The WDF can describe some optical functions, such as the lens effect, the phase modulation, the diffraction grating, the wave propagation, and so on. Its applications to optical signal processing are presented. The four dimensional 4-D WDF in the space-time signal is also introduced.
Diffraction and Signal Analysis
Published in Francis T. S. Yu, Entropy and Information Optics, 2017
which is known as the Wigner distribution function (WDF). Instead of using correlation operator [i.e., u(t)u*(t + τ)], Wigner used the convolution operator (i.e., u(t)u*(τ – t)) for his transformation. The physical properties of the WDF can be shown as
Power quality disturbances classification based on curvelet transform
Published in International Journal of Computers and Applications, 2018
Yue Shen, Fida Hussain, Hui Liu, Destaw Addis
Feature extraction is an important stage to recognize and classify the PQDs and appropriate selection of feature technique. It can significantly enhance the performance of detection and classification accuracy. Many techniques have been revealed for feature extraction of PQDs’ classification, for example wavelet transform (WT), empirical mode decomposition, compressive sensing (CS), S-transform, Kalman filter, short-time Fourier transform (STFT), wavelet packet transform, curvelet based, Hilbert transform, Hilbert–Haung transform, hybrid transform-based methods, Gabor transform, Wigner distribution function, and over-complete hybrid dictionaries [7–16]. These features are used as input to a classifier such as fuzzy logic, ensemble technique, support vector machine (SVM), deep learning, rule base, artificial neural network (NN), expert system, and maximum likelihood classifier [17–25].