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Thesis: WuotD: beacon
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@ -174,8 +174,9 @@ Taking one antenna as the reference antenna with $(\tClock)_r = 0$, the mismatch
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% signals to send, and measure, (\tTrueArriv)_i.
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In the former, the mechanism of measuring $(\tMeasArriv)_i$ from the signal has been deliberately left out.
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The nature of the beacon allows for different methods to determine $(\tMeasArriv)_i$.\Todo{reword towards next sections?}
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The nature of the beacon allows for different methods to determine $(\tMeasArriv)_i$.
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In the following, two approaches for measuring $(\tMeasArriv)_i$ are examined.
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\Todo{reword towards next sections?}
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%%%%
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@ -286,17 +287,22 @@ This relies on the ability of counting how many beacon periods have passed since
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\bigskip
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% Yay for the sine wave
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In the following, the scenario of a (single) sine wave as a beacon is worked out.
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This involves the tuning of the signal strength to attain the required accuracy.
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Later, a mechanism to lift the period degeneracy using an airshower as discrete signal is shown.
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In the following section, the scenario of a (single) sine wave as a beacon is worked out.
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It involves the tuning of the signal strength to attain the required accuracy.
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Later, a mechanism to lift the period degeneracy using an airshower as discrete signal is presented.
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%%
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%% Phase measurement
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\subsection{Phase measurement}
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A continuous beacon can syntonise antennas by correcting for the measured difference in beacon phase $(\Delta \pMeasArriv)_{ij}$.
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The beacon phase can be derived from an antenna trace by applying a Fourier Transform to the data.
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\\
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The trace will contain noise from various sources external and internal to the detector such as
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A continuous beacon can syntonise an array of antennas by correcting for the measured difference in beacon phases $(\Delta \pMeasArriv)_{ij}$.
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They are derived by applying a \gls{FT} to the traces of each antenna.
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The digital measurement of the beacon phase is dependent on at least two factors:
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the strength of the beacon in comparison to other signals (such as noise) and the length of the traces.
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Additionally, the \gls{FT} can be performed in a number of ways.
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These aspects are examined in the following section.
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\begin{figure}[h]
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\begin{subfigure}{0.45\textwidth}
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@ -330,35 +336,94 @@ The trace will contain noise from various sources external and internal to the d
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}
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\label{fig:beacon:ttl_sine_beacon}
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\end{figure}
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% DTFT
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\subsubsection{Discrete Time Fourier Transform}
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% FFT common knowledge ..
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The typical \gls{FT} to obtain spectral information from periodic data is the \gls{FFT} (a fast implementation of the \gls{DFT} \eqref{eq:fourier:dft}).
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Such an algorithm efficiently finds the magnitudes and phases within a trace $x$ at specific frequencies $f = f_s \tfrac{k}{N}$ determined solely by the number of samples $N$ ($0 \leq k < N$) and the sampling frequency $f_s$.
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\\
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% .. but we require a DTFT
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Depending on the frequency of the beacon, the sampling frequency and the number of samples, one can resort to use such a \gls{DFT}.
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However, if the frequency of interest is not covered in the specific frequencies, the approach must be modified (e.g. zero-padding or interpolation).
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Especially when a single frequency is of interest, a shorter route can be taken by evaluating a discretized \gls{FT} directly.
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\\
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% DTFT from CTFT
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Spectral information in data can be obtained using a \acrlong{FT}.
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\begin{equation}
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\label{eq:fourier}
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X(\omega) = \frac{1}{2\pi} \int \dif{t}\, x(t)\, e^{i \omega t}
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X(f) = \frac{1}{2\pi} \int_\infty^\infty \dif{t}\, x(t)\, e^{i 2 \pi f t}
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\end{equation}
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The general (continuous) \gls{FT} \eqref{eq:fourier} can be discretized in time to result in the \acrlong{DTFT}:
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\begin{equation}
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\label{eq:fourier:dtft}
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X(\omega) = \frac{1}{2\pi N} \sum_{n=0}^N x(t[n])\, e^{i \omega t[n]}
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X(f) = \frac{1}{2\pi N} \sum_{n=0}^{N-1} x(t[n])\, e^{i 2 \pi f t[n]}
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\end{equation}
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where $X(f)$ is the transform of $x(t)$ at frequency $f$, sampled at $t[n]$.
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\\
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\bigskip
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% DFT sampling of DTFT / efficient multifrequency FFT
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When the sampling of $x(t)$ is equally spaced, the $t[n]$ terms can be decomposed as a sequence, $t[n] = \tfrac{n}{f_s}$ such that \eqref{eq:fourier:dtft} becomes the \acrlong{DFT}:
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\begin{equation}
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\label{eq:fourier:dft}
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X_k = \frac{1}{N} \sum_{n=0}^{N-1} x[n]\, \cdot e^{ \frac{i 2 \pi}{N} k n }
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\phantom{.}
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X(k) = \frac{1}{N} \sum_{n=0}^{N-1} x[n]\, \cdot e^{ i 2 \pi {\frac{k n}N} }
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.
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\end{equation}
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with $\omega = \tfrac{k}{N}$.
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% FT term to phase and magnitude
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\bigskip
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The magnitude of at frequency $f$
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\bigskip
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% Beacon frequency known -> single DTFT run
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When the beacon frequency is known, a single \gls{DTFT} needs to be evaluated.
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From this $X(f)$, the magnitude $A$ and phase $\pTrue$ are derived using
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\begin{equation}
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\label{eq:magnitude_and_phase}
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\phantom.
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A(f) = {\left|X(f)\right|}^2
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\hfill
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\pTrue(f) = \arctantwo\left(\Re(X(f)), \Im(X(f))\right)
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.
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\end{equation}
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The decomposition of $X(f)$ into a real and imaginary part
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With a constant beacon frequency, the coefficients for evaluating the \gls{DTFT} can be put into the hardware of the detectors.
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% Beacon frequency unknown -> either zero-padding FFT or DTFT grid search
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% Removing the beacon from the signal trace
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% Signal to noise
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\subsubsection{Signal to Noise}
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% Gaussian noise
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The traces will contain noise from various sources, both internal (e.g. LNA) and external (e.g. radio communications) to the detector.
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Adding gaussian noise to the traces in simulation gives a simple noise model, associated to many random noise sources.
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Especially important is that this simple noise model will affect the phase measurement depending on the strength of the beacon with respect to the noise level.
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\\
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\bigskip
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Phasor concept
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\cite{goodman1985:2.9}
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Known phasor $\vec{s}$ + random phasor $\vec{m} = a e^{i\pTrue}$ with $-\pi < \pTrue < \pi$ and $a \geq 0$.
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\begin{equation}
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\label{eq:phasor_pdf}
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\label{eq:random_phasor_pdf}
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p_{A\PTrue}(a, \pTrue; s, \sigma)
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= \frac{a}{2\pi\sigma^2}
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\exp[ -
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@ -373,20 +438,8 @@ Known phasor $\vec{s}$ + random phasor $\vec{m} = a e^{i\pTrue}$ with $-\pi < \p
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requiring $ -\pi < 0 \leq pi $ and $a > 0$, otherwise $p_{A\PTrue} = 0$.
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\bigskip
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Rician distribution ( 2D Gaussian at $\nu$ with $\sigma$ spread)
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\begin{equation}
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\label{eq:amplitude_pdf:rice}
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p^{\mathrm{RICE}}_A(a; s, \sigma)
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= \frac{a}{\sigma^2}
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\exp[-\frac{a^2 + s^2}{2\sigma^2}]
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\;
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I_0\left( \frac{a s}{\sigma^2} \right)
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\end{equation}
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with $I_0(z)$ the modified Bessel function of the first kind with order zero.
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No signal $\mapsto$ Rayleigh ($s = 0$);
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Large signal $\mapsto$ Gaussian ($s \gg a$)
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\bigskip
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Noise only Amplitude:
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Rayleigh distribution
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\begin{equation}
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\label{eq:amplitude_pdf:rayleigh}
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@ -404,10 +457,24 @@ Gaussian distribution
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\end{equation}
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\bigskip
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Rician phase distribution: uniform (low $s$) + gaussian (high $s$)
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Rician distribution ( 2D Gaussian at $\nu$ with $\sigma$ spread)
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\begin{equation}
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\label{eq:phase_pdf:full}
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\label{eq:amplitude_pdf:rice}
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p^{\mathrm{RICE}}_A(a; s, \sigma)
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= \frac{a}{\sigma^2}
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\exp[-\frac{a^2 + s^2}{2\sigma^2}]
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\;
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I_0\left( \frac{a s}{\sigma^2} \right)
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\end{equation}
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with $I_0(z)$ the modified Bessel function of the first kind with order zero.\\
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No signal $\mapsto$ Rayleigh ($s = 0$);\\
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Large signal $\mapsto$ Gaussian ($s \gg a$)
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\bigskip
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Random Phasor Sum phase distribution: uniform (low $s$) + gaussian (high $s$)
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\begin{equation}
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\label{eq:phase_pdf:random_phasor_sum}
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p_\PTrue(\pTrue; s, \sigma) =
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\frac{ e^{-\left(\frac{s^2}{2\sigma^2}\right)} }{ 2 \pi }
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+
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@ -85,6 +85,7 @@
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\newcommand{\Corr}{\operatorname{Corr}}
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%\newcommand{\erf}{\operatorname{erf}}
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\DeclareMathOperator{\arctantwo}{arctan2}
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% Units
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\newacronym{PA}{PA}{Pierre~Auger}
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\newacronym{PAObs}{PAO}{Pierre~Auger Observatory}
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\newacronym{AERA}{AERA}{Auger Engineering Radio Array}
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\newacronym{DTFT}{DTFT}{Discrete Time Fourier Transform}
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\newacronym{DFT}{DFT}{Discrete Fourier Transform}
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\newacronym{FFT}{FFT}{Fast Fourier Transform}
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\newacronym{FT}{FT}{Fourier Transform}
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