Cross Correlation Theorem

The Meaning of Cross Correlation Theorem

Let's take a closer look at the Cross Correlation Theorem. There are a few key concepts you have to grasp.

The Cross Correlation function measures how much two signals 'agree' with each other for a given time shift. If the two signals match up perfectly, the correlation is 1. If they are the exact opposite, the correlation is -1. Anywhere between those values, the correlation signifies the degree of similarity.

Cross Correlation Theorem Simplified

The Cross Correlation Theorem, no doubt, is a hefty concept to swallow. But, don't worry. You're not alone in this discovery journey. We're going to break it down, slice by slice.

The theorem states that the cross-correlation of two signals in the time domain is equal to the product of their respective Fourier Transforms conjugate multiplied in the frequency domain. \[ CrossCorrelation(f, g)(t)=\int f^{*}(s)g(s+t) ds \] Everything right from the \( \int \) which denotes an integral (akin to summing up all the values), to \( f^{*}(s) \) and \( g(s+t) \), which represent our two signals, all combine to form the base of the Cross Correlation Theorem.

def CrossCorrelation(f, g):
    conj_f = numpy.conj(f)
    return scipy.signal.fftconvolve(conj_f, g, mode='same')

The code snippet above is an example of how the Cross Correlation can be calculated for two digital signals in a computer, using the Python programming language and some scientific computation libraries (numpy and scipy).

Demonstrating the Cross Correlation Theorem

Let's take it up a notch. Having gained an understanding of what the Cross Correlation Theorem is, it's time to put it into action. Seeing a theorem at work can significantly help strengthen your grasp of the concept. So how about we roll up our sleeves and work through some examples?

Example of Cross Correlation Theorem

Let's consider a simple example where we use the Cross Correlation Theorem to find the time shift between two signals. We are going to use two signals here: one is a sinusoidal signal, and the other one is the same signal but delayed by a certain time.

Our sample signals can be represented as, \( f(t) = sin(t) \), the undelayed signal, and \( g(t) = sin(t+\alpha) \), the delayed signal, where \( \alpha \) is the time shift between the two signals.

We can calculate the Cross Correlation of these two signals using the formula for cross correlation: \[ CrossCorrelation(s, g)(t)=\int f^{*}(t)g(t+\tau) dt \]

import numpy as np
import matplotlib.pyplot as plt

# Sample signals
f = np.sin(t)
g = np.sin(t + 5)

# Cross correlation
cross_correlation = np.correlate(f, g, 'same')

# Displaying the cross correlation 
plt.plot(cross_correlation)
plt.show()

Here's what's happening in the code above: We use the numpy and matplotlib libraries in Python. Numpy provides functions for working with arrays and matrices, and matplotlib is used for plotting the results. The numpy correlate function calculates the cross correlation of two signals. In our example, we use two sinusoidal signals with a delay of 5 time units. When we plot the cross correlation signal, we observe a peak at the point corresponding to the time delay we introduced, indicating strong correlation.

Practical Usage: Cross Correlation Theorem Example

Now that you're familiar with the workings of the Cross Correlation Theorem, let's dig a little deeper and discover some practical applications of this fascinating principle.

Happen to have heard of Spread Spectrum Communications? It's a communications technique where the transmitted signal is spread over a wide frequency band that's much wider than the minimum bandwidth required to transfer the information. This is typically done using a code sequence that solely the sending and receiving ends know. And guess what? The Cross Correlation Theorem comes in very handy here.

Imagine a scenario where we are transmitting a clean coded signal \( c(t) \), but when this signal reaches the receiver, it ends up being mixed with unwanted noise \( n(t) \) and thus, can be expressed as \( x(t) = c(t) + n(t) \).

from scipy import signal

# Clean signal 
c = np.random.choice([1,-1], size=10000)

# Noise signal
n = np.random.normal(size=c.shape)

# Received signal (Coded signal + noise)
x = c + n

# Decoding the received signal
cross_correlation = signal.correlate(x, c, mode='valid')

plt.plot(cross_correlation)
plt.show()

In the above Python code, we use the scipy library to generate a random coded signal and add the normal random noise to it. Now, the purpose is to recover the original signal \( c(t) \) from the received noisy signal \( x(t) \). This is simply done by cross-correlating the received signal with the original coded signal. Cross correlating the noisy received signal with the code signal accurately retrieves the signal since the cross correlation of random noise with anything tends to average out to zero, leaving behind just the correlation of the received signal with the code signal.

Applications like these echo the value of the Cross Correlation Theorem in our day-to-day technology and communications.

Interrelation between Theorems

Unlocking the power of any substantial theorem often requires understanding its relationship with other principles in the field. In the realm of signal processing and statistics, such connections are not only common but deeply intertwined. For computational problem-solving, engineers often leverage these interrelations for more efficient results.

Wiener Khinchin Theorem and Cross Correlation

The Wiener Khinchin Theorem is notably foundational in the sphere of signal processing. It essentially represents the connection between the autocorrelation function and the power spectral density of a signal.

Wiener Khinchin theorem states that the power spectrum of a signal is the Fourier transform of its autocorrelation. This connection between power spectral density and autocorrelation is critical in signal processing and system analysis.

def autocorrelation(f):
    return scipy.signal.fftconvolve(f, f[::-1], mode='full')
    
def powerSpectralDensity(f):
    return np.abs(np.fft.fft(f))**2

The Python code snippet above demonstrates how to compute the autocorrelation and power spectral density of a signal using the scipy and numpy libraries. The function 'autocorrelation' computes the convolution of a signal with its reversed version which gives us its autocorrelation. The function 'powerSpectralDensity' computes the Fourier Transform of the signal and raises it to the power of two, giving us the power spectral density of the signal.

Now, how is this related to the Cross Correlation Theorem, you ask? No doubt, these two have quite a close bearing. If you compare the formulas of the two, the only major distinction is in the underlying signals being processed. Autocorrelation, unlike Cross Correlation, analyses the same signal, just at different times. If we replace one of the signals in Cross Correlation with the other one, it essentially turns into Autocorrelation. In this way, Autocorrelation is a special case of Cross Correlation.

Comparison: Cross Correlation vs Convolution Theorem

With a good grasp of the Cross Correlation Theorem, we can pit it up against another fundamental theorem in signals and systems- The Convolution Theorem. This theorem is a cornerstone in Fourier analysis and establishes a relationship between the Fourier transform of a function's convolution and the pointwise product of their Fourier transforms.

def Convolution(f, g):
    return scipy.signal.fftconvolve(f, g, mode='same')

The Python code snippet represents how to compute the convolution of two signals using the scipy library.

While it's tempting to mistake Cross Correlation for Convolution due to their similar mathematical structures, a critical variance exists. In Convolution, one of the signals is first reversed before 'sliding' it across the other signal, unlike Cross Correlation where no reversing is done.

This equation clearly depicts the convolution of two signals, 'f' and 'g'. The notation \( f * g \) is classic for the convolution operation. Take note that the reversal of the signal 'g' is evident by \( g(t-u) \) replacing \( g(u) \).

So what does this difference make? Consider a scenario where you have two signals: one of a sound wave and another of its echo. If we were to use convolution to analyse these signals, we would be flipping one of them, which would distort the intended comparison. So, for tasks like these, which require the computation of similarity between two signals without flipping, Cross Correlation takes precedence over Convolution.

On the other hand, Convolution dominates in situations dealing with systems' outputs based on their inputs and impulse responses. In these cases, the 'flip and slide' of Convolution aligns perfectly with the chronological order of cause-effects.

Understanding this essential difference and opting for Cross Correlation or Convolution accordingly undoubtedly takes you one step ahead in your engineering journey with signals and systems.

Diving into the Details of Cross Correlation Theorem

Before we can effectively use the Cross Correlation Theorem, it's vital to have a deep, firm grasp of what it really stands for. This theorem is at the bedrock of signal processing, systems theory and several areas of engineering. It can be particularly helpful in determining the similarity between two signals, identifying the time delay between them, or recognising a signal within a noisy background.

Proof of the Cross Correlation Theorem

Let's delve right into the spirit of engineering, the way a true engineer would, and try to prove the Cross Correlation Theorem.

Remember, the Cross Correlation Theorem states that the Fourier Transform of the cross correlation of two signals is equal to the product of the Fourier Transform of the first signal and the complex conjugate of Fourier Transform of the second signal. Mathematically expressed as:

Given, the Fourier Transform pair \( f(t) \longleftrightarrow F(\omega) \) , \( g(t) \longleftrightarrow G(\omega) \).

The cross correlation of \( f(t) \) and \( g(t) \) is: \r[ f(t) * g(t) = \int_{-\infty}^{\infty} f(\tau)g(t-\tau) d\tau \r]

Now, taking the Fourier Transform of this expression, \[ \mathcal{F}\{f(t) * g(t)\} = \mathcal{F}\{\int_{-\infty}^{\infty} f(\tau)g(t-\tau) d\tau\} \]

Through the Linearity property of the Fourier Transform, we can bring the integral to the outside: \[ = \int_{-\infty}^{\infty} f(\tau) \mathcal{F}\{g(t-\tau)\} d\tau \]

Applying the Time Shift property of Fourier Transform: \[ = \int_{-\infty}^{\infty} f(\tau) e^{j\omega\tau} G(\omega) d\tau \]

Pulling out \(G(\omega)\) from the integral as it is not a function of \(\tau\), \[ = G(\omega) \int_{-\infty}^{\infty} f(\tau) e^{j\omega\tau} d\tau \]

Now it is visible that the expression inside the integral is the Fourier Transform of \(f(t)\), denoted as \(F(\omega)\). \[ = F(\omega) . G(\omega) \]

Therefore, this proof confirms the Cross Correlation Theorem, i.e., \[ \mathcal{F}\{f(t) * g(t)\} = \mathcal{F}\{f(t)\} . \mathcal{F}\{g(t)\}^{*} \]

Cross Correlation Theorem Formula and its Interpretation

The mathematical expression of the Cross Correlation Theorem is quite enlightening once you understand what it conveys.

Let's dissect the formula to extract its essence. As already stated, the Cross Correlation Theorem is generally expressed as:

In this equation:

• \(f(t)\) and \(g(t)\) are the two signals we are working with.
• '*' denotes the cross correlation operation.
• \(\mathcal{F}\) signifies the Fourier Transform function.
• '.' signifies the multiplication operation.
• \(^\{*}\) denotes the complex conjugate operation.

The left-hand side of the equation represents the Fourier Transform of the cross correlation of the two signals \(f(t)\) and \(g(t)\) while the right-hand side of the equation represents the multiplication of the Fourier Transform of the first signal by the complex conjugate of the Fourier Transform of the second signal.

This theorem reveals a crucial harmonic footprint of the Cross Correlation function in the frequency domain, that is, it is the multiplication of the Fourier Transform of one function with the conjugate of the Fourier Transform of the other function.

In other words, the Cross Correlation Theorem transforms the cross correlation operation in the time domain to a basic multiplication operation in the frequency domain. This enables the possibility of frequency-domain-based operations which are computationally much more efficient, hence the theorem's extensive use in digital signal processing.

To sum up, the Cross Correlation Theorem not only expands our understanding of signal processing techniques in relation to one another, but it also paves the way for computationally simpler methods to analyse signals.

Utilising the Cross Correlation Theorem

To harness the full potential of the Cross Correlation Theorem, it’s vital to understand its practical applications in mathematics, engineering and the physical sciences. Whether it's to determine the degree of similarity between two signals or to identify the presence of one signal within a cluttered, noisy output, employing the theorem can enable a clear, unambiguous determination.

Applications of Cross Correlation Theorem

The ability of the Cross Correlation Theorem to translate cross correlation from the time domain to the frequency domain has wide-ranging applications across numerous fields. These range from signal and system analysis to complex imaging techniques. Below are a few of these applications.

Signal Processing and System Analysis: In the realm of signal processing and system analysis, the Cross Correlation Theorem is regularly employed. For instance, the theorem provides a valuable means to establish the degree of resemblance between two signals. In a typical case, this might include comparing a raw input signal with a signal that has passed through a given system, enabling the detection and analysis of any resulting alterations.

The theorem also facilitates the identification of a specific signal within a noisy output. For example, it allows engineers to extract vital information from signals indistinguishable from background noise in real-world environments. This technique is widely used in telecommunications, radar, and acoustics.

Pattern Recognition: The Cross Correlation Theorem also holds immense relevance in the field of pattern recognition. Through the theorem, a template of the desired signal (also known as a kernel) may be cross-correlated with a larger database. The output peak of this cross correlation operation signifies where the template matches the database most closely.

Structural Analysis in Bioinformatics: In bioinformatics, the theorem affords a means to compare protein structures. By cross correlating the secondary structure elements (helices, strands and coils) of two protein structures, substantial insights can be gleaned about the functional similarities and evolutionary relationships between proteins.

Extensive Use Cases: Cross Correlation Theorem Applications

The application of the Cross Correlation Theorem extends well beyond these initial considerations, evidencing its fundamental importance in a broad array of practices.

Geophysics: In geophysics, the theorem affords a powerful tool in the monitoring of earthquakes. By cross correlating the seismic waves recorded at two different observation stations, it's possible to both accurately locate the epicentre of an earthquake and track the propagation of seismic waves.

Astronomy: In the domain of astronomy, the theorem is employed in interferometry to calculate and compensate the delay in signals received by different telescopes. This allows astronomers to combine signals from multiple telescopes to produce images with higher resolution than could be obtained with any single telescope.

Medical Imaging: The Cross Correlation Theorem has also been put to use in intricate medical imaging techniques like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). For instance, to reconstruct images from the raw data generated in these techniques, one relies upon the Fourier transform. However, this raw data might at times get corrupted due to physical or technical reasons, appearing as streaks or irregularities in the image. Echoing the definition of Cross Correlation, you compare these corrupted images with a set of saved standard image signals, so as to identify and correct these defects.

Given this vast applicability, it's clear that the Cross Correlation Theorem is not just a mathematical novelty, but it firmly imprints an unwavering influence on today's scientific advancements.

Machine Learning: Within the rapidly developing scope of machine learning, the Cross Correlation Theorem is applied in the field of convolutional neural networks (CNNs). These networks are used for image and video processing tasks, including image classification, object detection, and semantic segmentation. Here an input image is 'cross correlated' with a set of learnable filters (also known as kernels) to extract important features from the image. Through this cross correlation operation at each layer of the network, the CNN progressively learns to recognise intricate patterns and features.

def cross_correlation(image, filter):
    return scipy.signal.correlate2d(image, filter, mode='valid') 

This Python code snippet represents a cross correlation operation for a 2-dimensional input image and filter, using the scipy library. 'Mode' is set to 'valid' which means that it does not perform any zero-padding on the inputs and it is only computed where the inputs overlap completely.

These wide-ranging applications substantiate the versatility and essential role that the Cross Correlation Theorem plays in connecting underlying mathematical principles to practical real-world engineering and scientific solutions.