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Representation and Analysis of Statistical Signals

This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.

The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.

Description of Files

  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.

Each file can be run independently of the other.

About

Set of MATLAB examples for signal processing and frequency analysis

Topics

frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes

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0 stars

Watchers

1 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Representation and Analysis of Statistical Signals
This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.
The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.
Description of Files
  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.
Each file can be run independently of the other.
About
Set of MATLAB examples for signal processing and frequency analysis
Topics
frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes
Resources
Readme
Activity
Stars
0 stars
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Representation and Analysis of Statistical Signals
This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.
The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.
Description of Files
  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.
Each file can be run independently of the other.
About
Set of MATLAB examples for signal processing and frequency analysis
Topics
frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Representation and Analysis of Statistical Signals
This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.
The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.
Description of Files
  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.
Each file can be run independently of the other.
About
Set of MATLAB examples for signal processing and frequency analysis
Topics
frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Representation and Analysis of Statistical Signals
This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.
The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.
Description of Files
  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.
Each file can be run independently of the other.
About
Set of MATLAB examples for signal processing and frequency analysis
Topics
frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Representation and Analysis of Statistical Signals
This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.
The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.
Description of Files
  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.
Each file can be run independently of the other.
About
Set of MATLAB examples for signal processing and frequency analysis
Topics
frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Representation and Analysis of Statistical Signals
This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.
The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.
Description of Files
  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.
Each file can be run independently of the other.
About
Set of MATLAB examples for signal processing and frequency analysis
Topics
frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes
Resources
Readme
Activity
Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Representation and Analysis of Statistical Signals
This repository aims at providing some MATLAB code that can be used as examples for signal processing and analysis of noisy signals.
The code is written as a MATLAB live script, and can be read by using MATLAB. For easy viewing, PDF (directly view on Github) and HTML files (download and open on browser) are also available.
Description of Files
  1. Stochastic_Processes.mlx: Includes brownian motion (both correlated and non correlated), Poisson process, Markov Chains and diffusion process.
  2. Filtering_time_Series.mlx: Includes Moving Average (MA) process, noise smoothening, auto-covariance, Auto-regressive (AR) process and noisy sinusoid vs AR process.
  3. Time_Estimation.mlx: Includes estimation of auto and inter-correlation functions, applications of the autocorrelation function and the Levinson ALgorithm
  4. Frequency_Estimation.mlx: Includes the periodogram, used for estimating frequencies ina noisy signal. Also gives an implementation of the MUSIC algorithm for high resolution frequency estimation.
Each file can be run independently of the other.
About
Set of MATLAB examples for signal processing and frequency analysis
Topics
frequency-analysislevinson-recursionmatlabrandom-variablessignal-processingstochastic-processes
Resources
Readme
Activity
Stars
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