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CSE535: Mobile Computing Project - Handwritten Digit Recognition App

This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.

🌟 Key Features:

  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.

🚀 Technologies Used:

  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)

📘 Course:

This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.

📥 How to Use:

Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.

🤝 Contribution:

Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.

🙏 Thank you for exploring this Mobile Computing Project!

About

No description, website, or topics provided.

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0 stars

Watchers

1 watching

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2 forks
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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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CSE535: Mobile Computing Project - Handwritten Digit Recognition App
This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.
🌟 Key Features:
  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.
🚀 Technologies Used:
  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)
📘 Course:
This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.
📥 How to Use:
Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.
🤝 Contribution:
Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.
🙏 Thank you for exploring this Mobile Computing Project!
About
No description, website, or topics provided.
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Readme
Activity
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Watchers
1 watching
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2 forks
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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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CSE535: Mobile Computing Project - Handwritten Digit Recognition App
This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.
🌟 Key Features:
  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.
🚀 Technologies Used:
  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)
📘 Course:
This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.
📥 How to Use:
Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.
🤝 Contribution:
Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.
🙏 Thank you for exploring this Mobile Computing Project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
2 forks
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Used by
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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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CSE535: Mobile Computing Project - Handwritten Digit Recognition App
This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.
🌟 Key Features:
  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.
🚀 Technologies Used:
  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)
📘 Course:
This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.
📥 How to Use:
Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.
🤝 Contribution:
Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.
🙏 Thank you for exploring this Mobile Computing Project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
2 forks
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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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CSE535: Mobile Computing Project - Handwritten Digit Recognition App
This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.
🌟 Key Features:
  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.
🚀 Technologies Used:
  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)
📘 Course:
This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.
📥 How to Use:
Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.
🤝 Contribution:
Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.
🙏 Thank you for exploring this Mobile Computing Project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
2 forks
Report repository
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Used by
Contributors
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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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CSE535: Mobile Computing Project - Handwritten Digit Recognition App
This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.
🌟 Key Features:
  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.
🚀 Technologies Used:
  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)
📘 Course:
This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.
📥 How to Use:
Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.
🤝 Contribution:
Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.
🙏 Thank you for exploring this Mobile Computing Project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
2 forks
Report repository
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Used by
Contributors
Languages

, '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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CSE535: Mobile Computing Project - Handwritten Digit Recognition App
This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.
🌟 Key Features:
  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.
🚀 Technologies Used:
  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)
📘 Course:
This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.
📥 How to Use:
Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.
🤝 Contribution:
Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.
🙏 Thank you for exploring this Mobile Computing Project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
2 forks
Report repository
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Packages
Used by
Contributors
Languages

, '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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CSE535: Mobile Computing Project - Handwritten Digit Recognition App
This repository contains the project developed for CSE535: Mobile Computing, representing a sophisticated Android application designed for recognizing handwritten digits. The app integrates a cutting-edge machine learning model and employs a peer-to-peer computing approach, incorporating a master-slave architecture, to ensure precise digit recognition.
🌟 Key Features:
  • 🖊 Handwritten Digit Recognition:
    • Implements advanced machine learning models for accurate recognition of handwritten digits.
  • 🤖 Peer-to-Peer Computing:
    • Employs a robust master-slave architecture to enable efficient computing between peers.
  • 🛠 Technologically Advanced:
    • Developed using Java in the Android Studio environment, adhering to optimal coding practices and standards.
🚀 Technologies Used:
  • Programming Language: Java
  • Development Environment: Android Studio
  • Computing Architecture: Peer-to-Peer (Master-Slave)
📘 Course:
This application is a dedicated endeavor under the CSE535: Mobile Computing course, aiming to delve deep into the exploration and implementation of diverse mobile computing paradigms and applications.
📥 How to Use:
Refer to the installation instructions encapsulated in the installation guide to configure the app on your Android device and navigate through its extensive functionalities.
🤝 Contribution:
Feel encouraged to fork this project, contribute, and initiate pull requests for enhancing the application or extending its feature set. For reporting issues or suggesting enhancements, please utilize the issue tracker.
🙏 Thank you for exploring this Mobile Computing Project!
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
2 forks
Report repository
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Used by
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