Added spark animations to main logo

assets/css/custom.css

@@ -166,3 +166,15 @@ article video {
   max-width: 100%;
   height: auto;
 }
+
+/* Main page logo with programmatically generated sparks */
+body:has(.background-container) article.glass figure:first-of-type {
+  position: relative;
+  isolation: isolate;
+  display: inline-block;
+}
+
+body:has(.background-container) article.glass figure:first-of-type img {
+  position: relative;
+  z-index: 0;
+}

assets/js/logo-sparks.js

@@ -0,0 +1,118 @@
+// Programmatic spark generation for homepage logo
+(function() {
+  const config = {
+    numSparks: 9,
+    minSize: 1.5,
+    maxSize: 3.5,
+    minRadius: 180,
+    maxRadius: 350,
+    minDuration: 23,
+    maxDuration: 49,
+    pathComplexity: 17,
+    behindLogoFraction: 0.75,
+    minOpacity: 0.5,
+    maxOpacity: 0.8,
+    staggerAnimations: true
+  };
+
+  function random(min, max) {
+    return Math.random() * (max - min) + min;
+  }
+
+  function generatePath(radius, complexity) {
+    const points = [];
+    for (let i = 0; i <= complexity; i++) {
+      const angle = (i / complexity) * Math.PI * 2;
+      const r = random(radius * 0.8, radius * 1.2);
+      const x = Math.cos(angle) * r + random(-50, 50);
+      const y = Math.sin(angle) * r + random(-50, 50);
+      points.push({ x, y, scale: random(0.7, 1.3) });
+    }
+    return points;
+  }
+
+  function createKeyframes(id, path, isBehind) {
+    const steps = path.length;
+    let keyframes = '@keyframes spark' + id + ' {\n';
+    
+    path.forEach((point, i) => {
+      const percent = (i / (steps - 1)) * 100;
+      const opacity = (i > 1 && i < steps - 2) ? random(config.minOpacity, config.maxOpacity) : 0;
+      
+      let opacityValue = opacity;
+      if (isBehind) {
+        const hiddenStart = random(30, 45);
+        const hiddenEnd = random(55, 70);
+        if (percent > hiddenStart && percent < hiddenEnd) {
+          opacityValue = 0;
+        }
+      }
+      
+      keyframes += `  ${percent.toFixed(1)}% {\n`;
+      keyframes += `    transform: translate(-50%, -50%) translateX(${point.x.toFixed(1)}px) translateY(${point.y.toFixed(1)}px) scale(${point.scale.toFixed(2)});\n`;
+      keyframes += `    opacity: ${opacityValue.toFixed(2)};\n`;
+      keyframes += `  }\n`;
+    });
+    
+    keyframes += '}\n';
+    return keyframes;
+  }
+
+  function initSparks() {
+    const logoFigure = document.querySelector('body:has(.background-container) article.glass figure:first-of-type');
+    if (!logoFigure) return;
+
+    let allKeyframes = '';
+    const sparks = [];
+
+    for (let i = 0; i < config.numSparks; i++) {
+      const spark = document.createElement('span');
+      spark.className = 'logo-spark';
+      spark.dataset.sparkId = i;
+      
+      const size = random(config.minSize, config.maxSize);
+      const duration = random(config.minDuration, config.maxDuration);
+      const radius = random(config.minRadius, config.maxRadius);
+      const isBehind = Math.random() < config.behindLogoFraction;
+      
+      const delay = config.staggerAnimations 
+        ? random(0, config.maxDuration) 
+        : 0;
+      
+      spark.style.cssText = `
+        position: absolute;
+        width: ${size}px;
+        height: ${size}px;
+        border-radius: 50%;
+        background: radial-gradient(circle, rgba(255, 255, 255, 0.9), rgba(74, 158, 255, 0.6), transparent);
+        box-shadow: 0 0 8px rgba(74, 158, 255, 0.8), 0 0 4px rgba(255, 255, 255, 0.6);
+        top: 50%;
+        left: 50%;
+        pointer-events: none;
+        z-index: ${isBehind ? -1 : 1};
+        animation: spark${i} ${duration}s linear infinite;
+        animation-delay: -${delay.toFixed(2)}s;
+      `;
+      
+      const path = generatePath(radius, config.pathComplexity);
+      allKeyframes += createKeyframes(i, path, isBehind);
+      
+      sparks.push(spark);
+    }
+
+    // Inject keyframes
+    const style = document.createElement('style');
+    style.textContent = allKeyframes;
+    document.head.appendChild(style);
+
+    // Inject sparks
+    sparks.forEach(spark => logoFigure.appendChild(spark));
+  }
+
+  if (document.readyState === 'loading') {
+    document.addEventListener('DOMContentLoaded', initSparks);
+  } else {
+    initSparks();
+  }
+})();
+

content/professional-projects/usm-blueiq-acoustic-target-localization/index.md

@@ -7,9 +7,6 @@ categories = ['USM', 'BlueIQ', "Acoustics"]
 tags = ['data science', 'KarstTech', 'sonar', 'TDOA']
 +++
 
-Using low-cost AI enabled acoustic buoys to detect and track vessels
-<!--more-->
-
 {{< katex >}}
 
 # Summary of "Enhancing Maritime Domain Awareness Through AI-Enabled Acoustic Buoys"

content/professional-projects/usm-magnetics-machine-learning/index.md

@@ -7,9 +7,6 @@ categories = ['USM', 'magnetics']
 tags = ['data science', 'KarstTech', 'UUV', 'modeling', 'machine learning', 'AI']
 +++
 
-Using machine learning to detect and localize magnetic targets underwater
-<!--more-->
-
 {{< katex >}}
 
 # Machine Learning for Target Classification and Localization

content/professional-projects/usm-magnetics-magnetic-modeling/index.md

@@ -7,9 +7,6 @@ categories = ['USM', 'magnetics']
 tags = ['data science', 'KarstTech', 'UUV', 'modeling', 'machine learning', 'COMSOL']
 +++
 
-Modeling magnetic targets to support machine learning applications in maritime environments
-<!--more-->
-
 # Potential Fields Modeling to Support Machine Learning Applications in Maritime Environments
 
 The summary below is from a published paper that I co-authored with my colleagues at USM Magnetics. If you would rather read the full paper, you can find it [here](https://www.comsol.com/paper/potential-fields-modeling-to-support-machine-learning-applications-in-maritime-environments-135922).

content/professional-projects/usm-magnetics-magnetic-parametric-inversion/index.md

@@ -7,9 +7,6 @@ categories = ['USM', 'magnetics']
 tags = ['data science', 'KarstTech', 'modeling']
 +++
 
-Estimating the position and magnetic moment of a dipole from a few field measurements
-<!--more-->
-
 {{< katex >}}
 
 # Magnetic Dipole Localization: Methods for Parametric Inversion

content/professional-projects/usm-magnetics-sensor-fusion/index.md

@@ -7,9 +7,6 @@ categories = ['USM', 'magnetics']
 tags = ['data science', 'KarstTech', 'UUV', 'sensor fusion']
 +++
 
-Combining data from multiple sensors to make them all more useful!
-<!--more-->
-
 ## Getting Started
 Several months ago I was referred to the University of Southern Mississippi [Marine Research Center](https://www.usm.edu/ocean-enterprise/marine-research-center.php) by a coworker. They wanted some assistance from someone with a physics and data science background to work on an autonomous underwater vehicle for the purpose of developing a magnetic sensing platform.
 

layouts/partials/extend-head.html

@@ -0,0 +1,13 @@
+{{ if .IsHome }}
+  {{ $jsLogoSparks := resources.Get "js/logo-sparks.js" }}
+  {{ if $jsLogoSparks }}
+    {{ $jsLogoSparks = $jsLogoSparks | resources.Minify | resources.Fingerprint ($.Site.Params.fingerprintAlgorithm | default "sha256") }}
+    <script
+      defer
+      type="text/javascript"
+      src="{{ $jsLogoSparks.RelPermalink }}"
+      integrity="{{ $jsLogoSparks.Data.Integrity }}"
+    ></script>
+  {{ end }}
+{{ end }}
+