Publications page

Author: agarnung

README.md

@@ -116,6 +116,7 @@ Changes made to fulfil my needs:
 * Edited main page layout
 * Changed footer, header and other includes
 * Added [Latex support](https://talk.jekyllrb.com/t/how-to-use-latex-on-jekyll/4119/2) in _layouts/post.html
+* Added customized "Publications" page
 
 <div align="center">
 <sub><sup>© 2025 Alejandro Garnung Menéndez, licensed under the <a href="./LICENSE">MIT License</a>.</sup></sub>

TODO.txt

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+- Usar un DNS propio (hay que registrarlo) (ver https://github.com/alesordo):
+	1. crear archivo CNAME en raíz con contenido: www.agarnung.com 
+	2. en _cofig.yml poner:   url               : https://agarnung.com
+	3. ya estaría.
+- Ver si se puede permitir posts al futuro (y documentar las reglas de nombrar posts, con fecha primero y formato...)
+	

_data/other.yml

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+- title: "Introductory, Descriptive, and Practical Essay on Neural Networks"
+  date: 05 jun. 2023
+  url: '/assets/publications_pdfs/other/Ensayo_CNNs.pdf'
+  language: Spanish
+  image: '/assets/publications_images/other/neuronas.png'
+  description: |
+    This document encompasses several words addressing matters related to the art of creating the quality of discernment, namely, Artificial Intelligence. More specifically, it focuses on one of the most significant foreign contributions to this field: artificial neural networks (ANNs), which are currently highly popular due to their most important functional characteristic—the ability to learn automatically. This capability does not aim to formally create knowledge but rather emulate it through training or learning. Juxtaposed with the results attached to this document, which provide various experiments using MATLAB tools—particularly studying convolutional (artificial) neural networks—it is intended for the reader to accurately grasp and absorb numerous concepts related to these topics and many others that constitute, as a broad ensemble, artificial intelligent systems. All of this is undertaken while keeping in mind the question that, at the time, brilliantly ignited the beginning of this field of study: Can machines think beyond what we can instruct them? A question “whose ramifications continue to be explored to this day.”
+    Based on final master's course assignment report.

_data/papers.yml

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+- title: "Physics Meets Pixels: PDE Models inImage Processing"
+  date: 11 dic. 2024
+  url: https://arxiv.org/abs/2412.11946
+  publisher: arxiv (Cornell Tech)
+  image: '/assets/publications_images/preprints/pixels.png'
+  description: |
+    Partial Differential Equations (PDEs) have long been recognized as powerful tools for image processing and analysis, providing a framework to model and exploit structural and geometric properties inherent in visual data. Over the years, numerous PDE-based models have been developed and refined, inspired by natural analogies between physical phenomena and image spaces. These methods have proven highly effective in a wide range of applications, including denoising, deblurring, sharpening, inpainting, feature extraction, and others. This work provides a theoretical and computational exploration of both fundamental and innovative PDE models applied to image processing, accompanied by extensive numerical experimentation and objective and subjective analysis. Building upon well-established techniques, we introduce novel physical-based PDE models specifically designed for various image processing tasks. These models incorporate mathematical principles and approaches that, to the best of our knowledge, have not been previously applied in this domain, showcasing their potential to address challenges beyond the capabilities of traditional and existing PDE methods. By formulating and solving these mathematical models, we demonstrate their effectiveness in advancing image processing tasks while retaining a rigorous connection to their theoretical underpinnings. This work seeks to bridge foundational concepts and cutting-edge innovations, contributing to the evolution of PDE methodologies in digital image processing and related interdisciplinary fields.
+
+- title: "A Hybrid Frameworkfor Statistical Feature Selection and Image-Based Noise-Defect Detection"
+  date: 11 dic. 2024
+  url: https://www.arxiv.org/abs/2412.08800
+  publisher: arxiv (Cornell Tech)
+  image: '/assets/publications_images/preprints/hybrid.png'
+  description: |
+    In industrial imaging, accurately detecting and distinguishing surface defects from noise is critical and challenging, particularly in complex environments with noisy data. This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy while minimizing false positives. The motivation of the system is based on the generation of scalar scores that represent the likelihood that a region of interest (ROI) is classified as a defect or noise. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods such as Fisher separation, chi-squared test, and variance analysis. These techniques identify the most discriminative features, focusing on maximizing the separation between true defects and noise. Fisher's criterion ensures robust, real-time performance for automated systems. This statistical framework opens up multiple avenues for application, functioning as a standalone assessment module or as an a posteriori enhancement to machine learning classifiers. The framework can be implemented as a black-box module that applies to existing classifiers, providing an adaptable layer of quality control and optimizing predictions by leveraging intuitive feature extraction strategies, emphasizing the rationale behind feature significance and the statistical rigor of feature selection. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications, especially in complex, noisy environments.

_includes/about/other.html

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+<div class="col mt-4">
+  <div class="timeline-body bg-themed">
+    {% for item in site.data.other %}
+      <div class="timeline-item">
+        <div class="content">
+          <h2>{{ item.title }}</h2>
+          <h6 class="date">{{ item.date }}</h6>
+          <div style="display: flex; align-items: center; gap: 10px;">
+          	<h6 class="url"><a target="_blank" href="{{ item.url }}">Link to PDF</a></h6>
+          	<h6 class="language">- {{ item.language }}</h6>
+          </div>
+          <strong>Description:</strong>
+          <p style="text-align:justify; font-size:13px"><i>{{ item.description }}</i></p>
+          <img src="{{ item.image }}" alt="" class="center">
+        </div>
+      </div>
+    {% endfor %}
+  </div>
+</div>

_includes/about/papers.html

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+<div class="col mt-4">
+  <div class="timeline-body bg-themed">
+    {% for item in site.data.papers %}
+      <div class="timeline-item">
+        <div class="content">
+          <h2>{{ item.title }}</h2>
+          <h6 class="date">{{ item.date }}</h6>
+          <div style="display: flex; align-items: center; gap: 10px;">
+          	<h6 class="url"><a target="_blank" href="{{ item.url }}">Link to preprint</a></h6>
+          	<h6 class="publisher">- {{ item.publisher }}</h6>
+          </div>
+          <strong>Abstract:</strong>
+          <p style="text-align:justify; font-size:13px"><i>{{ item.description }}</i></p>
+          <img src="{{ item.image }}" alt="" class="center">
+        </div>
+      </div>
+    {% endfor %}
+  </div>
+</div>