Varun Siddaraju

Publications

Documenting My Journey Through XR, AI, and Human-Centered Innovation

In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing

Authors: George Koutitas, Varun Kumar Siddaraju, Vangelis Metsis
Journal: Sensors (2020, Vol. 20, Issue 3, Article 690)
Year: 2020
Citations: 12+

  • Lead author of “In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing” (Sensors 2020) — pioneering the fusion of AR and wireless-ray-tracing for indoor network planning.

  • Developed a low-cost “image-to-facet” model that transforms smartphone-captured indoor images into vector maps for ray-tracing signal-propagation predictions.

  • Validated the method in a real apartment, achieving high accuracy, and enabling users to visualize predicted wireless-signal strength in real space via holograms.

Beginning Windows Mixed Reality Programming For HoloLens and Mixed Reality Headsets

Beginning Windows Mixed Reality Programming: For HoloLens and Mixed Reality Headsets (2nd Edition)

Authors: Sean Ong & Varun Kumar Siddaraju.
Publisher: Apress
Year: 2022
Citations: 26+

  • Build XR apps with Unity & MRTK, covering HoloLens 2 and spatial tracking.

  • Focus on spatial computing, monetization, and publishing.

  • 26 citations (2nd Ed.) and 10K+ accesses on SpringerLink.

  • Trusted by educators, developers, and XR innovators worldwide.

An Augmented Reality Facet Mapping Technique for Ray Tracing Applications

Authors: Varun Kumar Siddaraju
Thesis, Texas State University
Year: 2018
Advisor: Dr. George Koutitas

  • Pioneered a fusion of AR and wireless ray tracing for indoor signal visualization—enabling real-time holographic representation of wireless channels.

  • Introduced a facet-mapping technique that converts smartphone-captured indoor images into 3D spatial models for accurate signal-propagation prediction.

  • Validated the framework in real environments, achieving high accuracy in predicting wireless strength and offering a new paradigm for smart-building network planning.

X-Reality Research Lab: Augmented Reality Meets Internet of Things

Authors: Varun Kumar Siddaraju, George Koutitas, Vangelis Metsis
Conference: IEEE INFOCOM — International Conference on Computer Communications, 2018
Date: April 3, 2018
Citations: 25+

  • Presented a 4D interactive system integrating Augmented Reality (AR) and Internet of Things (IoT) for immersive network visualization.

  • Enabled users to interact with IoT sensor data through avatars and holograms—creating digital twins of real-world environments.

  • Demonstrated object recognition, hologram interaction, and network topology visualization for smart sensor network education and research.

An Augmented Reality Facet Mapping Technique for Ray Tracing Applications

Authors: Varun Kumar Siddaraju, Dr. George Koutitas
Conference: International Conference on Digital Telecommunications (ICDT 2018)
Date: March 12, 2018

  • Developed a low-cost AR-based spatial mapping algorithm that transforms smartphone-captured indoor images into accurate 3D facet models—eliminating the need for expensive RGB-D or SLAM cameras.

  • Integrated AR visualization with ray-tracing simulations to predict and visualize indoor 5G wireless channel behavior in real-time, improving human-to-network interaction.

  • Enabled rapid indoor network planning through an intuitive GUI that constructs a 3D indoor model in minutes, allowing non-experts to analyze and optimize signal coverage.

Retina-Based Biometric Identification System Using Artificial Neural Network

Authors: Vidya T, Varun Kumar S, Ajay Menon M, Guruprasad Hegde
Journal: International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE)
Volume: 3, Issue 10
Date: October 2015

 
  • Introduced an ANN-powered retinal recognition system capable of extracting distinct vascular patterns using fundus images and adaptive image enhancement techniques.

  • Implemented FAST feature detection algorithm, achieving higher speed and accuracy compared to SIFT and MSER methods.

  • Demonstrated high authentication accuracy through confusion matrix analysis and validation performance graphs, showing minimal error rates in retinal feature matching.