Lubina, B., & Sundaram, R. (2025, March), Advancing WiFi-based Imaging: An Approach for Real-Time Object Detection and Classification  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia. https://peer.asee.org/54648

\bibitem{asee_peer_54648}
  Lubina, B., \& Sundaram, R. (2025, March), \emph{Advancing WiFi-based Imaging: An Approach for Real-Time Object Detection and Classification}
  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia.
  https://peer.asee.org/54648

Benjamin Lubina and Ramakrishnan Sundaram.  "Advancing WiFi-based Imaging: An Approach for Real-Time Object Detection and Classification".  2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/54648 Internet.  24 Apr, 2025

\bibitem{asee_peer_54648}
  Benjamin Lubina and Ramakrishnan Sundaram.
  "Advancing WiFi-based Imaging: An Approach for Real-Time Object Detection and Classification".
  \emph{2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/54648 Internet.  24 Apr, 2025

@INPROCEEDINGS{asee_peer_54648
author = "Benjamin Lubina and Ramakrishnan Sundaram"
title = "Advancing WiFi-based Imaging: An Approach for Real-Time Object Detection and Classification"
booktitle = "2025 ASEE North Central Section  (NCS) Annual Conference"
year = "2025"
month = "March"
address = "Marshall University, Huntington, West Virginia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/54648}
}

TY  - CPAPER
AB  - This research paper explores the development and implementation of a real-time, live feed system connected to an ESP32-based sensor array. Building on prior success in visualizing objects through WiFi signal variations, this study aims to enhance detection capabilities by integrating an object classification algorithm. The live feed system continuously processes WiFi signal fluctuations captured by the sensor array, allowing for immediate updates and an ongoing visualization of the spatial environment. The classification component employs machine learning techniques to distinguish between various object types, using signal patterns to infer object characteristics and potential compositions. This approach not only expands the array's capabilities beyond simple detection but also aims to identify objects with increasing accuracy and detail. Potential applications include security monitoring, navigation aids, and interactive educational tools. This work highlights the challenges of real-time data processing, algorithmic optimization, and signal noise mitigation. Future research directions focus on refining classification accuracy, improving computational efficiency, and expanding the sensor network's adaptability to diverse environments and object types.
AU  - Benjamin Lubina
AU  - Ramakrishnan Sundaram
CY  - Marshall University, Huntington, West Virginia
DA  - 2025/03/28
PB  - ASEE Conferences
TI  - Advancing WiFi-based Imaging: An Approach for Real-Time Object Detection and Classification
UR  - https://peer.asee.org/54648
ER  -