2026-09-21T17:16:29Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129507 2026-02-05 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01 The student, Chaitanya Amballa, accepted the attached license on 2025-04-21 at 11:51. The student, Chaitanya Amballa, submitted this Thesis for approval on 2025-04-21 at 12:16. This Thesis was approved for publication on 2025-04-21 at 16:16. DSpace SAF Submission Ingestion Package generated from Vireo submission #21689 on 2025-10-19 at 19:14:26 Inferring indoor floorplans from wireless signals Amballa, Chaitanya 2025-04-21 Roy Choudhury, Romit NeRF Indoor floorplan eng Neural Radiance Fields (NeRFs) have been remarkably successful at synthesizing novel views of 3D scenes by optimizing a volumetric scene function. This scene function models how an optical ray accumulates colors on its path and eventually delivers this color to the camera pixel it impinges upon. Radio frequency (RF) or audio signals can also be viewed as a vehicle for delivering information about the environment to a sensor. However, unlike camera pixels, an RF/audio sensor receives a mixture of signals that contains many environmental reflections. Is it still possible to infer the environment using such mixed signals? We show that with redesign, the core NeRF framework has the potential to solve this inverse problem. We focus on a specific application of inferring the indoor floorplan of a home from WiFi measurements made at multiple locations inside the home. Our inferred floorplans look promising, and benefit downstream signal prediction applications. Our work also uncovers a number of problems for continued research. 2025-05 Thesis https://hdl.handle.net/2142/129507 Copyright 2025 Chaitanya Amballa Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis