Date of Award

6-9-2026

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2026

Departments

Electrical Engineering; Electrical and Computer Engineering; Computer Engineering; Computer Science and Engineering; General Engineering

First Advisor

Farokh Eskafi

Second Advisor

Jes Kuczenski

Third Advisor

Sally Wood

Abstract

Finding suitable places to study or work on a college campus can be challenging due to fluctuating noise levels and overcrowding in shared spaces. This project, titled StudyScape, aims to help students identify optimal study environments by providing real-time information on ambient noise and room occupancy in campus buildings. The system deploys edge-based sensing nodes in selected indoor locations, each equipped with a microphone and camera. Audio signals are processed locally to estimate noise levels, while computer vision techniques are used solely for accurate occupancy counting; no video or image data is stored or transmitted, ensuring user privacy. A mobile application presents this information to users as an interactive campus map, allowing students to quickly identify quiet or less crowded spaces. User profiles enable personalized recommendations based on individual noise and occupancy preferences. By combining edge computing, privacy-preserving computer vision, and mobile visualization, StudyScape demonstrates a scalable approach to improve the student study experience.

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