OpenRTiST utilizes Gabriel, a platform for wearable cognitive assistance applications, to transform the live video from a mobile client into the styles of various artworks.

Bandwidth-efficient Live Video Analytics for Drones via Edge Computing

Real-time video analytics on small autonomous drones poses several  difficult challenges at the intersection of wireless bandwidth,  processing capacity, energy consumption, result accuracy, and timeliness  of results. In response to these challenges, we describe four  strategies to build an adaptive computer vision pipeline for search  tasks in domains such as

Edge-based Discovery of Training Data for Machine Learning

The generation of high-quality training data has become the key  bottleneck in the use of deep learning across many domains. We describe  Eureka, an interactive system that leverages edge computing and early  discard to greatly improve the productivity of experts in the  construction of a labeled data set. Our experimental

Disk Tray Assembly

Disk Tray AssemblyIn collaboration with the company inwinSTACK, we created a Gabriel application for training a new worker in disk tray assembly for a desktop.   This demo was shown live at the Computex 2018 show in Taiwan in June 2018.   The application was created by Junjue Wang of CMU, and

An Application Platform for Wearable Cognitive Assistance

Wearable cognitive assistance applications can provide guidance for many facets of a user’s daily life. This thesis targets the enabling of a new genre of such applications that require both heavy computation and very low response time on inputs from mobile devices.  The core contribution of this thesis is

15-821 Fall 2017

6 course projectsThe Fall 2017 offering of 15-821/18-843 "Mobile and Pervasive Computing" course included several student projects based on cloudlets and wearable cognitive assistance. This is a YouTube playlist with videos of the student projects captured on the final day of class.

Live Synthesis of Vehicle-Sourced Data Over 4G LTE

Accurate, up-to-date maps of transient traffic and hazards are  invaluable to drivers, city managers, and the emerging class of  self-driving vehicles. We present LiveMap, a scalable, automated system  for acquiring, curating, and disseminating detailed, continually-updated  road conditions in a region. LiveMap leverages in-vehicle cameras,  sensors, and processors to crowd-source hazard