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Software Projects

Key Projects

SteelEagle

The code behind:  M. Bala et al., "The OODA Loop of Cloudlet-Based Autonomous Drones," 2024 IEEE/ACM Symposium on Edge Computing (SEC), Rome, Italy, 2024, pp. 178-190, doi: 10.1109/SEC62691.2024.00022. Bala, M., Eiszler, T., Chen, X., Harkes, J., Blakley, J., Pillai, P., & Satyanarayanan, M. (2023, December). Democratizing Drone Autonomy via Edge Computing. In 2023 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 40-52). IEEE.

Quetzal (A SteelEagle Project)

Quetzal: An Interactive System for Drone Video Frame Alignment & Detection of Salient Changes Quetzal offers automated frame alignment for drone footage captured along similar routes. The Quetzal app features a file-system GUI designed for organizing and sharing various projects and videos, enabling users to compare two videos from the database. Additionally, it incorporates zero-shot object detection (GroundingSAM), allowing users to search for objects within video frames based on text prompts provided.

Hawk

The code behind: E. Sturzinger and M. Satyanarayanan, "Beyond Federated Learning: Survival-Critical Machine Learning," in 2024 IEEE/ACM Symposium on Edge Computing (SEC), Rome, Italy, 2024, pp. 483-489, doi: 10.1109/SEC62691.2024.00056. E. Sturzinger, J. Harkes, P. Pillai and M. Satyanarayanan, "Edge-based Live Learning for Robot Survival," in IEEE Transactions on Emerging Topics in Computing, doi: 10.1109/TETC.2024.3479082. Shilpa George, Haithem Turki, Ziqiang Feng, Deva Ramanan, Padmanabhan Pillai, and Mahadev Satyanarayanan. 2023. "Low-Bandwidth Self-Improving Transmission of Rare Training Data". Proceedings of the 29th Annual International Conference on Mobile Computing and Networking. Association for Computing Machinery, New York, NY, USA, Article 86, 1–15. https://doi.org/10.1145/3570361.3613300

Sinfonia

The code behind: Satyanarayanan, Mahadev, Jan Harkes, Jim Blakley, Marc Meunier, Govindarajan Mohandoss, Kiel Friedt, Arun Thulasi, Pranav Saxena, and Brian Barritt. "Sinfonia: Cross-Tier Orchestration for Edge-Native Applications." Frontiers in the Internet of Things (2022): 5

Network Latency Segmentation Project

The code behind: “Segmenting Latency in a Private 4G LTE Network”, Sophie Smith, Ishan Darwhekar, James Blakley, Thomas Eiszler, Jan Harkes, Â鶹¹ÙÍø Technical Report Â鶹¹ÙÍø-CS-22-115, May 2022.

OpenScout

OpenScout is an edge-native application designed for automated situational awareness. The idea behind OpenScout was to build a pipeline that would support automated object detection and facial recognition. This kind of situational awareness is crucial in domains such as disaster recovery and military operations where connection over the WAN to the cloud may be disrupted or temporarily disconnected.

OpenRTiST

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.

The Gabriel Platform

Gabriel is a framework for wearable cognitive assistance using cloudlets. We have released Gabriel version 2.0.

Wearable Cognitive Assistance Applications

A collection of the many wearable cognitive assistance applications created by the Living Edge Lab

Mega-NeRF

The code behind: Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs H Turki, D Ramanan, M Satyanarayanan - arXiv preprint arXiv:2112.10703, 2021 We explore how to leverage neural radiance fields (NeRFs) to build interactive 3D environments from large-scale visual captures spanning buildings or even multiple city blocks collected primarily from drone data.

OpenTPOD

Create deep learning based object detectors without writing a single line of code. OpenTPOD is an all-in-one open-source tool for nonexperts to create custom deep neural network object detectors. It is designed to lower the barrier of entry and facilitates the end-to-end authoring workflow of custom object detection using state-of-art deep learning methods.

OpenWorkFlow

A suite of tools for creating wearable cognitive assistants.

LiveMap

Code Described in: Christensen, K., Mertz, C., Pillai, P., Hebert, M., & Satyanarayanan, M. (2019, February). Towards a distraction-free waze. In Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications (pp. 15-20).

DroneSearch

This repo contains a python package dronesearch for running live video analytics on drone video feeds leveraging edge servers. It also contains our experiment code for SEC'18 paper Bandwidth-efficient Live Video Analytics for Drones via Edge Computing.

PyEdgeSim

PyEdgeSim is a mostly python-based simulation framework built around the AdvantEDGE Mobile Edge Emulation Platform. This framework was used to run the simulations covered in the Â鶹¹ÙÍø Computer Science Department Technical Report, Simulating Edge Computing Environments to Optimize Application Experience and the Open Edge Computing Initiative whitepaper How Close to the Edge?: Edge Computing and Carrier Interexchange.

Nephele

nephele is a CLI utility that provides capabilities to create, manage, and migrate KVM virtual machines over the wide-area network. Built upon the principles devised and implemented by Kiryong Ha, nephele utilizes deduplication, compression, and bandwidth adaptation to migrate VMs efficiently in WAN environments where the bandwidth and latency characteristics are far below those found in datacenters where traditional VM migration happens. More information, including related publications, can be found on our website.

OpenFace

Free and open source face recognition with deep neural networks.

FaceSwap

FaceSwap is an Android application that swaps people's faces in real time using face tracking, face detection, and face recognition. FaceSwap is a demo application to visualize differences cloudlet can make in reducing network latency for compute-intensive and latency-sensitive applications.

RTFace

RTFace is a framework that selectively blurs a person's face based on his identity in real-time to protect user's privacy. It leverages object tracking to achieve real-time while running face detection using dlib, and face recognition using OpenFace.