74 citations · 113 across the 11 of their papers we have counts for
14 papers
ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi Fine Time Measurements
Bryan Bo Cao, Abrar Alali, Hansi Liu +5
Tracking subjects in videos is one of the most widely used functions in camera-based IoT applications such as security surveillance, smart city traffic safety enhancement, vehicle…
ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences
Hansi Liu, Kristin Dana, Marco Gruteser +1
In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to…
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning
Nicholas Meegan, Hansi Liu, Bryan Bo Cao +5
We introduce ViFiCon, a self-supervised contrastive scheme which learns a cross-modal association between vision and wireless modalities. Specifically, the system uses pedestrian d…
Algorithms for bounding contribution for histogram estimation under user-level privacy
Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu +2
We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. We consider the het…
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation
Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem +4
We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data bec…
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
Madhumitha Harishankar, Jun Han, Sai Vineeth Kalluru Srinivas +7
The resolution of GPS measurements, especially in urban areas, is insufficient for identifying a vehicle's lane. In this work, we develop a deep LSTM neural network model LaNet tha…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Keith Bonawitz, Fariborz Salehi, Jakub Konečný +2
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do mac…
Sub-6GHz Assisted MAC for Millimeter Wave Vehicular Communications
Baldomero Coll-Perales, Javier Gozalvez, Marco Gruteser
Sub-6GHz vehicular communications (using DSRC, ITS-G5 or C-V2X) have been developed to support active safety applications. Future connected and automated driving applications can r…
Evaluation of IEEE 802.11ad for mmWave V2V Communications
Baldomero Coll-Perales, Marco Gruteser, Javier Gozalvez
Autonomous vehicles can construct a more accurate perception of their surrounding environment by exchanging rich sensor data with nearby vehicles. Such exchange can require larger…
Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications
Shubham Jain, Marco Gruteser
As smartphone rooted distractions become commonplace, the lack of compelling safety measures has led to a rise in the number of injuries to distracted walkers. Various solutions ad…
Reading Between the Pixels: Photographic Steganography for Camera Display Messaging
Eric Wengrowski, Kristin Dana, Marco Gruteser +1
We exploit human color metamers to send light-modulated messages less visible to the human eye, but recoverable by cameras. These messages are a key component to camera-display mes…
Optimal Radiometric Calibration for Camera-Display Communication
Wenjia Yuan, Eric Wengrowski, Kristin J. Dana +3
We present a novel method for communicating between a camera and display by embedding and recovering hidden and dynamic information within a displayed image. A handheld camera poin…
V2V Propagation Modeling with Imperfect RSSI Samples
Silvija Kokalj-Filipovic, Larry Greenstein, Bin Cheng +1
We describe three in-field data collection efforts yielding a large database of RSSI values vs. time or distance from vehicles communicating with each other via DSRC. We show sever…