Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Matt Millar

dblp:151/6789 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Privacy and data protection · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection › privacy in ubiquitous computing
context-aware privacy
0.212014
ipShield: A Framework For Enforcing Context-Aware Privacy · NSDI 2014
Privacy and data protection › privacy policy
privacy policy enforcement
0.212014
ipShield: A Framework For Enforcing Context-Aware Privacy · NSDI 2014
YearPublicationVenuePosition
2017 A glove-based system for studying hand-object manipulation via joint pose and force sensing
abstract
We present a design of an easy-to-replicate glove-based system that can reliably perform simultaneous hand pose and force sensing in real time, for the purpose of collecting human hand data during fine manipulative actions. The design consists of a sensory glove that is capable of jointly collecting data of finger poses, hand poses, as well as forces on palm and each phalanx. Specifically, the sensory glove employs a network of 15 IMUs to measure the rotations between individual phalanxes. Hand pose is then reconstructed using forward kinematics. Contact forces on the palm and each phalanx are measured by 6 customized force sensors made from Velostat, a piezoresistive material whose force-voltage relation is investigated. We further develop an open-source software pipeline consisting of drivers and processing code and a system for visualizing hand actions that is compatible with the popular Raspberry Pi architecture. In our experiment, we conduct a series of evaluations that quantitatively characterize both individual sensors and the overall system, proving the effectiveness of the proposed design.
Hangxin Liu, Xu Xie 0001, Matt Millar, Mark Edmonds, Feng Gao 0013, Yixin Zhu 0001, Veronica J. Santos, Brandon Rothrock, Song-Chun Zhu
IROS3
2014 ipShield: A Framework For Enforcing Context-Aware Privacy
Supriyo Chakraborty, Chenguang Shen, Kasturi Rangan Raghavan, Yasser Shoukry, Matt Millar, Mani Srivastava 0001
NSDI5