Mobile and Pervasive
Project Descriptions and Mapping to Students
4. Just-in-Time Sign Translation
(Mentor: Junjue Wang)
in a foreign language environment often have troubles navigating
through surroundings. Sometimes failure to navigate can result in
severe consequences, for example, going through wrong declaration lanes
at Customs in airports. More often, it causes inconvenience and
frustration, for instance, missing a highway exit. This project aims to
perform just-in-time sign translation in places including airports,
highway, and hotels, to help travelers navigate in a foreign language
Leveraging a head-mounted smart glasses (e.g. Google Glass), the system
translates phrases on signs as a user looks at them and displays the
translation on the screen. The function is similar to Google and
Translate, but the project should reduce end-to-end latency
significantly leveraging cloudlets, since a user is likely to be
walking or even driving as she passes through a sign. To build such a
system, OCR is needed to convert images of text into text. Machine
translation is also required to convert the text into another language.
Fortunately, there are well established open-source projects for both
OCR (tesseract-ocr) and machine translation (OpenNMT). The source and
target language can be fixed to make the machine translation easier.
The focus of this project is on overall system design and
implementation, not on improving OCR nor machine translation.
8(a). Opportunistic Search of Edge-sourced Data
(Mentor: Jan Harkes)
receive multimedia data feeds from IoT sensors, video cameras, drones
and other data sources that they are associated with.
Searching this data at Internet scale with relatively low result
latency will become important in the future. The goal of
this project is to create a search framework for edge-sourced data that
is based on the publish-subscribe paradigm. Cloudlets can
subscribe or poll for any appropriately scoped searches, run the search
and send results back to the cloud.
8(b). Improving Search Efficiency in Edge Computing
(Mentor: Jan Harkes)
edge computing nodes (e.g., on a small, autonomous drone) may be
under-powered, and incapable of running expensive algorithms such as
those based on convolutional neural networks. The cloud has
the necessary resources, but shipping all the data from the edge to the
cloud is wasteful of bandwidth. However, the edge node
could run a cheap "early-discard" algorithm that has a higher false
positive rate than the best algorithm. The goal of this project
is to explore this tradeoff space and to demonstrate a search
capability based on these principles.
9. ActiveMap Location-based Services
(Mentor: Jason Hong)
The goal of ActiveMap is to make it easy for people to quickly create and deploy many kinds of location-based services. As an analogy, just as a web browser can read and render web pages, we want the ActiveMap to be able to read and render Maps. Example Maps might include a map of a location (e.g. a building floorplan), or content about where static or dynamic objects are (e.g. nearby Starbucks and discounts, real-time location of a person or members of a family, or location of buses as they move about). Systems issues include specifying the Map functionality and syntax (e.g. defining floorplans, defining locations inside, update rates, diffs from last read version, text snippets to
show), creating a GUI to display and interact with multiple Maps, and creating backend software for making these Maps easy to deploy on web servers.
Last updated 2017-09-18 by Satya