snBench

Azer Bestavros and Michael Ocean

The snBench project aims to design and implement the programming and run-time infrastructure necessary for developers to specify and deploy truly distributed applications over a heterogeneous network of Sensing Elements (SEs) and of Computing Elements (CEs). snBench offers the means via which applications may (1) locate, monitor, and query SEs and CEs for services they support, and (2) initiate, control, or otherwise use such SE and CE services. In addition to supporting these functionalities, the snBench provides basic run-time resource management services for QoS support, including real-time scheduling and admission control functionalities. While the architecture of the system and most of its services will be targeted at generic sensing modalities, our immediate target will be the Sensorium infrastructure. To that end, the basic set of SE services to be made available to distributed applications will include 'atomic' video operations (e.g., video frame capture, blob counting, motion detection, tracking, among others) whereas the basic set of CE services to be made available to distributed applications will include in-network stream aggregation services (e.g., concasting coordinates of detected features, real-time stitching of frames from multiple vantage points, among others). One of the salient features of snBench is extensibility. To that end, snBench provides a type-disciplined framework for defining and adding new services--both atomic services at SEs and compositional services at CEs.

netEmbed

Azer Bestavros and Jorge Londono

Emerging configurable infrastructures such as the Sensorium comprise diverse sets of available computing and networking resources whose characteristics are both complex and time-varying. At the same time, applications to be deployed on these infrastructures exhibit increasingly complex constraints and requirements on resources they wish to utilize. Examples include selecting nodes and links to schedule an overlay multicast file transfer across the Grid, embedding a network experiment with specific resource constraints in a distributed testbed such as PlanetLab, or dispatching sense-and-compute processes over a distributed sensor network such as the Sensorium.  Thus, a common problem facing the efficient deployment of distributed applications on these infrastructures is that of "mapping" application-level requirements onto the network in such a manner that the requirements of the application are realized, assuming that the underlying characteristics of the network are known. We refer to this problem as the network embedding problem. In this project, we pursue a set of approaches to tackle this combinatorially-hard problem.  These approaches are fleshed out in a service (called netEmbed) which we have shown to be quite effective in identifying one (or all) possible embeddings for quite sizable queries and hosting networks.

TRAFFIC

Azer Bestavros, Assaf Kfoury, Likai Liu and Ibrahim Matta

It is difficult to develop correct, efficient, and resilient networking applications, especially on infrastructures such as the Sensorium, which are meant to host many such applications, each with specific safety constraints spelled out as requirements on performance, security, etc.  The heterogeneity and open nature of such systems make the certification of component composition quite challenging.  Towards that end, in this project we aim to make the design and implementation of certifiably robust services on infrastructires such as the Sensorium accessible to the average programmer. We accomplish this by breaking down the specification of a an application into two parts: specification of flows and specification of types. Specification of flows describes the topology and composition of the networked system, and how individual components are connected to each other. We expect this part to be accessible by any average programmer. The specification of types formalizes properties of a network in terms of some concrete syntax that can be understood and analyzed automatically by our type system. This is more difficult to specify, but the efforts of a limited number of experts can be shared.

The CameraMouse

Margrit Betke, John Magee and Stephen Crampton

The Camera Mouse is a system that tracks the movements of computer users with a video camera and translates them into the movements of the mouse pointer on the screen. The approach is based on correlation of brightness patterns and recursive filtering. Another system is the Blink Link, designed for people who have extremely limited muscle control, but are capable of blinking voluntarily. Such users can generate selection commands through their eye blinks in order to operate switch-based scanning programs and on-screen keyboards. We have developed systems that use deformable templates to detect eyebrow raises and generate selection commands control the camera's pan, tilt, and zoom mechanisms to actively detect and track the user's head and eyes, and interact with the computer with hand gestures. Ongoing research focuses on methods to detect unique patterns in the voluntary movements that a particular child can make comfortably. The interface will then be developed to recognize these patterns and provide an efficient tool for communication.

Fast Face and Eye Tracking

John Magee and Margrit Betke

Over 100,000 severely paralized people in the United States have only the ability to control the muscles in their eyes. For these people, eye movements or blinks are the only way to communicate. Previous attempts to provide an interface system are intrusive or require special hardware. We are creating a system that can run on an average PC with video input from an inexpensive USB camera. The goal is to detect if the computer user is looking at the screen, or off to the left or right side. The output of our system could be used as an interface for the computer. The face is located and tracked in the video images using a variety of computer vision techniques: color histograms, motion analysis, and multi-resolution template matching with normalized correlation. Once the face is located, analysis of the eyes can begin.

SlurpCast

Likai Liu, Assaf Kfoury, and Hongwei Xi