EyeRIS: A General-Purpose System for Eye Movement Contingent Display Control

In experimental studies of visual performance, the need often emerges to modify the stimulus according to the eye movements perfonncd by the subject. The methodology of Eye Movement-Contingent Display (EMCD) enables accurate control of the position and motion of the stimulus on the retina. EMCD procedures have been used successfully in many areas of vision science, including studies of visual attention, eye movements, and physiological characterization of neuronal response properties. Unfortunately, the difficulty of real-time programming and the unavailability of flexible and economical systems that can be easily adapted to the diversity of experimental needs and laboratory setups have prevented the widespread use of EMCD control. This paper describes EyeRIS, a general-purpose system for performing EMCD experiments on a Windows computer. Based on a digital signal processor with analog and digital interfaces, this integrated hardware and software system is responsible for sampling and processing oculomotor signals and subject responses and modifying the stimulus displayed on a CRT according to the gaze-contingent procedure specified by the experimenter. EyeRIS is designed to update the stimulus within a delay of 10 ms. To thoroughly evaluate EyeRIS' perforltlancc, this study (a) examines the response of the system in a number of EMCD procedures and computational benchmarking tests, (b) compares the accuracy of implementation of one particular EMCD procedure, retinal stabilization, to that produced by a standard tool used for this task, and (c) examines EyeRIS' performance in one of the many EMCD procedures that cannot be executed by means of any other currently available device.National Institute of Health (EY15732-01

Identifying the task variables that predict object assembly difficulty.

We investigated the physical attributes of an object that influence the difficulty of its assembly. Identifying attributes that contribute to assembly difficulty will provide a method for predicting assembly complexity

Grid computing for the numerical reconstruction of digital holograms

Digital holography has the potential to greatly extend holography's applications and move it from the lab into the field: a single CCD or other solid-state sensor can capture any number of holograms while numerical reconstruction within a computer eliminates the need for chemical processing and readily allows further processing and visualisation of the holographic image. The steady increase in sensor pixel count and resolution leads to the possibilities of larger sample volumes and of higher spatial resolution sampling, enabling the practical use of digital off-axis holography. However this increase in pixel count also drives a corresponding expansion of the computational effort needed to numerically reconstruct such holograms to an extent where the reconstruction process for a single depth slice takes significantly longer than the capture process for each single hologram. Grid computing - a recent innovation in largescale distributed processing -provides a convenient means of harnessing significant computing resources in an ad-hoc fashion that might match the field deployment of a holographic instrument. In this paper we consider the computational needs of digital holography and discuss the deployment of numericals reconstruction software over an existing Grid testbed. The analysis of marine organisms is used as an exemplar for work flow and job execution of in-line digital holography

Using an Open Software System (Sakai) to Develop Student Portfolios

E-portfolios are digital collections of artifacts that represent the achievements and reflections of individuals. They offer a unique view into student learning and allow educators and external accreditors to assess student progress towards established standards as well as reviewing their program’s performance in supporting that progress. Students benefit from assembling their e-portfolios through the process of reviewing their own work with a critical eye, choosing pieces of their work that best represent their abilities, and reflecting on the transformative nature of their University experience, both in class and through extra-curricular, service learning, internships and international activities. An e-portfolio provides a holistic view of a student’s personal growth and abilities that will serve them well in their career search or graduate school application. The challenge for an institution is to provide this learning and assessment resource in an accessible and affordable vehicle that is manageable for both faculty and students. Roger Williams University has crafted a strategy to utilize the Sakai open source course management system with its integrated e-portfolio tool set and a linked website to provide both e-portfolios and program assessment. This strategy will also be employed to propose a virtual accreditation of a professional program that will serve as a model throughout the University and the broader higher education community

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols