Adaptive online deployment for resource constrained mobile smart clients

Nowadays mobile devices are more and more used as a platform for applications. Contrary to prior generation handheld devices configured with a predefined set of applications, today leading edge devices provide a platform for flexible and customized application deployment. However, these applications have to deal with the limitations (e.g. CPU speed, memory) of these mobile devices and thus cannot handle complex tasks. In order to cope with the handheld limitations and the ever changing device context (e.g. network connections, remaining battery time, etc.) we present a middleware solution that dynamically offloads parts of the software to the most appropriate server. Without a priori knowledge of the application, the optimal deployment is calculated, that lowers the cpu usage at the mobile client, whilst keeping the used bandwidth minimal. The information needed to calculate this optimum is gathered on the fly from runtime information. Experimental results show that the proposed solution enables effective execution of complex applications in a constrained environment. Moreover, we demonstrate that the overhead from the middleware components is below 2%

Cursor control by point-of-regard estimation for a computer with integrated webcam

This work forms part of the project Eye-Communicate funded by the Malta Council for Science and Technology through the National Research & Innovation Programme (2012) under Research Grant No. R&I-2012-057.The problem of eye-gaze tracking by videooculography has been receiving extensive interest throughout the years owing to the wide range of applications associated with this technology. Nonetheless, the emergence of a new paradigm referred to as pervasive eye-gaze tracking, introduces new challenges that go beyond the typical conditions for which classical video-based eye- gaze tracking methods have been developed. In this paper, we propose to deal with the problem of point-of-regard estimation from low-quality images acquired by an integrated camera inside a notebook computer. The proposed method detects the iris region from low-resolution eye region images by its intensity values rather than the shape, ensuring that this region can also be detected at different angles of rotation and under partial occlusion by the eyelids. Following the calculation of the point- of-regard from the estimated iris center coordinates, a number of Kalman filters improve upon the noisy point-of-regard estimates to smoothen the trajectory of the mouse cursor on the monitor screen. Quantitative results obtained from a validation procedure reveal a low mean error that is within the footprint of the average on-screen icon.peer-reviewe

On-screen point-of-regard estimation under natural head movement for a computer with integrated webcam

Recent developments in the field of eye-gaze tracking by vidoeoculography indicate a growing interest towards unobtrusive tracking in real-life scenarios, a new paradigm referred to as pervasive eye-gaze tracking. Among the challenges associated with this paradigm, the capability of a tracking platform to integrate well into devices with in-built imaging hardware and to permit natural head movement during tracking is of importance in less constrained scenarios. The work presented in this paper builds on our earlier work, which addressed the problem of estimating on-screen point-of-regard from iris center movements captured by an integrated camera inside a notebook computer, by proposing a method to approximate the head movements in conjunction with the iris movements in order to alleviate the requirement for a stationary head pose. Following iris localization by an appearance-based method, linear mapping functions for the iris and head movement are computed during a brief calibration procedure permitting the image information to be mapped to a point-of-regard on the monitor screen. Following the calculation of the point-of-regard as a function of the iris and head movement, separate Kalman filters improve upon the noisy point-of-regard estimates to smoothen the trajectory of the mouse cursor on the monitor screen. Quantitative and qualitative results obtained from two validation procedures reveal an improvement in the estimation accuracy under natural head movement, over our previous results achieved from earlier work.peer-reviewe