Traditional lidar simulations render surface models to generate simulated range data. For objects with welldefined surfaces, this approach works well, and traditional 3D scene reconstruction algorithms can be employed to automatically generate the surface models. This approach breaks down, though, for many trees, tall grasses, and other objects with fine-scale geometry: surface models do not easily represent the geometry, and automated reconstruction from real data is difficult. In this paper, we introduce a new stochastic volumetric model that better captures the complexities of real lidar data of vegetation and is far better suited for automatic modeling of scenes from field collected lidar data. We also introduce several methods for automatic modeling and for simulating lidar data utilizing the new model. To measure the performance of the stochastic simulation we use histogram comparison metrics to quantify the differences between data produced by the real and simulated lidar. We evaluate our approach on a range of real world datasets and show improved fidelity for simulating geo-specific outdoor, vegetation scenes.

While the needs and applications for computing technology have been growing, the enrollment and interest in Computer Science (CS) at the university level has not been growing in proportion. Moreover, the increasing prevalence of globalization requires a new set of skills for future technology leaders. These skills include the ability to work well in multidisciplinary and globally distributed teams, cross cultural boundaries with ease, create innovative solutions for problems that arise in unfamiliar settings, and think outside the box to solve a variety of problems while building effectively upon related work in the literature. These changes are also reflected in the growing interest among youth in global issues and how we can make a difference in underserved communities. This paper reports on our experience in designing and deploying an innovative internship that addresses these issues and seeks to enhance participation and education in CS through guided research projects in underserved communities.

Although para-social workers (PSW) carry the primary responsibility in providing essential services to the growing population of orphans and vulnerable children in Tanzania, they are often not paid for this work. Moreover, PSWs are unable to access governmental resources due to the lack of an efficient means of reporting their needs to relevant government officials in a timely manner. In this paper we describe a text message (SMS) based solution that harnesses the prevalence of mobile phones coupled with several Open Source tools to empower PSWs. Specifically, we build a more efficient mechanism for reporting summary data on orphans and vulnerable children to relevant government officials in a cost-effective and efficient manner. This paper reports on our needs assessment process, reviews the related work, describes the implementation and testing of our prototype solution, and concludes with a discussion of relevant future work.

This work advances the state-of-the-art in assistive technology for the visually impaired by enhancing an automated tutor designed to teach beginner-level braille writing skills. The hardware component of the tutor is a low-cost device that provides an intelligent tactile interface that connects to a laptop or desktop computer. We demonstrate the many uses of this tutor including its multilingual capabilities, interactive exercises, and educational games. Preliminary field tests of the tutor in India, Zambia, and Qatar have demonstrated the usefulness of this automated tutor, and its potential to enhance braille literacy in developing regions.