Rural development and communication: a community media project in Uttar Pradesh (India)

Loaded with 16% of the world’s population, India is a challenged country. More than a third of its citizens live below the poverty line - on less than a dollar a day. These people have no proper electricity, no proper drinking water supply, no proper sanitary facilities and well over 40% are illiterates. More than 65% live in rural areas and 60% earn their livelihood from agriculture. Only a meagre 3.63% have access to telephone and less than 1% have access to a computer. Therefore, providing access to timely information on agriculture, weather, social, health care, employment, fishing, is of utmost importance to improve the conditions of rural poor. After some introductive chapters, whose function is to provide a comprehensive framework – both theoretical and practical – of the current rural development policies and of the media situation in India and Uttar Pradesh, my dissertation presents the findings of the pilot project entitled “Enhancing development support to rural masses through community media activity”, launched in 2005 by the Department of Mass Communication and Journalism of the Faculty of Arts of the University of Lucknow (U.P.) and by the local NGO Bharosa. The project scope was to involve rural people and farmers from two villages of the district of Lucknow (namely Kumhrava and Barhi Gaghi) in a three-year participatory community media project, based on the creation, implementation and use of a rural community newspaper and a rural community internet centre. Community media projects like this one have been rarely carried out in India because the country has no proper community media tradition: therefore the development of the project has been a challenge for the all stakeholders involved.

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.