13,492 research outputs found
Massively Parallel Ray Tracing Algorithm Using GPU
Ray tracing is a technique for generating an image by tracing the path of
light through pixels in an image plane and simulating the effects of
high-quality global illumination at a heavy computational cost. Because of the
high computation complexity, it can't reach the requirement of real-time
rendering. The emergence of many-core architectures, makes it possible to
reduce significantly the running time of ray tracing algorithm by employing the
powerful ability of floating point computation. In this paper, a new GPU
implementation and optimization of the ray tracing to accelerate the rendering
process is presented
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
One of the main open challenges in visual odometry (VO) is the robustness to
difficult illumination conditions or high dynamic range (HDR) environments. The
main difficulties in these situations come from both the limitations of the
sensors and the inability to perform a successful tracking of interest points
because of the bold assumptions in VO, such as brightness constancy. We address
this problem from a deep learning perspective, for which we first fine-tune a
Deep Neural Network (DNN) with the purpose of obtaining enhanced
representations of the sequences for VO. Then, we demonstrate how the insertion
of Long Short Term Memory (LSTM) allows us to obtain temporally consistent
sequences, as the estimation depends on previous states. However, the use of
very deep networks does not allow the insertion into a real-time VO framework;
therefore, we also propose a Convolutional Neural Network (CNN) of reduced size
capable of performing faster. Finally, we validate the enhanced representations
by evaluating the sequences produced by the two architectures in several
state-of-art VO algorithms, such as ORB-SLAM and DSO
Will 5G See its Blind Side? Evolving 5G for Universal Internet Access
Internet has shown itself to be a catalyst for economic growth and social
equity but its potency is thwarted by the fact that the Internet is off limits
for the vast majority of human beings. Mobile phones---the fastest growing
technology in the world that now reaches around 80\% of humanity---can enable
universal Internet access if it can resolve coverage problems that have
historically plagued previous cellular architectures (2G, 3G, and 4G). These
conventional architectures have not been able to sustain universal service
provisioning since these architectures depend on having enough users per cell
for their economic viability and thus are not well suited to rural areas (which
are by definition sparsely populated). The new generation of mobile cellular
technology (5G), currently in a formative phase and expected to be finalized
around 2020, is aimed at orders of magnitude performance enhancement. 5G offers
a clean slate to network designers and can be molded into an architecture also
amenable to universal Internet provisioning. Keeping in mind the great social
benefits of democratizing Internet and connectivity, we believe that the time
is ripe for emphasizing universal Internet provisioning as an important goal on
the 5G research agenda. In this paper, we investigate the opportunities and
challenges in utilizing 5G for global access to the Internet for all (GAIA). We
have also identified the major technical issues involved in a 5G-based GAIA
solution and have set up a future research agenda by defining open research
problems
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Lensfree computational microscopy tools for cell and tissue imaging at the point-of-care and in low-resource settings.
The recent revolution in digital technologies and information processing methods present important opportunities to transform the way optical imaging is performed, particularly toward improving the throughput of microscopes while at the same time reducing their relative cost and complexity. Lensfree computational microscopy is rapidly emerging toward this end, and by discarding lenses and other bulky optical components of conventional imaging systems, and relying on digital computation instead, it can achieve both reflection and transmission mode microscopy over a large field-of-view within compact, cost-effective and mechanically robust architectures. Such high throughput and miniaturized imaging devices can provide a complementary toolset for telemedicine applications and point-of-care diagnostics by facilitating complex and critical tasks such as cytometry and microscopic analysis of e.g., blood smears, Pap tests and tissue samples. In this article, the basics of these lensfree microscopy modalities will be reviewed, and their clinically relevant applications will be discussed
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