3,099 research outputs found
Efficient Semantic Segmentation on Edge Devices
Semantic segmentation works on the computer vision algorithm for assigning
each pixel of an image into a class. The task of semantic segmentation should
be performed with both accuracy and efficiency. Most of the existing deep FCNs
yield to heavy computations and these networks are very power hungry,
unsuitable for real-time applications on portable devices. This project
analyzes current semantic segmentation models to explore the feasibility of
applying these models for emergency response during catastrophic events. We
compare the performance of real-time semantic segmentation models with
non-real-time counterparts constrained by aerial images under oppositional
settings. Furthermore, we train several models on the Flood-Net dataset,
containing UAV images captured after Hurricane Harvey, and benchmark their
execution on special classes such as flooded buildings vs. non-flooded
buildings or flooded roads vs. non-flooded roads. In this project, we developed
a real-time UNet based model and deployed that network on Jetson AGX Xavier
module
A micropower centroiding vision processor
Published versio
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
- …