19,663 research outputs found
Vision-based deep execution monitoring
Execution monitor of high-level robot actions can be effectively improved by
visual monitoring the state of the world in terms of preconditions and
postconditions that hold before and after the execution of an action.
Furthermore a policy for searching where to look at, either for verifying the
relations that specify the pre and postconditions or to refocus in case of a
failure, can tremendously improve the robot execution in an uncharted
environment. It is now possible to strongly rely on visual perception in order
to make the assumption that the environment is observable, by the amazing
results of deep learning. In this work we present visual execution monitoring
for a robot executing tasks in an uncharted Lab environment. The execution
monitor interacts with the environment via a visual stream that uses two DCNN
for recognizing the objects the robot has to deal with and manipulate, and a
non-parametric Bayes estimation to discover the relations out of the DCNN
features. To recover from lack of focus and failures due to missed objects we
resort to visual search policies via deep reinforcement learning
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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