222,154 research outputs found
Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View
Small satellite systems enable whole new class of missions for navigation,
communications, remote sensing and scientific research for both civilian and
military purposes. As individual spacecraft are limited by the size, mass and
power constraints, mass-produced small satellites in large constellations or
clusters could be useful in many science missions such as gravity mapping,
tracking of forest fires, finding water resources, etc. Constellation of
satellites provide improved spatial and temporal resolution of the target.
Small satellite constellations contribute innovative applications by replacing
a single asset with several very capable spacecraft which opens the door to new
applications. With increasing levels of autonomy, there will be a need for
remote communication networks to enable communication between spacecraft. These
space based networks will need to configure and maintain dynamic routes, manage
intermediate nodes, and reconfigure themselves to achieve mission objectives.
Hence, inter-satellite communication is a key aspect when satellites fly in
formation. In this paper, we present the various researches being conducted in
the small satellite community for implementing inter-satellite communications
based on the Open System Interconnection (OSI) model. This paper also reviews
the various design parameters applicable to the first three layers of the OSI
model, i.e., physical, data link and network layer. Based on the survey, we
also present a comprehensive list of design parameters useful for achieving
inter-satellite communications for multiple small satellite missions. Specific
topics include proposed solutions for some of the challenges faced by small
satellite systems, enabling operations using a network of small satellites, and
some examples of small satellite missions involving formation flying aspects.Comment: 51 pages, 21 Figures, 11 Tables, accepted in IEEE Communications
Surveys and Tutorial
Robotic swarm control from spatio-temporal specifications
In this paper, we study the problem of controlling a two-dimensional robotic swarm with the purpose of achieving high level and complex spatio-temporal patterns. We use a rich spatio-temporal logic that is capable of describing a wide range of time varying and complex spatial configurations, and develop a method to encode such formal specifications as a set of mixed integer linear constraints, which are incorporated into a mixed integer linear programming problem. We plan trajectories for each individual robot such that the whole swarm satisfies the spatio-temporal requirements, while optimizing total robot movement and/or a metric that shows how strongly the swarm trajectory resembles given spatio-temporal behaviors. An illustrative case study is included.This work was partially supported by the National Science Foundation under grants NRI-1426907 and CMMI-1400167. (NRI-1426907 - National Science Foundation; CMMI-1400167 - National Science Foundation
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Adaptive Real Time Imaging Synthesis Telescopes
The digital revolution is transforming astronomy from a data-starved to a
data-submerged science. Instruments such as the Atacama Large Millimeter Array
(ALMA), the Large Synoptic Survey Telescope (LSST), and the Square Kilometer
Array (SKA) will measure their accumulated data in petabytes. The capacity to
produce enormous volumes of data must be matched with the computing power to
process that data and produce meaningful results. In addition to handling huge
data rates, we need adaptive calibration and beamforming to handle atmospheric
fluctuations and radio frequency interference, and to provide a user
environment which makes the full power of large telescope arrays accessible to
both expert and non-expert users. Delayed calibration and analysis limit the
science which can be done. To make the best use of both telescope and human
resources we must reduce the burden of data reduction.
Our instrumentation comprises of a flexible correlator, beam former and
imager with digital signal processing closely coupled with a computing cluster.
This instrumentation will be highly accessible to scientists, engineers, and
students for research and development of real-time processing algorithms, and
will tap into the pool of talented and innovative students and visiting
scientists from engineering, computing, and astronomy backgrounds.
Adaptive real-time imaging will transform radio astronomy by providing
real-time feedback to observers. Calibration of the data is made in close to
real time using a model of the sky brightness distribution. The derived
calibration parameters are fed back into the imagers and beam formers. The
regions imaged are used to update and improve the a-priori model, which becomes
the final calibrated image by the time the observations are complete
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