23,493 research outputs found
Control limitations from distributed sensing: theory and Extremely Large Telescope application
We investigate performance bounds for feedback control of distributed plants
where the controller can be centralized (i.e. it has access to measurements
from the whole plant), but sensors only measure differences between neighboring
subsystem outputs. Such "distributed sensing" can be a technological necessity
in applications where system size exceeds accuracy requirements by many orders
of magnitude. We formulate how distributed sensing generally limits feedback
performance robust to measurement noise and to model uncertainty, without
assuming any controller restrictions (among others, no "distributed control"
restriction). A major practical consequence is the necessity to cut down
integral action on some modes. We particularize the results to spatially
invariant systems and finally illustrate implications of our developments for
stabilizing the segmented primary mirror of the European Extremely Large
Telescope.Comment: submitted to Automatic
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
Feedback-Aware Precoding for Millimeter Wave Massive MIMO Systems
Millimeter wave (mmWave) communication is a promising solution for coping
with the ever-increasing mobile data traffic because of its large bandwidth. To
enable a sufficient link margin, a large antenna array employing directional
beamforming, which is enabled by the availability of channel state information
at the transmitter (CSIT), is required. However, CSIT acquisition for mmWave
channels introduces a huge feedback overhead due to the typically large number
of transmit and receive antennas. Leveraging properties of mmWave channels,
this paper proposes a precoding strategy which enables a flexible adjustment of
the feedback overhead. In particular, the optimal unconstrained precoder is
approximated by selecting a variable number of elements from a basis that is
constructed as a function of the transmitter array response, where the number
of selected basis elements can be chosen according to the feedback constraint.
Simulation results show that the proposed precoding scheme can provide a
near-optimal solution if a higher feedback overhead can be afforded. For a low
overhead, it can still provide a good approximation of the optimal precoder.Comment: 7 pages, 5 figures, to appear at the IEEE International Symposium on
Personal, Indoor and Mobile Radio Communications (PIMRC) 201
Neuromorphic analogue VLSI
Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do
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