26,109 research outputs found
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
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The role of HG in the analysis of temporal iteration and interaural correlation
Magnetic Cellular Nonlinear Network with Spin Wave Bus for Image Processing
We describe and analyze a cellular nonlinear network based on magnetic
nanostructures for image processing. The network consists of magneto-electric
cells integrated onto a common ferromagnetic film - spin wave bus. The
magneto-electric cell is an artificial two-phase multiferroic structure
comprising piezoelectric and ferromagnetic materials. A bit of information is
assigned to the cell's magnetic polarization, which can be controlled by the
applied voltage. The information exchange among the cells is via the spin waves
propagating in the spin wave bus. Each cell changes its state as a combined
effect of two: the magneto-electric coupling and the interaction with the spin
waves. The distinct feature of the network with spin wave bus is the ability to
control the inter-cell communication by an external global parameter - magnetic
field. The latter makes possible to realize different image processing
functions on the same template without rewiring or reconfiguration. We present
the results of numerical simulations illustrating image filtering, erosion,
dilation, horizontal and vertical line detection, inversion and edge detection
accomplished on one template by the proper choice of the strength and direction
of the external magnetic field. We also present numerical assets on the major
network parameters such as cell density, power dissipation and functional
throughput, and compare them with the parameters projected for other
nano-architectures such as CMOL-CrossNet, Quantum Dot Cellular Automata, and
Quantum Dot Image Processor. Potentially, the utilization of spin waves
phenomena at the nanometer scale may provide a route to low-power consuming and
functional logic circuits for special task data processing
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Large-Scale Optical Neural Networks based on Photoelectric Multiplication
Recent success in deep neural networks has generated strong interest in
hardware accelerators to improve speed and energy consumption. This paper
presents a new type of photonic accelerator based on coherent detection that is
scalable to large () networks and can be operated at high (GHz)
speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using
the massive spatial multiplexing enabled by standard free-space optical
components. In contrast to previous approaches, both weights and inputs are
optically encoded so that the network can be reprogrammed and trained on the
fly. Simulations of the network using models for digit- and
image-classification reveal a "standard quantum limit" for optical neural
networks, set by photodetector shot noise. This bound, which can be as low as
50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for
digital irreversible computation is theoretically possible in this device. The
proposed accelerator can implement both fully-connected and convolutional
networks. We also present a scheme for back-propagation and training that can
be performed in the same hardware. This architecture will enable a new class of
ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5,
figures, 2 table
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