17,735 research outputs found
Means for visually indicating flight paths of vehicles between the Earth, Venus, and Mercury Patent
Table structure and rotating magnet system simulating gravitational forces on spacecraft and displaying trajectories between Earth, Venus, and Mercur
Detecting periodicity in experimental data using linear modeling techniques
Fourier spectral estimates and, to a lesser extent, the autocorrelation
function are the primary tools to detect periodicities in experimental data in
the physical and biological sciences. We propose a new method which is more
reliable than traditional techniques, and is able to make clear identification
of periodic behavior when traditional techniques do not. This technique is
based on an information theoretic reduction of linear (autoregressive) models
so that only the essential features of an autoregressive model are retained.
These models we call reduced autoregressive models (RARM). The essential
features of reduced autoregressive models include any periodicity present in
the data. We provide theoretical and numerical evidence from both experimental
and artificial data, to demonstrate that this technique will reliably detect
periodicities if and only if they are present in the data. There are strong
information theoretic arguments to support the statement that RARM detects
periodicities if they are present. Surrogate data techniques are used to ensure
the converse. Furthermore, our calculations demonstrate that RARM is more
robust, more accurate, and more sensitive, than traditional spectral
techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified
styl
Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure
Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law
distribution of population event sizes with an exponent of -3/2. It has been
observed in the superficial layers of cortex both \emph{in vivo} and \emph{in
vitro}. In this paper we analyze the information transmission of a novel
self-organized neural network with active-neuron-dominant structure. Neuronal
avalanches can be observed in this network with appropriate input intensity. We
find that the process of network learning via spike-timing dependent plasticity
dramatically increases the complexity of network structure, which is finally
self-organized to be active-neuron-dominant connectivity. Both the entropy of
activity patterns and the complexity of their resulting post-synaptic inputs
are maximized when the network dynamics are propagated as neuronal avalanches.
This emergent topology is beneficial for information transmission with high
efficiency and also could be responsible for the large information capacity of
this network compared with alternative archetypal networks with different
neural connectivity.Comment: Non-final version submitted to Chao
Scramjet nozzle design and analysis as applied to a highly integrated hypersonic research airplane
Engine-nozzle airframe integration at hypersonic speeds was conducted by using a high-speed research aircraft concept as a focus. Recently developed techniques for analysis of scramjet-nozzle exhaust flows provide a realistic analysis of complex forces resulting from the engine-nozzle airframe coupling. By properly integrating the engine-nozzle propulsive system with the airframe, efficient, controlled and stable flight results over a wide speed range
Orbiter/launch system
The system includes reusable turbojet propelled booster vehicles releasably connected to a reusable rocket powered orbit vehicle. The coupled orbiter-booster combination takes off horizontally and ascends to staging altitude and speed under booster power with both orbiter and booster wings providing lift. After staging, the booster vehicles fly back to Earth for horizontal landing and the orbiter vehicle continues ascending to orbit
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