239,151 research outputs found
Asymptotic Behavior of Error Exponents in the Wideband Regime
In this paper, we complement Verd\'{u}'s work on spectral efficiency in the
wideband regime by investigating the fundamental tradeoff between rate and
bandwidth when a constraint is imposed on the error exponent. Specifically, we
consider both AWGN and Rayleigh-fading channels. For the AWGN channel model,
the optimal values of and are calculated, where
is the maximum rate at which information can be transmitted over a
channel with bandwidth when the error-exponent is constrained to be
greater than or equal to Based on this calculation, we say that a sequence
of input distributions is near optimal if both and are
achieved. We show that QPSK, a widely-used signaling scheme, is near-optimal
within a large class of input distributions for the AWGN channel. Similar
results are also established for a fading channel where full CSI is available
at the receiver.Comment: 59 pages, 6 figure
Integrable representations of the quantum affine special linear superalgebra
The simple integrable modules with finite dimensional weight spaces are
classified for the quantum affine special linear superalgebra
\U_q(\hat{\mathfrak{sl}}(M|N)) at generic . Any such module is shown to be
a highest weight or lowest weight module with respect to one of the two natural
triangular decompositions of the quantum affine superalgebra depending on
whether the level of the module is zero or not. Furthermore, integrable
\U_q(\hat{\mathfrak{sl}}(M|N))-modules at nonzero levels exist only if or
is .Comment: 31 page
Ultra High Energy Cosmic Rays: Strangelets? -- Extra dimensions, TeV-scale black holes and strange matter
The conjecture that ultra high energy cosmic rays (UHECRs) are actually
strangelets is discussed. Besides the reason that strangelets can do as cosmic
rays beyond the GZK-cutoff, another argument to support the conjecture is
addressed in this letter via the study of formation of TeV-scale microscopic
black holes when UHECRs bombarding bare strange stars. It is proposed that the
exotic quark surface of a bare strange star could be an effective
astro-laboratory in the investigations of the extra dimensions and of the
detection of ultra-high energy neutrino fluxes. The flux of neutrinos (and
other point-like particles) with energy >2.3 x 10^{20} eV could be expected to
be smaller than 10^{-26} cm^{-2}$ s^{-1} if there are two extra spatial
dimensions.Comment: accepted by Chin. Phys. Lett., or at
http://vega.bac.pku.edu.cn/~rxxu/publications/index_P.ht
Method and apparatus for contour mapping using synthetic aperture radar
By using two SAR antennas spaced a known distance, B, and oriented at substantially the same look angle to illuminate the same target area, pixel data from the two antennas may be compared in phase to determine a difference delta phi from which a slant angle theta is determined for each pixel point from an equation Delta phi = (2 pi B/lambda)sin(theta - alpha), where lambda is the radar wavelength and alpha is the roll angle of the aircraft. The height, h, of each pixel point from the aircraft is determined from the equation h = R cos theta, and from the known altitude, a, of the aircraft above sea level, the altitude (elevation), a', of each point is determined from the difference a - h. This elevation data may be displayed with the SAR image by, for example, quantizing the elevation at increments of 100 feet starting at sea level, and color coding pixels of the same quantized elevation. The distance, d, of each pixel from the ground track of the aircraft used for the display may be determined more accurately from the equation d = R sin theta
Learning Loosely Connected Markov Random Fields
We consider the structure learning problem for graphical models that we call
loosely connected Markov random fields, in which the number of short paths
between any pair of nodes is small, and present a new conditional independence
test based algorithm for learning the underlying graph structure. The novel
maximization step in our algorithm ensures that the true edges are detected
correctly even when there are short cycles in the graph. The number of samples
required by our algorithm is C*log p, where p is the size of the graph and the
constant C depends on the parameters of the model. We show that several
previously studied models are examples of loosely connected Markov random
fields, and our algorithm achieves the same or lower computational complexity
than the previously designed algorithms for individual cases. We also get new
results for more general graphical models, in particular, our algorithm learns
general Ising models on the Erdos-Renyi random graph G(p, c/p) correctly with
running time O(np^5).Comment: 45 pages, minor revisio
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