7,931 research outputs found
Iterative decoding for MIMO channels via modified sphere decoding
In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain
Information recovery from rank-order encoded images
The time to detection of a visual stimulus by the primate eye is recorded at
100 ā 150ms. This near instantaneous recognition is in spite of the considerable
processing required by the several stages of the visual pathway to recognise and
react to a visual scene. How this is achieved is still a matter of speculation.
Rank-order codes have been proposed as a means of encoding by the primate
eye in the rapid transmission of the initial burst of information from the sensory
neurons to the brain. We study the efficiency of rank-order codes in encoding
perceptually-important information in an image. VanRullen and Thorpe built a
model of the ganglion cell layers of the retina to simulate and study the viability
of rank-order as a means of encoding by retinal neurons. We validate their model
and quantify the information retrieved from rank-order encoded images in terms
of the visually-important information recovered. Towards this goal, we apply
the āperceptual information preservation algorithmā, proposed by Petrovic and
Xydeas after slight modification. We observe a low information recovery due
to losses suffered during the rank-order encoding and decoding processes. We
propose to minimise these losses to recover maximum information in minimum
time from rank-order encoded images. We first maximise information recovery by
using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder
decoding. We then apply the biological principle of lateral inhibition to
minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap
Correction algorithm. To test the perfomance of rank-order codes in
a biologically realistic model, we design and simulate a model of the foveal-pit
ganglion cells of the retina keeping close to biological parameters. We use this
as a rank-order encoder and analyse its performance relative to VanRullen and
Thorpeās retinal model
Temperature 1 Self-Assembly: Deterministic Assembly in 3D and Probabilistic Assembly in 2D
We investigate the power of the Wang tile self-assembly model at temperature
1, a threshold value that permits attachment between any two tiles that share
even a single bond. When restricted to deterministic assembly in the plane, no
temperature 1 assembly system has been shown to build a shape with a tile
complexity smaller than the diameter of the shape. In contrast, we show that
temperature 1 self-assembly in 3 dimensions, even when growth is restricted to
at most 1 step into the third dimension, is capable of simulating a large class
of temperature 2 systems, in turn permitting the simulation of arbitrary Turing
machines and the assembly of squares in near optimal
tile complexity. Further, we consider temperature 1 probabilistic assembly in
2D, and show that with a logarithmic scale up of tile complexity and shape
scale, the same general class of temperature systems can be simulated
with high probability, yielding Turing machine simulation and
assembly of squares with high probability. Our results show a sharp
contrast in achievable tile complexity at temperature 1 if either growth into
the third dimension or a small probability of error are permitted. Motivated by
applications in nanotechnology and molecular computing, and the plausibility of
implementing 3 dimensional self-assembly systems, our techniques may provide
the needed power of temperature 2 systems, while at the same time avoiding the
experimental challenges faced by those systems
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