101 research outputs found
Cyclone Codes
We introduce Cyclone codes which are rateless erasure resilient codes. They
combine Pair codes with Luby Transform (LT) codes by computing a code symbol
from a random set of data symbols using bitwise XOR and cyclic shift
operations. The number of data symbols is chosen according to the Robust
Soliton distribution. XOR and cyclic shift operations establish a unitary
commutative ring if data symbols have a length of bits, for some prime
number . We consider the graph given by code symbols combining two data
symbols. If such random pairs are given for data symbols, then a
giant component appears, which can be resolved in linear time. We can extend
Cyclone codes to data symbols of arbitrary even length, provided the Goldbach
conjecture holds.
Applying results for this giant component, it follows that Cyclone codes have
the same encoding and decoding time complexity as LT codes, while the overhead
is upper-bounded by those of LT codes. Simulations indicate that Cyclone codes
significantly decreases the overhead of extra coding symbols
Simulation of a first prototypical 3D solution for Indoor Localization based on Directed and Reflected Signals
We introduce a solution for a specific case of Indoor Localization which
involves a directed signal, a reflected signal from the wall and the time
difference between them. This solution includes robust localization with a
given wall, finding the right wall from a group of walls, obtaining the
reflecting wall from measurements, using averaging techniques for improving
measurements with errors and successfully grouping measurements regarding
reflecting walls. It also includes performing self-calibration by computation
of wall distance and direction introducing algorithms such as All pairs,
Disjoint pairs and Overlapping pairs and clustering walls based on Inversion
and Gnomonic Projection. Several of these algorithms are then compared in order
to ameliorate the effects of measurement errors.Comment: 16 page
Seminar Delay-Tolerant Networks
Abstract In this paper, I introduce main concepts of delay-tolerant networks and their architecture. Delay-tolerant networks are designed to operate in environments characterized by very long delay paths and frequent network partitions. In the end, I give results of performance evaluation of the DTN store-and-forward approach compared to traditional Internet data transfer protocols. The paper does not introduce novel approaches created by the author himself but rather should be seen as a summarized review of reference papers
Robust Tracking of a Mobile Beacon using Time Differences of Arrival with Simultaneous Calibration of Receiver Positions
Abstract-Localization based on time differences of arrival (TDOA) has turned out to be a promising approach when neither receiver positions nor the positions of signal origins are known a priori. In this paper, we consider calibration-free tracking of a mobile beacon using TDOA, i.e., the positions of the receivers are not given. We propose a probabilistic formulation using a particle filter to simultaneously localize the signal beacon and the receivers. Our method is robust against measurement outliers and incorrect initialization. This is achieved through a probabilistic sensor model for TDOA data which explicitly considers the measurement uncertainty and takes into account disproportional errors caused by measurement outliers. For the reliable initialization of the particle filter, we apply an iterative optimization approach to multiple subsets of TDOA data, where the best solution is implicitly selected by appropriate weighing of the sensor model. We verify the robustness of our approach in extensive experiments in a spacious indoor environment by an ultrasound beacon moving on various trajectories. We demonstrate that our approach ensures a proper initialization of the particle filter and provides accurate position estimates for the signal beacon and the receivers even in case of measurement outliers. Compared to position references of an optical motion capture system we achieve mean position errors below 5 centimeters
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