11,497 research outputs found
Fast Decoder for Overloaded Uniquely Decodable Synchronous Optical CDMA
In this paper, we propose a fast decoder algorithm for uniquely decodable
(errorless) code sets for overloaded synchronous optical code-division
multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a
way that the users can uniquely recover the information bits with a very simple
decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML)
decoder, which has a high computational complexity for even moderate code
lengths, the proposed decoder has much lower computational complexity.
Simulation results in terms of bit error rate (BER) demonstrate that the
performance of the proposed decoder for a given BER requires only 1-2 dB higher
signal-to-noise ratio (SNR) than the ML decoder.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0395
Robust 3-Dimensional Object Recognition using Stereo Vision and Geometric Hashing
We propose a technique that combines geometric hashing with stereo vision. The idea is to use the robustness of geometric hashing to spurious data to overcome the correspondence problem, while the stereo vision setup enables direct model matching using the 3-D object models. Furthermore, because the matching technique relies on the relative positions of local features, we should be able to perform robust recognition even with partially occluded objects. We tested this approach with simple geometric objects using a corner point detector. We successfully recognized objects even in scenes where the objects were partially occluded by other objects. For complicated scenes, however, the limited set of model features and required amount of computing time, sometimes became a proble
Too good to be true: when overwhelming evidence fails to convince
Is it possible for a large sequence of measurements or observations, which
support a hypothesis, to counterintuitively decrease our confidence? Can
unanimous support be too good to be true? The assumption of independence is
often made in good faith, however rarely is consideration given to whether a
systemic failure has occurred.
Taking this into account can cause certainty in a hypothesis to decrease as
the evidence for it becomes apparently stronger. We perform a probabilistic
Bayesian analysis of this effect with examples based on (i) archaeological
evidence, (ii) weighing of legal evidence, and (iii) cryptographic primality
testing.
We find that even with surprisingly low systemic failure rates high
confidence is very difficult to achieve and in particular we find that certain
analyses of cryptographically-important numerical tests are highly optimistic,
underestimating their false-negative rate by as much as a factor of
Probabilistic learning for selective dissemination of information
New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile
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