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16,547
Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids
, 1996
"... In order to reuse existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known ..."
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Cited by 200 (45 self)
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to the known starting point. This paper describes the position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment. Our method is designed to work with standard sensors and is independent of any knowledge about the starting point
Position Tracking with Position Probability Grids
 In Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots. IEEE Computer
, 1996
"... One of the main problems in the field of mobile robotics is the estimation of the robot's position in the environment. Position probability grids have been proven to be a robust technique for the estimation of the absolute position of a mobile robot. In this paper we describe an application of ..."
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Cited by 11 (2 self)
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One of the main problems in the field of mobile robotics is the estimation of the robot's position in the environment. Position probability grids have been proven to be a robust technique for the estimation of the absolute position of a mobile robot. In this paper we describe an application
Hidden Variables or Positive Probabilities
 Int. J. Theor. Phys
, 2001
"... Despite claims that Bell’s inequalities are based on the Einstein locality condition, or equivalent, all derivations make an identical mathematical assumption: that local hiddenvariable theories produce a set of positivedefinite probabilities for detecting a particle with a given spin orientation. ..."
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Cited by 2 (0 self)
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Despite claims that Bell’s inequalities are based on the Einstein locality condition, or equivalent, all derivations make an identical mathematical assumption: that local hiddenvariable theories produce a set of positivedefinite probabilities for detecting a particle with a given spin orientation
Positions, probabilities, and levels of categorisation
, 2000
"... of the 20th century, three lessons stand out. The first is that prosody and other positional information cannot be separated from the specification of phonetic contrast. Children learn the sounds of their native languages in context, and machine systems for synthesis and recognition can be improved ..."
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Cited by 1 (0 self)
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by taking position into account. The second is that frequency matters. Many recent studies suggest that humans (like modern speech recognition systems) use probabilities to interpret the speech signal, and there seems to be no level of phonological knowledge that can be encapsulated away from probabilistic
PROBABILITY INEQUALITIES FOR SUMS OF BOUNDED RANDOM VARIABLES
, 1962
"... Upper bounds are derived for the probability that the sum S of n independent random variables exceeds its mean ES by a positive number nt. It is assumed that the range of each summand of S is bounded or bounded above. The bounds for Pr(SES> nt) depend only on the endpoints of the ranges of the s ..."
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Cited by 2215 (2 self)
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Upper bounds are derived for the probability that the sum S of n independent random variables exceeds its mean ES by a positive number nt. It is assumed that the range of each summand of S is bounded or bounded above. The bounds for Pr(SES> nt) depend only on the endpoints of the ranges
Maximum entropy markov models for information extraction and segmentation
, 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
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Cited by 561 (18 self)
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, capitalization, formatting, partofspeech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We
Attention and the detection of signals
 Journal of Experimental Psychology: General
, 1980
"... Detection of a visual signal requires information to reach a system capable of eliciting arbitrary responses required by the experimenter. Detection latencies are reduced when subjects receive a cue that indicates where in the visual field the signal will occur. This shift in efficiency appears to b ..."
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Cited by 565 (2 self)
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about the way in which expectancy improves performance. First, when subjects are cued on each trial, they show stronger expectancy effects than when a probable position is held constant for a block, indicating the active nature of the expectancy. Second, while information on spatial position improves
RealTime Tracking of NonRigid Objects using Mean Shift
 IEEE CVPR 2000
, 2000
"... A new method for realtime tracking of nonrigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) an ..."
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Cited by 815 (19 self)
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A new method for realtime tracking of nonrigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution
Network Applications of Bloom Filters: A Survey
 INTERNET MATHEMATICS
, 2002
"... A Bloomfilter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been used in ..."
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Cited by 522 (17 self)
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A Bloomfilter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Bloom filters allow false positives but the space savings often outweigh this drawback when the probability of an error is controlled. Bloom filters have been used
The Capacity of LowDensity ParityCheck Codes Under MessagePassing Decoding
, 2001
"... In this paper, we present a general method for determining the capacity of lowdensity paritycheck (LDPC) codes under messagepassing decoding when used over any binaryinput memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chos ..."
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Cited by 574 (9 self)
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exponentially fast in the length of the code with arbitrarily small loss in rate.) Conversely, transmitting at rates above this capacity the probability of error is bounded away from zero by a strictly positive constant which is independent of the length of the code and of the number of iterations performed
Results 1  10
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16,547