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Probability of error in MMSE multiuser detection
IEEE Transactions on Information Theory, 1997Summary: Performance analysis of the minimum-mean-square-error (MMSE) linear multiuser detector is considered in an environment of nonorthogonal signaling and additive white Gaussian noise. In particular, the behavior of the multiple-access interference (MAI) at the output of the MMSE detector is examined under various asymptotic conditions, including:
Harold Vincent Poor, Sergio Verdú
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Bivariate probability-based anomaly detection
2014 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2014), 2014Statistical techniques play a crucial role in anomaly detection. Although they usually are simple and can be trained unsupervised, they face three challenges: parametric techniques usually rely on the assumption that the data meet a special distribution; existing Histogram-based techniques only take account of individual attribute, which cannot capture
Hua Lou, Ye Zhu 0002
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Probability detection mechanisms and motor learning
Experimental Brain Research, 2004The automatic detection of patterns or regularities in the environment is central to certain forms of motor learning, which are largely procedural and implicit. The rules underlying the detection and use of probabilistic information in the perceptual-motor domain are largely unknown.
O V, Lungu +4 more
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On the Probability of Detecting a Region by a Linear Search
Theory of Probability & Its Applications, 1964Lying in a circle G is a convex rectifiable region K. If a cut L is made in the circle at random then the probability of cutting the region K is \[ P = \frac{{\frac{2}{\pi }LV(K) + W(K)}}{{\pi R^2 - L\sqrt {R^2 - \frac{{L^2 }}{4}} - R^2 \arcsin \frac{L}{{2R}}}}. \] Here, $V(K)$ and $W(K)$ are the linear and planar variations of K in the sense of A.
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Outlier Detection with Arbitrary Probability Functions
2013We consider the problem of unsupervised outlier detection in large collections of data objects when objects are modeled by means of arbitrary multidimensional probability density functions. Specifically, we present a novel definition of outlier in the context of uncertain data under the attribute level uncertainty model, according to which an uncertain
Angiulli F, FASSETTI, Fabio
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Probability of Detection and Reliability
2016Ultrasonic nondestructive evaluation is often used to help guarantee safety and reliability of critical structures and systems. Thus, it is natural to leverage the use of ultrasonic models in the determination of inspection parameters such as probability of detection (POD) and in estimating the influence that inspections have on reliability.
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Probability of Signal Detection in a Vigilance Task
Science, 1962It is hypothesized that the probability of detecting a signal in a vigilance task depends upon its temporal location with respect to the preceding series of signals. Probability of detection should be at a maximum when the signal occurs after a temporal interval which is equivalent to the mean of the intervals between the preceding signals detected ...
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Computing the Probability of False Watermark Detection
2000Several methods of watermark detection involve computing a vector from some input media, computing the normalized correlation between that vector and a predefined watermark vector, and comparing the result against a threshold. We show that, if the probability density function of vectors that arise from random, unwatermarked media is a zero-mean ...
Matthew L. Miller, Jeffrey A. Bloom
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The detection probabilities in simultaneous pursuit games
1994One could expect that the difference in detection probabilities for optimal pursuit and search strategies will be small for small l (detection radius), but the above examples show that this is not always case. This suggests that the choosing of the payoff function must be done with special care by the applied mathematitian, because the intuitive ...
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