Results 251 to 260 of about 3,422,913 (297)

Proportion Estimation of Correlated Sequences

SIMULATION, 2001
Simulated estimates of proportions are needed in some simulation studies. We propose a quasi-in dependent method to determine the length of a simulation run so that the proportion estimates satisfy a pre-specified precision requirement. The method uses histogram approximation to generate confidence intervals with the desired precision.
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Compound estimation of a monotone sequence

Statistics & Probability Letters, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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An estimate of the sequencing error frequency in the DNA sequence databases

DNA Sequence, 1992
We have examined vector sequences fortuitously present in the EMBL sequence database as contaminating parts of submitted sequences, and found a sequencing error frequency of 3.55% in this subset of release 27 of the database. We discuss the possibility that this value may be representative for corresponding errors in the database as a whole.
T, Kristensen, R, Lopez, H, Prydz
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DISCREPANCY ESTIMATES FOR ROTATION SEQUENCES AND OSCILLATION SEQUENCES

Asian-European Journal of Mathematics, 2012
Two kinds of sequences, which are of interest in problems of uniform distribution and dynamical systems, are considered. The rotation sequences (or Kronecker sequences) (KS) are closely related to the orbits of the rotation map and the oscillation sequences (OS) are a discrete-time form of orbits of the simplest oscillators.
Harald Niederreiter, Ashot Yu. Shahverdian   +1 more
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On Simultaneous System and Input Sequence Estimation

IFAC Proceedings Volumes, 1992
Equalization is concerned with estimation of the input sequence of a linear system given noisy measurements of the output signal. In case the system description is unknown we have the problem of blind equalization. A scheme for blind equalization which is based on the assumption that the input signal belongs to a finite alphabet is proposed.
F. Gustafsson, B. Wahlberg
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Parameter estimation of quasi-periodic sequences

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
Measurements of a process described by t(i)=k/sub i/T+ tau + eta (i), (i=1, 2, . . .), where k/sub i/ are unknown integers and ( eta (i)) is a white noise, are considered. The estimation of the 'period' T and 'phase' tau of such processes is often required in communications and radar. Popular linear regression techniques for this estimation problem are
Eli Fogel, Motti Gavish
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Delayed decision-feedback sequence estimation

IEEE Transactions on Communications, 1989
The authors delayed-decision-feedback sequence estimation (DDFSE) for uncoded PAM signals is considered. Estimates on the performance of the algorithm are given, and simulation results are provided for several examples. A more general form of DDFSE applicable to coded modulation systems is also presented. As an example, detection of trellis-coded QPSK (
Alexandra Duel-Hallen, Chris Heegard
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ADAPTIVE CHANNEL ESTIMATION FOR MAXIMUM LIKELIHOOD SEQUENCE ESTIMATION

IFAC Proceedings Volumes, 1992
Abstract The paper reviews the problem of adaptive channel estimation for maximum likelihood sequence estimation. It explains necessity of channel estimation for operation of the ML receiver, reviews a few of the most important receiver structures, discusses several adaptation algorithms and joint blind adaptive sequence and channel estimation.
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Maximum-Likelihood Sequence Estimation Sequence for OFDM

1996
In this paper, Maximum-Likelihood Sequence Estimation (MLSE) for OFDM is derived together with a corresponding upper bound on probability of error. The error bound of the MLSE is used to assess the performance of an OFDM system with a two-path channel and a linear zero-forcing equaliser.
Anders Vahlin, Nils Holte
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Endometrium estimation in a sequence of ultrasonic images

Computerized Medical Imaging and Graphics, 2001
In this paper a novel algorithm for motion estimation of the endometrium in a sequence of ultrasonic images is presented. The algorithm used is based on the criterion that a pixel of a motion object at different times has a different gray value. The motion estimation includes motion frequency and thickness; the former one is determined by the detection
C H, Yang, P C, Chung, Y C, Tsai
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