7,134 research outputs found
Hidden Markov Models for Pipeline Damage Detection Using Piezoelectric Transducers
Oil and gas pipeline leakages lead to not only enormous economic loss but
also environmental disasters. How to detect the pipeline damages including
leakages and cracks has attracted much research attention. One of the promising
leakage detection method is to use lead zirconate titanate (PZT) transducers to
detect the negative pressure wave when leakage occurs. PZT transducers can
generate and detect guided stress waves for crack detection also. However, the
negative pressure waves or guided stress waves may not be easily detected with
environmental interference, e.g., the oil and gas pipelines in offshore
environment. In this paper, a Gaussian mixture model based hidden Markov model
(GMM-HMM) method is proposed to detect the pipeline leakage and crack depth in
changing environment and time-varying operational conditions. Leakages in
different sections or crack depths are considered as different states in hidden
Markov models (HMM). Laboratory experiments show that the GMM-HMM method can
recognize the crack depth and leakage of pipeline such as whether there is a
leakage, where the leakage is
Increasing Flash Memory Lifetime by Dynamic Voltage Allocation for Constant Mutual Information
The read channel in Flash memory systems degrades over time because the
Fowler-Nordheim tunneling used to apply charge to the floating gate eventually
compromises the integrity of the cell because of tunnel oxide degradation.
While degradation is commonly measured in the number of program/erase cycles
experienced by a cell, the degradation is proportional to the number of
electrons forced into the floating gate and later released by the erasing
process. By managing the amount of charge written to the floating gate to
maintain a constant read-channel mutual information, Flash lifetime can be
extended. This paper proposes an overall system approach based on information
theory to extend the lifetime of a flash memory device. Using the instantaneous
storage capacity of a noisy flash memory channel, our approach allocates the
read voltage of flash cell dynamically as it wears out gradually over time. A
practical estimation of the instantaneous capacity is also proposed based on
soft information via multiple reads of the memory cells.Comment: 5 pages. 5 figure
Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity
In orthogonal frequency division modulation (OFDM) communication systems,
channel state information (CSI) is required at receiver due to the fact that
frequency-selective fading channel leads to disgusting inter-symbol
interference (ISI) over data transmission. Broadband channel model is often
described by very few dominant channel taps and they can be probed by
compressive sensing based sparse channel estimation (SCE) methods, e.g.,
orthogonal matching pursuit algorithm, which can take the advantage of sparse
structure effectively in the channel as for prior information. However, these
developed methods are vulnerable to both noise interference and column
coherence of training signal matrix. In other words, the primary objective of
these conventional methods is to catch the dominant channel taps without a
report of posterior channel uncertainty. To improve the estimation performance,
we proposed a compressive sensing based Bayesian sparse channel estimation
(BSCE) method which can not only exploit the channel sparsity but also mitigate
the unexpected channel uncertainty without scarifying any computational
complexity. The propose method can reveal potential ambiguity among multiple
channel estimators that are ambiguous due to observation noise or correlation
interference among columns in the training matrix. Computer simulations show
that propose method can improve the estimation performance when comparing with
conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa
Thermometry of ultracold atoms via non-equilibrium work distributions
Estimating the temperature of a cold quantum system is difficult. Usually,
one measures a well-understood thermal state and uses that prior knowledge to
infer its temperature. In contrast, we introduce a method of thermometry that
assumes minimal knowledge of the state of a system and is potentially
non-destructive. Our method uses a universal temperature-dependence of the
quench dynamics of an initially thermal system coupled to a qubit probe that
follows from the Tasaki-Crooks theorem for non-equilibrium work distributions.
We provide examples for a cold-atom system, in which our thermometry protocol
may retain accuracy and precision at subnanokelvin temperatures.Comment: Updated to published version. 6 pages plus 11 pages of supplemental
material, and some numerical dat
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