12,589 research outputs found
Co-detection of acoustic emissions during failure of heterogeneous media: new perspectives for natural hazard early warning
A promising method for real time early warning of gravity driven rupture that
considers both the heterogeneity of natural media and characteristics of
acoustic emissions attenuation is proposed. The method capitalizes on
co-detection of elastic waves emanating from micro-cracks by multiple and
spatially separated sensors. Event co-detection is considered as surrogate for
large event size with more frequent co-detected events marking imminence of
catastrophic failure. Using a spatially explicit fiber bundle numerical model
with spatially correlated mechanical strength and two load redistribution
rules, we constructed a range of mechanical failure scenarios and associated
failure events (mapped into AE) in space and time. Analysis considering
hypothetical arrays of sensors and consideration of signal attenuation
demonstrate the potential of the co-detection principles even for insensitive
sensors to provide early warning for imminent global failure
Array signal processing for maximum likelihood direction-of-arrival estimation
Emitter Direction-of-Arrival (DOA) estimation is a fundamental problem in a variety of applications including radar, sonar, and wireless communications. The research has received considerable attention in literature and numerous methods have been proposed. Maximum Likelihood (ML) is a nearly optimal technique producing superior estimates compared to other methods especially in unfavourable conditions, and thus is of significant practical interest. This paper discusses in details the techniques for ML DOA estimation in either white Gaussian noise or unknown noise environment. Their performances are analysed and compared, and evaluated against the theoretical lower bounds
Receptor uptake arrays for vitamin B12, siderophores and glycans shape bacterial communities
Molecular variants of vitamin B12, siderophores and glycans occur. To take up
variant forms, bacteria may express an array of receptors. The gut microbe
Bacteroides thetaiotaomicron has three different receptors to take up variants
of vitamin B12 and 88 receptors to take up various glycans. The design of
receptor arrays reflects key processes that shape cellular evolution.
Competition may focus each species on a subset of the available nutrient
diversity. Some gut bacteria can take up only a narrow range of carbohydrates,
whereas species such as B.~thetaiotaomicron can digest many different complex
glycans. Comparison of different nutrients, habitats, and genomes provide
opportunity to test hypotheses about the breadth of receptor arrays. Another
important process concerns fluctuations in nutrient availability. Such
fluctuations enhance the value of cellular sensors, which gain information
about environmental availability and adjust receptor deployment. Bacteria often
adjust receptor expression in response to fluctuations of particular
carbohydrate food sources. Some species may adjust expression of uptake
receptors for specific siderophores. How do cells use sensor information to
control the response to fluctuations? That question about regulatory wiring
relates to problems that arise in control theory and artificial intelligence.
Control theory clarifies how to analyze environmental fluctuations in relation
to the design of sensors and response systems. Recent advances in deep learning
studies of artificial intelligence focus on the architecture of regulatory
wiring and the ways in which complex control networks represent and classify
environmental states. I emphasize the similar design problems that arise in
cellular evolution, control theory, and artificial intelligence. I connect
those broad concepts to testable hypotheses for bacterial uptake of B12,
siderophores and glycans.Comment: Added many new references, edited throughou
Optimal noise-canceling networks
Natural and artificial networks, from the cerebral cortex to large-scale
power grids, face the challenge of converting noisy inputs into robust signals.
The input fluctuations often exhibit complex yet statistically reproducible
correlations that reflect underlying internal or environmental processes such
as synaptic noise or atmospheric turbulence. This raises the practically and
biophysically relevant of question whether and how noise-filtering can be
hard-wired directly into a network's architecture. By considering generic phase
oscillator arrays under cost constraints, we explore here analytically and
numerically the design, efficiency and topology of noise-canceling networks.
Specifically, we find that when the input fluctuations become more correlated
in space or time, optimal network architectures become sparser and more
hierarchically organized, resembling the vasculature in plants or animals. More
broadly, our results provide concrete guiding principles for designing more
robust and efficient power grids and sensor networks.Comment: 6 pages, 3 figures, supplementary materia
Cation Discrimination in Organic Electrochemical Transistors by Dual Frequency Sensing
In this work, we propose a strategy to sense quantitatively and specifically
cations, out of a single organic electrochemical transistor (OECT) device
exposed to an electrolyte. From the systematic study of six different chloride
salts over 12 different concentrations, we demonstrate that the impedance of
the OECT device is governed by either the channel dedoping at low frequency and
the electrolyte gate capacitive coupling at high frequency. Specific cationic
signatures, which originates from the different impact of the cations behavior
on the poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)
polymer and their conductivity in water, allow their discrimination at the same
molar concentrations. Dynamic analysis of the device impedance at different
frequencies could allow the identification of specific ionic flows which could
be of a great use in bioelectronics to further interpret complex mechanisms in
biological media such as in the brain.Comment: Full text and supporting informatio
A Novel Optical/digital Processing System for Pattern Recognition
This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network
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