185,920 research outputs found
Reflectance Hashing for Material Recognition
We introduce a novel method for using reflectance to identify materials.
Reflectance offers a unique signature of the material but is challenging to
measure and use for recognizing materials due to its high-dimensionality. In
this work, one-shot reflectance is captured using a unique optical camera
measuring {\it reflectance disks} where the pixel coordinates correspond to
surface viewing angles. The reflectance has class-specific stucture and angular
gradients computed in this reflectance space reveal the material class.
These reflectance disks encode discriminative information for efficient and
accurate material recognition. We introduce a framework called reflectance
hashing that models the reflectance disks with dictionary learning and binary
hashing. We demonstrate the effectiveness of reflectance hashing for material
recognition with a number of real-world materials
Cross-Reactive Sensor Array for Metal Ion Sensing Based on Fluorescent SAMs\ud
Fluorescent self assembled monolayers (SAMs) on glass were previouslydeveloped in our group as new sensing materials for metal ions. These fluorescent SAMs arecomprised by fluorophores and small molecules sequentially deposited on a monolayer onglass. The preorganization provided by the surface avoids the need for complex receptordesign, allowing for a combinatorial approach to sensing systems based on small molecules.Now we show the fabrication of an effective microarray for the screening of metal ions andthe properties of the sensing SAMs. A collection of fluorescent sensing SAMs wasgenerated by combinatorial methods and immobilized on the glass surfaces of a custom-made 140 well microtiter-plate. The resulting libraries are easily measured and show variedresponses to a series cations such as Cu2+ , Co2+ , Pb2+ , Ca2+ and Zn2+ . These surfaces are notdesigned to complex selectively a unique analyte but rather they are intended to producefingerprint type responses to a range of analytes by less specific interactions. The unselectiveresponses of the library to the presence of different cations generate a characteristic patternfor each analyte, a “finger print” response.\u
Improved texture image classification through the use of a corrosion-inspired cellular automaton
In this paper, the problem of classifying synthetic and natural texture
images is addressed. To tackle this problem, an innovative method is proposed
that combines concepts from corrosion modeling and cellular automata to
generate a texture descriptor. The core processes of metal (pitting) corrosion
are identified and applied to texture images by incorporating the basic
mechanisms of corrosion in the transition function of the cellular automaton.
The surface morphology of the image is analyzed before and during the
application of the transition function of the cellular automaton. In each
iteration the cumulative mass of corroded product is obtained to construct each
of the attributes of the texture descriptor. In a final step, this texture
descriptor is used for image classification by applying Linear Discriminant
Analysis. The method was tested on the well-known Brodatz and Vistex databases.
In addition, in order to verify the robustness of the method, its invariance to
noise and rotation were tested. To that end, different variants of the original
two databases were obtained through addition of noise to and rotation of the
images. The results showed that the method is effective for texture
classification according to the high success rates obtained in all cases. This
indicates the potential of employing methods inspired on natural phenomena in
other fields.Comment: 13 pages, 14 figure
Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials
The capabilities of image probe experiments are rapidly expanding, providing
new information about quantum materials on unprecedented length and time
scales. Many such materials feature inhomogeneous electronic properties with
intricate pattern formation on the observable surface. This rich spatial
structure contains information about interactions, dimensionality, and disorder
-- a spatial encoding of the Hamiltonian driving the pattern formation. Image
recognition techniques from machine learning are an excellent tool for
interpreting information encoded in the spatial relationships in such images.
Here, we develop a deep learning framework for using the rich information
available in these spatial correlations in order to discover the underlying
Hamiltonian driving the patterns. We first vet the method on a known case,
scanning near-field optical microscopy on a thin film of VO2. We then apply our
trained convolutional neural network architecture to new optical microscope
images of a different VO2 film as it goes through the metal-insulator
transition. We find that a two-dimensional Hamiltonian with both interactions
and random field disorder is required to explain the intricate, fractal
intertwining of metal and insulator domains during the transition. This
detailed knowledge about the underlying Hamiltonian paves the way to using the
model to control the pattern formation via, e.g., tailored hysteresis
protocols. We also introduce a distribution-based confidence measure on the
results of a multi-label classifier, which does not rely on adversarial
training. In addition, we propose a new machine learning based criterion for
diagnosing a physical system's proximity to criticality
Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR
The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range
Development of a novel electrochemical inhibition sensor array based on bacteria immobilized on modified screen-printed gold electrodes for water pollution detection
The development of a novel and simple inhibition biosensor array for detection of water pollutants based on bacteria immobilized on the surface of the electrodes is the main goal of this work. A series of electrochemical measurements (i.e., cyclic voltammograms) were carried out on modified screen-printed gold electrodes with three types of bacteria, namely Escherichia coli, Shewanella oneidensis, and Methylococcus capsulatus (Bath), immobilized via poly l-lysine. For comparison purposes, similar measurements were carried out on bacteria samples in solutions; also optical measurements (fluorescence microscopy, optical density, and flow cytometry) were performed on the same bacteria in both liquid and immobilized forms. The study of the effect of heavy metal ions (lead), pesticides (atrazine), and petrochemicals (hexane) on DC electrochemical characteristics of immobilized bacteria revealed a possibility of pattern recognition of the above inhibition agents in an aquatic environment
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