11,529 research outputs found
Validating a new methodology for optical probe design and image registration in fNIRS studies
Functional near-infrared spectroscopy (fNIRS) is an imaging technique that relies on the principle of shining near-infrared light through tissue to detect changes in hemodynamic activation. An important methodological issue encountered is the creation of optimized probe geometry for fNIRS recordings. Here, across three experiments, we describe and validate a processing pipeline designed to create an optimized, yet scalable probe geometry based on selected regions of interest (ROIs) from the functional magnetic resonance imaging (fMRI) literature. In experiment 1, we created a probe geometry optimized to record changes in activation from target ROIs important for visual working memory. Positions of the sources and detectors of the probe geometry on an adult head were digitized using a motion sensor and projected onto a generic adult atlas and a segmented head obtained from the subject's MRI scan. In experiment 2, the same probe geometry was scaled down to fit a child's head and later digitized and projected onto the generic adult atlas and a segmented volume obtained from the child's MRI scan. Using visualization tools and by quantifying the amount of intersection between target ROIs and channels, we show that out of 21 ROIs, 17 and 19 ROIs intersected with fNIRS channels from the adult and child probe geometries, respectively. Further, both the adult atlas and adult subject-specific MRI approaches yielded similar results and can be used interchangeably. However, results suggest that segmented heads obtained from MRI scans be used for registering children's data. Finally, in experiment 3, we further validated our processing pipeline by creating a different probe geometry designed to record from target ROIs involved in language and motor processing
Sparsity for Ultrafast Material Identification
Mid-infrared spectroscopy is often used to identify material. Thousands of
spectral points are measured in a time-consuming process using expensive
table-top instrument. However, material identification is a sparse problem,
which in theory could be solved with just a few measurements. Here we exploit
the sparsity of the problem and develop an ultra-fast, portable, and
inexpensive method to identify materials. In a single-shot, a mid-infrared
camera can identify materials based on their spectroscopic signatures. This
method does not require prior calibration, making it robust and versatile in
handling a broad range of materials
Bayesian modelling and quantification of Raman spectroscopy
Raman spectroscopy can be used to identify molecules such as DNA by the
characteristic scattering of light from a laser. It is sensitive at very low
concentrations and can accurately quantify the amount of a given molecule in a
sample. The presence of a large, nonuniform background presents a major
challenge to analysis of these spectra. To overcome this challenge, we
introduce a sequential Monte Carlo (SMC) algorithm to separate each observed
spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by
additive white noise. The peaks are modelled as Lorentzian, Gaussian, or
pseudo-Voigt functions, while the baseline is estimated using a penalised cubic
spline. This latent continuous representation accounts for differences in
resolution between measurements. The posterior distribution can be
incrementally updated as more data becomes available, resulting in a scalable
algorithm that is robust to local maxima. By incorporating this representation
in a Bayesian hierarchical regression model, we can quantify the relationship
between molecular concentration and peak intensity, thereby providing an
improved estimate of the limit of detection, which is of major importance to
analytical chemistry
Are object detection assessment criteria ready for maritime computer vision?
Maritime vessels equipped with visible and infrared cameras can complement
other conventional sensors for object detection. However, application of
computer vision techniques in maritime domain received attention only recently.
The maritime environment offers its own unique requirements and challenges.
Assessment of the quality of detections is a fundamental need in computer
vision. However, the conventional assessment metrics suitable for usual object
detection are deficient in the maritime setting. Thus, a large body of related
work in computer vision appears inapplicable to the maritime setting at the
first sight. We discuss the problem of defining assessment metrics suitable for
maritime computer vision. We consider new bottom edge proximity metrics as
assessment metrics for maritime computer vision. These metrics indicate that
existing computer vision approaches are indeed promising for maritime computer
vision and can play a foundational role in the emerging field of maritime
computer vision
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