2,793 research outputs found
Signature extension preprocessing for LANDSAT MSS data
There are no author-identified significant results in this report
Smoothing dynamic positron emission tomography time courses using functional principal components
A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows Subsequent analysis by methods Such as Spectral Analysis to be substantially improved in terms of their mean squared error
Signature extension using transformed cluster statistics and related techniques
There are no author-identified significant results in this report
Improving discrimination of Raman spectra by optimising preprocessing strategies on the basis of the ability to refine the relationship between variance components
Discrimination of the samples into predefined groups is the issue at hand in many fields, such as medicine,
environmental and forensic studies, etc. Its success strongly depends on the effectiveness of groups separation,
which is optimal when the group means are much more distant than the data within the groups, i.e. the variation
of the group means is greater than the variation of the data averaged over all groups. The task is particularly
demanding for signals (e.g. spectra) as a lot of effort is required to prepare them in a way to uncover interesting
features and turn them into more meaningful information that better fits for the purpose of data analysis. The
solution can be adequately handled by using preprocessing strategies which should highlight the features relevant
for further analysis (e.g. discrimination) by removing unwanted variation, deteriorating effects, such as noise or
baseline drift, and standardising the signals. The aim of the research was to develop an automated procedure for
optimising the choice of the preprocessing strategy to make it most suitable for discrimination purposes. The
authors propose a novel concept to assess the goodness of the preprocessing strategy using the ratio of the
between-groups to within-groups variance on the first latent variable derived from regularised MANOVA that is
capable of exposing the groups differences for highly multidimensional data. The quest for the best preprocessing
strategy was carried out using the grid search and much more efficient genetic algorithm. The adequacy of this
novel concept, that remarkably supports the discrimination analysis, was verified through the assessment of the
capability of solving two forensic comparison problems - discrimination between differently-aged bloodstains and
various car paints described by Raman spectra - using likelihood ratio framework, as a recommended tool for
discriminating samples in the forensics
Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model
Typical fMRI studies have focused on either the mean trend in the
blood-oxygen-level-dependent (BOLD) time course or functional connectivity
(FC). However, other statistics of the neuroimaging data may contain important
information. Despite studies showing links between the variance in the BOLD
time series (BV) and age and cognitive performance, a formal framework for
testing these effects has not yet been developed. We introduce the Variance
Design General Linear Model (VDGLM), a novel framework that facilitates the
detection of variance effects. We designed the framework for general use in any
fMRI study by modeling both mean and variance in BOLD activation as a function
of experimental design. The flexibility of this approach allows the VDGLM to i)
simultaneously make inferences about a mean or variance effect while
controlling for the other and ii) test for variance effects that could be
associated with multiple conditions and/or noise regressors. We demonstrate the
use of the VDGLM in a working memory application and show that engagement in a
working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure
Using Near-Infrared Reflectance Spectroscopy (NIRS) for Qualitative determination of undesirable chemical component of high nitrogen content in protein raw material used for fish feed
Food safety and authenticity are important issue. Ingredients presenting high value are the most vulnerable for adulteration as the common practice is to replace original substance partially with cheap and easily available substance for economic gains. Authentication is also of concern to manufacturers who do not wish to be subjected to unfair competition. Fishmeal has been the major source of protein in feeds for farmed fish. Due to increase in the growth of aquaculture production and limited availability of FM, alternative protein sources such as plant proteins (PP) are used. Wheat gluten is a PP source that has given promising results. Wheat gluten is made by washing wheat flour dough with water until all the starch granules and soluble fiber have been removed. It is a high protein raw material with good digestibility and interesting amino acid profile in addition to be used for its binding property. Due to these qualities use of wheat gluten as plant protein source has considerably increased in aquaculture feeds. The aim of this study is to use NIRS and chemometric tools for the early discrimination of adulterated wheat gluten samples from pure wheat gluten samples. A SIMCA model was developed to discriminate between adulterated and unadulterated samples. SIMCA model showed 100 % classification at adulteration level of 3000 ppm. Thus, NIRS together with SIMCA model represent an attractive option for quality screening without sample pretreatments.Master's Thesis in ChemistryMAMN-KJEMKJEM39
Information extraction techniques for multispectral scanner data
The applicability of recognition-processing procedures for multispectral scanner data from areas and conditions used for programming the recognition computers to other data from different areas viewed under different measurement conditions was studied. The reflective spectral region approximately 0.3 to 3.0 micrometers is considered. A potential application of such techniques is in conducting area surveys. Work in three general areas is reported: (1) Nature of sources of systematic variation in multispectral scanner radiation signals, (2) An investigation of various techniques for overcoming systematic variations in scanner data; (3) The use of decision rules based upon empirical distributions of scanner signals rather than upon the usually assumed multivariate normal (Gaussian) signal distributions
Utforsking av overgangen fra tradisjonell dataanalyse til metoder med maskin- og dyp læring
Data analysis methods based on machine- and deep learning approaches are
continuously replacing traditional methods. Models based on deep learning (DL)
are applicable to many problems and often have better prediction performance
compared to traditional methods. One major difference between the traditional
methods and machine learning (ML) approaches is the black box aspect often
associated with ML and DL models. The use of ML and DL models offers
many opportunities but also challenges. This thesis explores some of these
opportunities and challenges of DL modelling with a focus on applications in
spectroscopy.
DL models are based on artificial neural networks (ANNs) and are known to
automatically find complex relations in the data. In Paper I, this property is
exploited by designing DL models to learn spectroscopic preprocessing based on
classical preprocessing techniques. It is shown that the DL-based preprocessing
has some merits with regard to prediction performance, but there is considerable
extra effort required when training and tuning these DL models. The flexibility
of ANN architecture designs is further studied in Paper II when a DL model for
multiblock data analysis is proposed which can also quantify the importance of
each data block.
A drawback of the DL models is the lack of interpretability. To address this,
a different modelling approach is taken in Paper III where the focus is to use
DL models in such a way as to retain as much interpretability as possible. The
paper presents the concept of non-linear error modelling, where the DL model
is used to model the residuals of the linear model instead of the raw input
data. The concept is essentially a shrinking of the black box aspect since the
majority of the data modelling is done by a linear interpretable model.
The final topic explored in this thesis is a more traditional modelling approach
inspired by DL techniques. Data sometimes contain intrinsic subgroups which
might be more accurately modelled separately than with a global model. Paper
IV presents a modelling framework based on locally weighted models and
fuzzy partitioning that automatically finds relevant clusters and combines the predictions of each local model. Compared to a DL model, the locally weighted
modelling framework is more transparent. It is also shown how the framework
can utilise DL techniques to be scaled to problems with huge amounts of data.Metoder basert på maskin- og dyp læring erstatter i stadig økende grad tradisjonell
datamodellering. Modeller basert på dyp læring (DL) kan brukes på
mange problemer og har ofte bedre prediksjonsevne sammenlignet med tradisjonelle
metoder. En stor forskjell mellom tradisjonelle metoder og metoder
basert på maskinlæring (ML) er den "svarte boksen" som ofte forbindes med
ML- og DL-modeller. Bruken av ML- og DL-modeller åpner opp for mange
muligheter, men også utfordringer. Denne avhandlingen utforsker noen av disse
mulighetene og utfordringene med DL modeller, fokusert på anvendelser innen
spektroskopi.
DL-modeller er basert på kunstige nevrale nettverk (KNN) og er kjent for å
kunne finne komplekse relasjoner i data. I Artikkel I blir denne egenskapen
utnyttet ved å designe DL-modeller som kan lære spektroskopisk preprosessering
basert på klassiske preprosesseringsteknikker. Det er vist at DL-basert
preprosessering kan være gunstig med tanke på prediksjonsevne, men det kreves
større innsats for å trene og justere disse DL-modellene. Fleksibiliteten til
design av KNN-arkitekturer er studert videre i Artikkel II hvor en DL-modell
for analyse av multiblokkdata er foreslått, som også kan kvantifisere viktigheten
til hver datablokk.
En ulempe med DL-modeller er manglende muligheter for tolkning. For å
adressere dette, er en annen modelleringsframgangsmåte brukt i Artikkel III,
hvor fokuset er på å bruke DL-modeller på en måte som bevarer mest mulig
tolkbarhet. Artikkelen presenterer konseptet ikke-lineær feilmodellering, hvor
en DL-modell blir bruk til å modellere residualer fra en lineær modell i stedet
for rå inputdata. Konseptet kan ses på som en krymping av den svarte boksen,
siden mesteparten av datamodelleringen er gjort av en lineær, tolkbar modell.
Det siste temaet som er utforsket i denne avhandlingen er nærmere en tradisjonell
modelleringsvariant, men som er inspirert av DL-teknikker. Data har av
og til iboende undergrupper som kan bli mer nøyaktig modellert hver for seg
enn med en global modell. Artikkel IV presenterer et modelleringsrammeverk
basert på lokalt vektede modeller og "fuzzy" oppdeling, som automatisk finner relevante grupperinger ("clusters") og kombinerer prediksjonene fra hver lokale
modell. Sammenlignet med en DL-modell, er det lokalt vektede modelleringsrammeverket
mer transparent. Det er også vist hvordan rammeverket kan
utnytte teknikker fra DL for å skalere opp til problemer med store mengder
data
Second order scattering descriptors predict fMRI activity due to visual textures
Second layer scattering descriptors are known to provide good classification
performance on natural quasi-stationary processes such as visual textures due
to their sensitivity to higher order moments and continuity with respect to
small deformations. In a functional Magnetic Resonance Imaging (fMRI)
experiment we present visual textures to subjects and evaluate the predictive
power of these descriptors with respect to the predictive power of simple
contour energy - the first scattering layer. We are able to conclude not only
that invariant second layer scattering coefficients better encode voxel
activity, but also that well predicted voxels need not necessarily lie in known
retinotopic regions.Comment: 3nd International Workshop on Pattern Recognition in NeuroImaging
(2013
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