Novel Regression Methods For Spectral Data

Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) are some of the mathematical pre- liminaries that are discussed prior to explaining PLS and PCR models. Both PLS and PCR are applied to real spectral data and their di erences and similarities are discussed in this thesis. The challenge lies in establishing the optimum number of components to be included in either of the models but this has been overcome by using various diagnostic tools suggested in this thesis. Correspondence analysis (CA) and PLS were applied to ecological data. The idea of CA was to correlate the macrophytes species and lakes. The di erences between PLS model for ecological data and PLS for spectral data are noted and explained in this thesis. i

Determination of decomposition products of flotation collector chemicals using capillary electrophoresis

Vaahdotusta käytetään yleisesti erottamaan eri mineraaleja malmista. Tässä menetelmässä käytetään erityisiä pinta-aktiivisia aineita, joita kutsutaan kokoojakemikaaleiksi, muuntamaan halutut mineraalit hydrofobisiksi ja erottamaan ne hydrofiilisistä partikkeleista ilmakuplien avulla. Eräs tärkeimmistä kokoojakemikaalien ryhmistä on ksantaatit. Ksantaateilla on havaittu taipumusta hajota useiksi erilaisiksi hajoamistuotteiksi vaahdotusprosessin aikana. Näillä hajoamistuotteilla voi olla monia haitallisia vaikutuksia vaahdotuksen tuloksiin. Näiden tuotteiden tunnistaminen ja määrittäminen on tärkeää vaahdotusprosessin paremman ymmärtämisen kannalta. Työn kirjallisuusosassa vaahdotusprosessi, ksantaatit ja niiden yleisimmät hajoamistuotteet on esitelty, kuten myös käytetty analyysimenetelmä, kapillaarielektroforeesi. Työn kokeellisessa osassa etsittiin sopivaa erotusmenetelmää etyyliksantaatin, etyylitiokarbonaatin, etyyliperksantaatin ja etyyliksantyylitiosulfaatin erottamiseksi kapillaarilelektroforeesilla. Pääasiassa keskityttiin kahteen eri erotusmenetelmään. Ensimmäinen menetelmä kykeni erottamaan kaikki tutkitut tuotteet puhdasvesinäytteissä, ja toinen menetelmä oli sopiva näiden tuotteiden erottamiseen prosessivesinäytteissä. Jälkimmäistä menetelmää kokeiltiin käytännössä rikastamolla, jossa sillä kyettiin erottamaan isobutyyliksantaatti, isobutyylitiokarbonaatti, ja suurella todennäköisyydellä myös isobutyyliperksantaatti.

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

The kinetics of the gas-phase decomposition of the 2-methyl-2-butoxyl and 2-methyl-2-pentoxyl radicals

The kinetics of the title reactions have been studied by relative-rate methods as a function of temperature. Relative-rate coefficients for the two decomposition channels of 2-methyl-2-butoxyl have been measured at five different temperatures between 283 and 345 K and the observed temperature dependence is consistent with the results of some previous experimental studies. The kinetics of the two decomposition channels of 2-methyl-2-pentoxyl have also been investigated, as a function of temperature, relative to the estimated rate of isomerisation of this radical. Room-temperature rate coefficient data for the two decomposition channels of both 2-methyl-2-pentoxyl and 2-methyl-2-butxoyl (after combining the relative rate coefficient for this latter with a value for the rate coefficient of the major channel, extrapolated from the data presented by Batt et al., Int. J. Chem. Kinet., 1978, 10, 931) are shown to be consistent with a non-linear kinetic correlation, for alkoxyl radical decomposition rate data, previously presented by this laboratory (Johnson et al., Atmos. Environ., 2004, 38, 1755-1765).

A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

The decomposition of some RDX and HMX based materials in the one dimensional time to explosion apparatus. Part 2. Estimating the violence of the cook-off event

Various methods of assessment have been applied to the One Dimensional Time to Explosion (ODTX) apparatus and experiments with the aim of allowing an estimate of the comparative violence of the explosion event to be made. Non-mechanical methods used were a simple visual inspection, measuring the increase in the void volume of the anvils following an explosion and measuring the velocity of the sound produced by the explosion over 1 metre. Mechanical methods used included monitoring piezo-electric devices inserted in the frame of the machine and measuring the rotational velocity of a rotating bar placed on the top of the anvils after it had been displaced by the shock wave. This last method, which resembles original Hopkinson Bar experiments, seemed the easiest to apply and analyse, giving relative rankings of violence and the possibility of the calculation of a “detonation” pressure.

Numerical computation of an analytic singular value decomposition of a matrix valued function

This paper extends the singular value decomposition to a path of matricesE(t). An analytic singular value decomposition of a path of matricesE(t) is an analytic path of factorizationsE(t)=X(t)S(t)Y(t) T whereX(t) andY(t) are orthogonal andS(t) is diagonal. To maintain differentiability the diagonal entries ofS(t) are allowed to be either positive or negative and to appear in any order. This paper investigates existence and uniqueness of analytic SVD's and develops an algorithm for computing them. We show that a real analytic pathE(t) always admits a real analytic SVD, a full-rank, smooth pathE(t) with distinct singular values admits a smooth SVD. We derive a differential equation for the left factor, develop Euler-like and extrapolated Euler-like numerical methods for approximating an analytic SVD and prove that the Euler-like method converges.

Evaluation of empirical mode decomposition for event-related potential analysis

Current methods for estimating event-related potentials (ERPs) assume stationarity of the signal. Empirical Mode Decomposition (EMD) is a data-driven decomposition technique that does not assume stationarity. We evaluated an EMD-based method for estimating the ERP. On simulated data, EMD substantially reduced background EEG while retaining the ERP. EMD-denoised single trials also estimated shape, amplitude, and latency of the ERP better than raw single trials. On experimental data, EMD-denoised trials revealed event-related differences between two conditions (condition A and B) more effectively than trials lowpass filtered at 40 Hz. EMD also revealed event-related differences on both condition A and condition B that were clearer and of longer duration than those revealed by low-pass filtering at 40 Hz. Thus, EMD-based denoising is a promising data-driven, nonstationary method for estimating ERPs and should be investigated further.

Morphological decomposition in Broca's aphasia

Background: Few studies have investigated how individuals diagnosed with post-stroke Broca’s aphasia decompose words into their constituent morphemes in real-time processing. Previous research has focused on morphologically complex words in non-time-constrained settings or in syntactic frames, but not in the lexicon. Aims: We examined real-time processing of morphologically complex words in a group of five Greek-speaking individuals with Broca’s aphasia to determine: (1) whether their morphological decomposition mechanisms are sensitive to lexical (orthography and frequency) vs. morphological (stem-suffix combinatory features) factors during visual word recognition, (2) whether these mechanisms are different in inflected vs. derived forms during lexical access, and (3) whether there is a preferred unit of lexical access (syllables vs. morphemes) for inflected vs. derived forms. Methods & Procedures: The study included two real-time experiments. The first was a semantic judgment task necessitating participants’ categorical judgments for high- and low-frequency inflected real words and pseudohomophones of the real words created by either an orthographic error at the stem or a homophonous (but incorrect) inflectional suffix. The second experiment was a letter-priming task at the syllabic or morphemic boundary of morphologically transparent inflected and derived words whose stems and suffixes were matched for length, lemma and surface frequency. Outcomes & Results: The majority of the individuals with Broca’s aphasia were sensitive to lexical frequency and stem orthography, while ignoring the morphological combinatory information encoded in the inflectional suffix that control participants were sensitive to. The letter-priming task, on the other hand, showed that individuals with aphasia—in contrast to controls—showed preferences with regard to the unit of lexical access, i.e., they were overall faster on syllabically than morphemically parsed words and their morphological decomposition mechanisms for inflected and derived forms were modulated by the unit of lexical access. Conclusions: Our results show that in morphological processing, Greek-speaking persons with aphasia rely mainly on stem access and thus are only sensitive to orthographic violations of the stem morphemes, but not to illegal morphological combinations of stems and suffixes. This possibly indicates an intact orthographic lexicon but deficient morphological decomposition mechanisms, possibly stemming from an underspecification of inflectional suffixes in the participants’ grammar. Syllabic information, however, appears to facilitate lexical access and elicits repair mechanisms that compensate for deviant morphological parsing procedures.

Extracting planetary waves from geomagnetic time series using Empirical Mode Decomposition

Empirical Mode Decomposition is presented as an alternative to traditional analysis methods to decompose geomagnetic time series into spectral components. Important comments on the algorithm and its variations will be given. Using this technique, planetary wave modes of 5-, 10-, and 16-day mean periods can be extracted from magnetic field components of three different stations in Germany. In a second step, the amplitude modulation functions of these wave modes can be shown to contain significant contribution from solar cycle variation through correlation with smoothed sunspot numbers. Additionally, the data indicate connections with geomagnetic jerk occurrences, supported by a second set of data providing reconstructed near-Earth magnetic field for 150 years. Usually attributed to internal dynamo processes within the Earth's outer core, the question of who is impacting whom will be briefly discussed here.

Black-box decomposition approach for computational hemodynamics: One-dimensional models

Increasing efforts exist in integrating different levels of detail in models of the cardiovascular system. For instance, one-dimensional representations are employed to model the systemic circulation. In this context, effective and black-box-type decomposition strategies for one-dimensional networks are needed, so as to: (i) employ domain decomposition strategies for large systemic models (1D-1D coupling) and (ii) provide the conceptual basis for dimensionally-heterogeneous representations (1D-3D coupling, among various possibilities). The strategy proposed in this article works for both of these two scenarios, though the several applications shown to illustrate its performance focus on the 1D-1D coupling case. A one-dimensional network is decomposed in such a way that each coupling point connects two (and not more) of the sub-networks. At each of the M connection points two unknowns are defined: the flow rate and pressure. These 2M unknowns are determined by 2M equations, since each sub-network provides one (non-linear) equation per coupling point. It is shown how to build the 2M x 2M non-linear system with arbitrary and independent choice of boundary conditions for each of the sub-networks. The idea is then to solve this non-linear system until convergence, which guarantees strong coupling of the complete network. In other words, if the non-linear solver converges at each time step, the solution coincides with what would be obtained by monolithically modeling the whole network. The decomposition thus imposes no stability restriction on the choice of the time step size. Effective iterative strategies for the non-linear system that preserve the black-box character of the decomposition are then explored. Several variants of matrix-free Broyden`s and Newton-GMRES algorithms are assessed as numerical solvers by comparing their performance on sub-critical wave propagation problems which range from academic test cases to realistic cardiovascular applications. A specific variant of Broyden`s algorithm is identified and recommended on the basis of its computer cost and reliability. (C) 2010 Elsevier B.V. All rights reserved.

Laurdan Spectrum Decomposition as a Tool for the Analysis of Surface Bilayer Structure and Polarity: a Study with DMPG, Peptides and Cholesterol

The highly hydrophobic fluorophore Laurdan (6-dodecanoyl-2-(dimethylaminonaphthalene)) has been widely used as a fluorescent probe to monitor lipid membranes. Actually, it monitors the structure and polarity of the bilayer surface, where its fluorescent moiety is supposed to reside. The present paper discusses the high sensitivity of Laurdan fluorescence through the decomposition of its emission spectrum into two Gaussian bands, which correspond to emissions from two different excited states, one more solvent relaxed than the other. It will be shown that the analysis of the area fraction of each band is more sensitive to bilayer structural changes than the largely used parameter called Generalized Polarization, possibly because the latter does not completely separate the fluorescence emission from the two different excited states of Laurdan. Moreover, it will be shown that this decomposition should be done with the spectrum as a function of energy, and not wavelength. Due to the presence of the two emission bands in Laurdan spectrum, fluorescence anisotropy should be measured around 480 nm, to be able to monitor the fluorescence emission from one excited state only, the solvent relaxed state. Laurdan will be used to monitor the complex structure of the anionic phospholipid DMPG (dimyristoyl phosphatidylglycerol) at different ionic strengths, and the alterations caused on gel and fluid membranes due to the interaction of cationic peptides and cholesterol. Analyzing both the emission spectrum decomposition and anisotropy it was possible to distinguish between effects on the packing and on the hydration of the lipid membrane surface. It could be clearly detected that a more potent analog of the melanotropic hormone alpha-MSH (Ac-Ser(1)-Tyr(2)-Ser(3)-Met(4)-Glu(5)-His(6)-Phe(7)-Arg(8)-Trp(9)-Gly(10)-Lys(11)-Pro(12)-Val(13)-NH(2)) was more effective in rigidifying the bilayer surface of fluid membranes than the hormone, though the hormone significantly decreases the bilayer surface hydration.