Hand somatosensory sub-cortical sources arehypoactive in migraine interictally: a functionalsource separation analysis

Background: Recent morpho-functional evidences pointed out that abnormalities in the thalamus could play a major role in the expression of migraine neurophysiological and clinical correlates. Whether this phenomenon is primary or secondary to its functional disconnection from the brain stem remains to be determined.Aim: We used a Functional Source Separation algorithmof EEG signal to extract the activity of the different neuronal pools recruited at different latencies along the somatosensory pathway in interictal migraine without aura(MO) patients. Method: Twenty MO patients and 20 healthy volunteers(HV) underwent EEG recording. Four ad-hoc functional constraints, two sub-cortical (FS14 at brain stem andFS16 at thalamic level) and two cortical (FS20 radial andFS22 tangential parietal sources), were used to extract the activity of successive stages of somatosensory information processing in response to the separate left and right median nerve electric stimulation. A band-pass digital filter (450–750 Hz) was applied offline in order to extract high-frequency oscillatory (HFO) activity from the broadband EEG signal. Results: In both stimulated sides, significant reduced subcortical brain stem (FS14) and thalamic (FS16) HFO activations characterized MO patients when compared with HV. No difference emerged in the two cortical HFO activations between two groups. Conclusion: Present results are the first neurophysiological evidence supporting the hypothesis that a functional disconnection of the thalamus from the subcortical monoaminergicsystem may underline the interictal cortical abnormal information processing in migraine. Further studiesare needed to investigate the precise directional connectivity across the entire primary subcortical and cortical somatosensory pathway in interictal MO.

Immobilized Microalgae for Nutrient Recovery from Source Separated Urine

Shortages in supply of nutrients and freshwater for a growing human population are critical global issues. Traditional centralized sewage treatment can prevent eutrophication and provide sanitation, but is neither efficient nor sustainable in terms of water and resources. Source separation of household wastes, combined with decentralized resource recovery, presents a novel approach to solve these issues. Urine contains within 1 % of household waste water up to 80 % of the nitrogen (N) and 50 % of the phosphorus (P). Since microalgae are efficient at nutrient uptake, growing these organisms in urine might be a promising technology to concomitantly clean urine and produce valuable biomass containing the major plant nutrients. While state-of-the-art suspension systems for algal cultivation have mayor shortcomings in their application, immobilized cultivation on Porous Substrate Photobioreactors (PSBRs) might be a feasible alternative. The aim of this study was to develop a robust process for nutrient recovery from minimally diluted human urine using microalgae on PSBRs. The green alga Desmodesmus abundans strain CCAC 3496 was chosen for its good growth, after screening 96 algal strains derived from urine-specific isolations and culture collections. Treatment of urine, 1:1 diluted with tap water and without addition of nutrients, was performed at a light intensity of 600 μmol photons m-2 s-1 with 2.5 % CO2 and at pH 6.5. A growth rate of 7.2 g dry weight m-² day-1 and removal efficiencies for N and P of 13.1 % and 94.1 %, respectively, were determined. Pre-treatment of urine with activated carbon was found to eliminate possible detrimental effects of pharmaceuticals. These results provide a basis for further development of the technology at pilot-scale. If found to be safe in terms human and environmental health, the biomass produced from three persons could provide the P for annual production of 31 kg wheat grain and 16 kg soybean, covering the caloric demand in food for almost one month of the year for such a household. In combination with other technologies, PSBRs could thus be applied in a decentralized resource recovery system, contributing to locally close the link between sanitation and food production.

Independent component analysis applied to raman spectra for classification of in vitro human coronary arteries

Optical diagnostic methods, such as near-infrared Raman spectroscopy allow quantification and evaluation of human affecting diseases, which could be useful in identifying and diagnosing atherosclerosis in coronary arteries. The goal of the present work is to apply Independent Component Analysis (ICA) for data reduction and feature extraction of Raman spectra and to perform the Mahalanobis distance for group classification according to histopathology, obtaining feasible diagnostic information to detect atheromatous plaque. An 830nm Ti:sapphire laser pumped by an argon laser provides near-infrared excitation. A spectrograph disperses light scattered from arterial tissues over a liquid-nitrogen cooled CCD to detect the Raman spectra. A total of 111 spectra from arterial fragments were utilized.

Municipal solid waste management system: decision support through systems analysis

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

Comparing ICA-based and single-trial topographic ERP analyses.

Single-trial analysis of human electroencephalography (EEG) has been recently proposed for better understanding the contribution of individual subjects to a group-analysis effect as well as for investigating single-subject mechanisms. Independent Component Analysis (ICA) has been repeatedly applied to concatenated single-trial responses and at a single-subject level in order to extract those components that resemble activities of interest. More recently we have proposed a single-trial method based on topographic maps that determines which voltage configurations are reliably observed at the event-related potential (ERP) level taking advantage of repetitions across trials. Here, we investigated the correspondence between the maps obtained by ICA versus the topographies that we obtained by the single-trial clustering algorithm that best explained the variance of the ERP. To do this, we used exemplar data provided from the EEGLAB website that are based on a dataset from a visual target detection task. We show there to be robust correspondence both at the level of the activation time courses and at the level of voltage configurations of a subset of relevant maps. We additionally show the estimated inverse solution (based on low-resolution electromagnetic tomography) of two corresponding maps occurring at approximately 300 ms post-stimulus onset, as estimated by the two aforementioned approaches. The spatial distribution of the estimated sources significantly correlated and had in common a right parietal activation within Brodmann's Area (BA) 40. Despite their differences in terms of theoretical bases, the consistency between the results of these two approaches shows that their underlying assumptions are indeed compatible.

A non-linear VAD for noisy environments

This paper deals with non-linear transformations for improving the performance of an entropy-based voice activity detector (VAD). The idea to use a non-linear transformation has already been applied in the field of speech linear prediction, or linear predictive coding (LPC), based on source separation techniques, where a score function is added to classical equations in order to take into account the true distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if the signal is clean, the estimated entropy is essentially the same; if the signal is noisy, however, the frames transformed using the score function may give entropy that is different in voiced frames as compared to nonvoiced ones. Experimental evidence is given to show that this fact enables voice activity detection under high noise, where the simple entropy method fails.

Post-Nonlinear Mixtures and Beyond

Although sources in general nonlinear mixturm arc not separable iising only statistical independence, a special and realistic case of nonlinear mixtnres, the post nonlinear (PNL) mixture is separable choosing a suited separating system. Then, a natural approach is based on the estimation of tho separating Bystem parameters by minimizing an indcpendence criterion, like estimated mwce mutual information. This class of methods requires higher (than 2) order statistics, and cannot separate Gaarsian sources. However, use of [weak) prior, like source temporal correlation or nonstationarity, leads to other source separation Jgw rithms, which are able to separate Gaussian sourra, and can even, for a few of them, works with second-order statistics. Recently, modeling time correlated s011rces by Markov models, we propose vcry efficient algorithms hmed on minimization of the conditional mutual information. Currently, using the prior of temporally correlated sources, we investigate the fesihility of inverting PNL mixtures with non-bijectiw non-liacarities, like quadratic functions. In this paper, we review the main ICA and BSS results for riunlinear mixtures, present PNL models and algorithms, and finish with advanced resutts using temporally correlated snu~sm

Estimación de funciones no lineales en mezclas post-no lineales

This paper proposes a new method for blindly inverting a nonlinear mapping which transforms a sum of random variables. This is the case of post-nonlinear (PNL) source separation mixtures. The importance of the method is based on the fact that it permits to decouple the estimation of the nonlinear part from the estimation of the linear one. Only the nonlinear part is inverted, without considering on the linear part. Hence the initial problem is transformed into a linear one that can then be solved with any convenient linear algorithm. The method is compared with other existing algorithms for blindly approximating nonlinear mappings. Experiments show that the proposed algorithm outperforms the results obtained with other algorithms and give a reasonably good linearized data

Exploring Non-linear Transformations for an Entropybased Voice Activity Detector

In this paper we explore the use of non-linear transformations in order to improve the performance of an entropy based voice activity detector (VAD). The idea of using a non-linear transformation comes from some previous work done in speech linear prediction (LPC) field based in source separation techniques, where the score function was added into the classical equations in order to take into account the real distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if signal is clean, estimated entropy is essentially the same; but if signal is noisy transformed frames (with score function) are able to give different entropy if the frame is voiced against unvoiced ones. Experimental results show that this fact permits to detect voice activity under high noise, where simple entropy method fails.

Jätehuollon tilannekuva puolustushallinnossa

Diplomityön tavoitteena oli tuottaa jätehuollon tilannekuva Suomen puolustushallinnolle. Diplomityön ensisijaiseksi tutkimustavoitteeksi määriteltiin jätehuollon nykytilan kartoitus vuonna 2013. Lisäksi diplomityössä tarkasteltiin jätehuollon suunnittelutyön merkitystä puolustushallinnolle sekä kartoitettiin lähitulevaisuuden painopistealueita puolustushallinnon jätehuollossa. Sisätilojen jätehuoltojärjestelyt eivät noudata samaa linjaa ulkojätepisteiden jäte-huoltojärjestelyjen kanssa. Tämä asettaa haasteita syntypaikkalajittelun toteuttamiselle ja edelleen jätehuoltotavoitteiden saavuttamiselle. Tähän jätehuollon epäkohtaan on mahdollista vaikuttaa jätehuollon prosessien selkeyttämisellä ja sopimusten tarkastelemisella yhdessä vuonna 2015 käynnistyvän puolustusvoimien Logistiikkalaitoksen kanssa. Laskennallisen vaikuttavuusanalyysin perusteella jätehuollon tehostamistoimilla on mahdollista saavuttaa myös kustannussäästöjä. Ulkojätepisteiden tehostamisella olisi mahdollista saavuttaa noin 25 % säästöt jätehuollon kustannuksissa. Tehokkaammalla syntypaikkalajittelulla olisi mahdollista saavuttaa noin 27–31 % säästöt jätteen käsittelymaksuissa. Jätehuoltotavoitteiden saavuttamisessa keskeinen rooli on myös jätehuollon tiedonhallinnalla ja raportoinnilla sekä jätehuollon käyttäjät tavoittavalla tiedotuksella. Jätehuollon suunnittelu- ja kehittämistyössä tulee pyrkiä käyttäjäläheisempään toteutukseen. Diplomityön pohjalta aloitetaan puolustushallinnon jätehuollon pitkän aikavälin toiminnallisen strategian suunnittelu. Pitkän aikavälin toiminnalliseen strategiaan sisällytetään uudet jätehuollon tavoitteet, joissa huomioidaan puolustushallinnon sisäisten tavoitteiden lisäksi myös valtakunnallinen jätesuunnittelu.

Yhdyskuntajätteen mekaanisen erottelun erotustehokkuus

EU:n jätehierarkia asettaa jätteenkäsittelyssä materiaalien hyötykäytön energiahyötykäytön edelle. EU on asettanut korkeat tavoitteet jätteenkierrätykseen, 50 painoprosenttia kotitalousjätteestä on ohjattava kierrätykseen vuoteen 2020 mennessä. Suomessa kaatopaikoista on pyritty eroon lisäämällä jätteenpolttokapasiteettia. Jätteiden hyödyntämisen osalta tilanne Suomessa on hyvä, mutta kierrätystavoitteiden täyttyminen nykyisillä toimilla vaikuttaa epätodennäköiseltä. Tässä työssä selvitetään mitä mekaanisia jätteen erottelumenetelmiä maailmalla on käytössä ja kuinka tehokkaita ne ovat. Työn tavoitteena on tutkia voitaisiinko kierrätystä Suomessa tehostaa yhdyskuntajätteen mekaanisella käsittelyllä. Kirjallisuusselvityksen lisäksi työssä on simuloitu mekaanisia erotteluketjuja ja verrattu niillä saatuja tuloksia Suomen syntypaikkalajittelun tasoon. Tämän tutkimuksen perusteella, mikään yksittäinen mekaaninen erottelumenetelmä ei riittävän tehokas erottelemaan kierrätettäviä materiaaleja yhdyskuntajätteestä. Mekaanisia erottelumenetelmiä tulee yhdistää lajittelulinjastoiksi, joiden optimoiminen on monen tekijän summa. Lajittelulinjaston suunnitteluun vaikuttavat muun muassa lähtömateriaalin laatu ja lopputuotteiden käyttötarkoitukset. Yhdyskuntajätteen sisältämä biojäte likaa herkästi muut jätteet ja vaikeuttaa mekaanisesti eroteltujen jätejakeiden uudelleenkäyttöä. Biojätteen poistaminen muiden jätteiden joukosta olisi ensiarvoisen tärkeää mekaanisen erotuksen tehokkuuden kannalta. Mekaaniset erotteluketjut poistavat tehokkaasti biojätettä ja metalleja, mutta lasin ja kuitujen osalta erotusketjujen tehokkuudet jäävät alhaisiksi. Muovien osalta mekaaninen erottelu voi parhaimmillaan ollaan erittäin tehokasta, toisaalta vaatimukset lähtömateriaalin laadulle ovat suuret. Muovien osalta syntypaikkalajittelun ja mekaanisen erottelun yhtäaikainen tehostaminen voisi tarjota ratkaisun kierrätysasteen nostamiseen.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.

Automatic artefact removal from event-related potentials via clustering

This paper outlines a method for automatic artefact removal from multichannel recordings of event-related potentials (ERPs). The proposed method is based on, firstly, separation of the ERP recordings into independent components using the method of temporal decorrelation source separation (TDSEP). Secondly, the novel lagged auto-mutual information clustering (LAMIC) algorithm is used to cluster the estimated components, together with ocular reference signals, into clusters corresponding to cerebral and non-cerebral activity. Thirdly, the components in the cluster which contains the ocular reference signals are discarded. The remaining components are then recombined to reconstruct the clean ERPs.

Hushållsavfall i Borlänge kommun : analyser av material en och flerfamiljshus

In Borlänge, source separation has been the basis for management of household waste for over five years. This report reviews today?s system and gives a model for further follow-up through waste grouping. In the basic system waste is separated into three fractions: biodegradable, waste to energy and waste to landfill. All waste is packed in plastic bags, put in separate containers for each fraction, and collected from the property. Separate analyses were made of waste from single family houses and apartment buildings. The amount of waste per household and week, number of non-sorted bags, purity, recovery rate and density of each fraction was calculated. The amount of packaging collected together with the household waste is given. Material collected under the Swedish law of Producers? Responsibility is not covered in this report.

Chaos-based communication systems in non-ideal channels

Recently, many chaos-based communication systems have been proposed. They can present the many interesting properties of spread spectrum modulations. Besides, they can represent a low-cost increase in security. However, their major drawback is to have a Bit Error Rate (BER) general performance worse than their conventional counterparts. In this paper, we review some innovative techniques that can be used to make chaos-based communication systems attain lower levels of BER in non-ideal environments. In particular, we succinctly describe techniques to counter the effects of finite bandwidth, additive noise and delay in the communication channel. Although much research is necessary for chaos-based communication competing with conventional techniques, the presented results are auspicious. (C) 2011 Elsevier B. V. All rights reserved.