181 research outputs found

    Two-dimensional spectrum estimation using the radon transform

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    An alternative approach to two-dimensional power spectrum estimation incorporating the Radon transform in conjunction with each of the one-dimensional periodogram, Blackman-Tukey, and Autoregressive parameter estimation algorithms is examined. The Radon transform is used to express a two-dimensional data set in terms of its projections onto a set of one-dimensional radial lines, effectively reducing the two-dimensional estimation problem to a series of one-dimensional problems. The resulting two-dimensional power spectrum estimates are compared to the known power spectra for a variety of data types. The Radon transform approach combined with autoregressive parameter estimation can provide a high-resolution power spectrum estimate, effectively surpassing the resolution limitations of the Fourier methods without the cumbersome implementations of the more direct high resolution estimation methods in two dimensions

    Label-efficient Time Series Representation Learning: A Review

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    The scarcity of labeled data is one of the main challenges of applying deep learning models on time series data in the real world. Therefore, several approaches, e.g., transfer learning, self-supervised learning, and semi-supervised learning, have been recently developed to promote the learning capability of deep learning models from the limited time series labels. In this survey, for the first time, we provide a novel taxonomy to categorize existing approaches that address the scarcity of labeled data problem in time series data based on their dependency on external data sources. Moreover, we present a review of the recent advances in each approach and conclude the limitations of the current works and provide future directions that could yield better progress in the field.Comment: Under Revie

    Investigations on the properties and estimation of earth response operators from EM sounding data

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    Incl. 3 reprints at backAvailable from British Library Document Supply Centre- DSC:D82993 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

    Linear Predictive Spectral Analysis via the Lp Norm

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    This study involves linear predictive spectral analysis under the general LP norm; both one dimensional and two dimensional spectral estimation algorithms are developed. The objective in this study is determination of frequency resolution capability for various LP normed solutions to linear predictive spectral estimation equations. A modified residual steepest descent algorithm is utilized to generate the required solution. The research presented in this thesis could not have been accomplished without the support of the Oklahoma State University Research Consortium For Well Log Data Enhancement Via Signal Processing. The member companies of this consortium include Amococ Production Company, Area Oil and Gas Company, Cities Service Oil and Gas Corporation, Conoco, Exxon, IBM, Mobil Research and Development, Phillips Petroleum Corporation, Sohio Petroleum Company, and Texaco.Electrical Engineerin

    A new recursive high-resolution parametric method for power spectral density estimation

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    Thesis (M.Eng.Sc.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 199

    Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings

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    L’épilepsie est une des maladies neurologiques les plus frĂ©quentes, touchant prĂšs d’un pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients Ă©pileptiques rĂ©pondent adĂ©quatement aux traitements pharmacologiques, il reste qu’un tiers des patients doivent vivre avec des crises invalidantes et imprĂ©visibles. Quoique la chirurgie d’épilepsie puisse ĂȘtre une autre option thĂ©rapeutique envisageable, le recours Ă  la chirurgie de rĂ©section demeure trĂšs faible en partie pour des raisons diverses (taux de rĂ©ussite modeste, peur des complications, perceptions nĂ©gatives). D’autres avenues de traitement sont donc souhaitables. Une piste actuellement explorĂ©e par des groupes de chercheurs est de tenter de prĂ©dire les crises Ă  partir d’enregistrements de l’activitĂ© cĂ©rĂ©brale des patients. La capacitĂ© de prĂ©dire la survenue de crises permettrait notamment aux patients, aidants naturels ou personnels mĂ©dical de prendre des mesures de prĂ©caution pour Ă©viter les dĂ©sagrĂ©ments reliĂ©s aux crises voire mĂȘme instaurer un traitement pour les faire avorter. Au cours des derniĂšres annĂ©es, d’importants efforts ont Ă©tĂ© dĂ©ployĂ©s pour dĂ©velopper des algorithmes de prĂ©diction de crises et d’en amĂ©liorer les performances. Toutefois, le manque d’enregistrements Ă©lectroencĂ©phalographiques intracrĂąniens (iEEG) de longue durĂ©e de qualitĂ©, la quantitĂ© limitĂ©e de crises, ainsi que la courte durĂ©e des pĂ©riodes interictales constituaient des obstacles majeurs Ă  une Ă©valuation adĂ©quate de la performance des algorithmes de prĂ©diction de crises. RĂ©cemment, la disponibilitĂ© en ligne d’enregistrements iEEG continus avec Ă©chantillonnage bilatĂ©ral (des deux hĂ©misphĂšres) acquis chez des chiens atteints d’épilepsie focale Ă  l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a partiellement facilitĂ© cette tĂąche. Cependant, une des limitations associĂ©es Ă  l’utilisation de ces donnĂ©es durant la conception d’un algorithme de prĂ©diction de crises Ă©tait l’absence d’information concernant la zone exacte de dĂ©but des crises (information non fournie par les gestionnaires de cette base de donnĂ©es en ligne). Le premier objectif de cette thĂšse Ă©tait la mise en oeuvre d’un algorithme prĂ©cis de prĂ©diction de crises basĂ© sur des enregistrements iEEG canins de longue durĂ©e. Les principales contributions Ă  cet Ă©gard incluent une localisation quantitative de la zone d’apparition des crises (basĂ©e sur la fonction de transfert dirigĂ© –DTF), l’utilisation d’une nouvelle fonction de coĂ»t via l’algorithme gĂ©nĂ©tique proposĂ©, ainsi qu’une Ă©valuation quasi-prospective des performances de prĂ©diction (donnĂ©es de test d’un total de 893 jours). Les rĂ©sultats ont montrĂ© une amĂ©lioration des performances de prĂ©diction par rapport aux Ă©tudes antĂ©rieures, atteignant une sensibilitĂ© moyenne de 84.82 % et un temps en avertissement de 10 %. La DTF, utilisĂ©e prĂ©cĂ©demment comme mesure de connectivitĂ© pour dĂ©terminer le rĂ©seau Ă©pileptique (objectif 1), a Ă©tĂ© prĂ©alablement validĂ©e pour quantifier les relations causales entre les canaux lorsque les exigences de quasi-stationnaritĂ© sont satisfaites. Ceci est possible dans le cas des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face aux exigences de non-stationnaritĂ©, la fonction de transfert adaptatif pondĂ©rĂ©e par le spectre (Spectrum weighted adaptive directed transfer function - swADTF) a Ă©tĂ© introduit en tant qu’une version variant dans le temps de la DTF. Le second objectif de cette thĂšse Ă©tait de valider la possibilitĂ© d’identifier les endroits Ă©metteurs (ou sources) et rĂ©cepteurs d’activitĂ© Ă©pileptiques en appliquant la swADTF sur des enregistrements iEEG de haute densitĂ© provenant de patients admis pour Ă©valuation prĂ©-chirurgicale au CHUM. Les gĂ©nĂ©rateurs d’activitĂ© Ă©pileptique Ă©taient dans le volume rĂ©sĂ©quĂ© pour les patients ayant des bons rĂ©sultats post-chirurgicaux alors que diffĂ©rents foyers ont Ă©tĂ© identifiĂ©s chez les patients ayant eu de mauvais rĂ©sultats postchirurgicaux. Ces rĂ©sultats dĂ©montrent la possibilitĂ© d’une identification prĂ©cise des sources et rĂ©cepteurs d’activitĂ©s Ă©pileptiques au moyen de la swADTF ouvrant la porte Ă  la possibilitĂ© d’une meilleure sĂ©lection d’électrodes de maniĂšre quantitative dans un contexte de dĂ©veloppement d’algorithme de prĂ©diction de crises chez l’humain. Dans le but d’explorer de nouvelles avenues pour la prĂ©diction de crises Ă©pileptiques, un nouveau prĂ©curseur a aussi Ă©tĂ© Ă©tudiĂ© combinant l’analyse des spectres d’ordre supĂ©rieur et les rĂ©seaux de neurones artificiels (objectif 3). Les rĂ©sultats ont montrĂ© des diffĂ©rences statistiquement significatives (p<0.05) entre l’état prĂ©ictal et l’état interictal en utilisant chacune des caractĂ©ristiques extraites du bi-spectre. UtilisĂ©es comme entrĂ©es Ă  un perceptron multicouche, l’entropie bispectrale normalisĂ©e, l’entropie carrĂ© normalisĂ©e, et la moyenne ont atteint des prĂ©cisions respectives de 78.11 %, 72.64% et 73.26%. Les rĂ©sultats de cette thĂšse confirment la faisabilitĂ© de prĂ©diction de crises Ă  partir d’enregistrements d’électroencĂ©phalographie intracrĂąniens. Cependant, des efforts supplĂ©mentaires en termes de sĂ©lection d’électrodes, d’extraction de caractĂ©ristiques, d’utilisation des techniques d’apprentissage profond et d’implĂ©mentation Hardware, sont nĂ©cessaires avant l’intĂ©gration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT Epilepsy is a chronic condition characterized by recurrent “unpredictable” seizures. While the first line of treatment consists of long-term drug therapy about one-third of patients are said to be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to persisting negative attitudes towards resective surgery, fear of complications and only moderate success rates. An important direction of research is to investigate the possibility of predicting seizures which, if achieved, can lead to novel interventional avenues. The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of ictal events, and the short duration of interictal periods have been important obstacles for an adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable NeuroVista ambulatory monitoring device have been made available on line for the benefit of researchers. Still, an important limitation of these recordings for seizure-prediction studies was that the seizure onset zone was not disclosed/available. The first objective of this thesis was to develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG recordings. Main contributions include a quantitative, directed transfer function (DTF)-based, localization of the seizure onset zone (electrode selection), a new fitness function for the proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed performance improvement compared to previous studies, achieving an average sensitivity of 84.82% and a time in warning of 10 %. The DTF has been previously validated for quantifying causal relations when quasistationarity requirements are met. Although such requirements can be fulfilled in the case of canine recordings due to the relatively low number of channels (objective 1), the identification of stationary segments would be more challenging in the case of high density iEEG recordings. To cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function (swADTF) was recently introduced as a time-varying version of the DTF. The second objective of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for patients with good post-surgical outcomes, whereas different or additional seizure foci were identified in patients with poor post-surgical outcomes. Results confirmed the possibility of accurate identification of seizure origin and propagation by means of swADTF paving the way for its use in seizure prediction algorithms by allowing a more tailored electrode selection. Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new precursor of seizure activity by combining higher order spectral analysis and artificial neural networks (objective 3). Results showed statistically significant differences (p<0.05) between preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and 73.26%, respectively. Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings; the transition into the ictal state is not random and consists of a “build-up”, leading to seizures. However, additional efforts in terms of electrode selection, feature extraction, hardware and deep learning implementation, are required before the translation of current approaches into commercial devices

    Empirical Analysis of Natural Gas Markets

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    Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide
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