Non-intrusive Head Movement Analysis of Videotaped Seizures of Epileptic Origin

Abstract — In this work we propose a non-intrusive video analytic system for patient’s body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head/face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients’ heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally nonintrusive. The user initializes the person-specific skin color and selects few face/head poses in the initial few frames. The system then tracks the head/face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection. I

Spatial Coordinate Trial : Converting Non-Spatial Data Dimension for DBSCAN

In big data, noise in data mining is a necessity. Its existence depends on data and algorithm, but it does not mean the algorithm caused noise. Although the advantages of the Density Based Spatial Clustering Application with Noise, DBSCAN algorithm, in executing spatial data (two-dimensional data) have been widely discussed, but it has not been convincing in executing non-spatial data. As an algorithm should perform well on any data for optimizing data mining, this research proposes a trial to convert dimensions of non-spatial data into 2 dimensions for executing with DBSCAN algorithm, and a different input value for epsilon to know about its minimum which begins arising noise in the execution. Method of analysis in trial is with considering the attributes of non-spatial data as variables that represent coordinate points, rather than cardinality. Technically, it is assumed that 2-dimensional coordinate axes as a spot point for coordinate with more than or equal 3 dimensions according to development of Cartesian coordinate system, by first paying attention to relationship of variables (attributes). This way is then called Spatial Coordinate. The different input values are with paying attention to numbers from non-zero minimum distance to the forth of epsilon where the epsilon is in integer. The results of trial and testing on clusters formed, with Silhouette Coefficient, point out that the clusters are well, strong, and quality enough. Therefore, this research gives a new way on how preprocessing non-spatial data for DBSCAN algorithm performance

Alimerkkijonot suomen sanojen vektoriesitysten tuottamisessa neuroverkoilla

Sanojen vektoriesityksiä käytetään moniin luonnollista kieltä käsitteleviin koneoppimistehtäviin, kuten luokitteluun, tiedonhakuun ja konekääntämiseen. Ne ilmaisevat sanat tietokoneelle ymmärrettävässä muodossa. Erityisen hyödyllinen tapa esittää sanat vektoreina on esittää sanat pisteinä jatkuvassa sana-avaruudessa, jolla on joitakin satoja ulottuvuuksia. Tällaisessa mallissa samankaltaiset sanat sijaitsevat avaruudessa lähekkäin, ja sanavektorien erotukset kuvaavat sana-analogiasuhteita, jos vektorit on tuotettu siihen tarkoitukseen luodulla neuroverkolla. Pelkästään tällaisia vektoreita katsomalla saadaan tietää jotakin sanan merkityksestä ja muodosta. Perinteisesti sanavektoreita opettaessa on käsitelty opetusaineiston sanat erillisinä merkkijonoina. Englannin kielessä tämä on usein toimiva menetelmä. Suomen kieli taas on vahvasti taivuttava, joten myös sananmuodot sisältävät paljon informaatiota. Osa informaatiosta menee hukkaan, jos sanat opetetaan kokonaan erillisinä. Lisäksi malli ei osaa yhdistää kahta saman sanan sanamuotoa toisiinsa. FastText-mallit ratkaisevat taivuttamisen ja johtamisen tuomat ongelmat hyödyntämällä tietoa sanojen sisältämistä alimerkkijonoista. Vektoriesitysmalli opetetaan siis paitsi sanojen, myös niiden sisältämien lyhyempien merkkijonojen perusteella. Tämän takia fastText-mallin voisi ajatella toimivan hyvin paljon taivuttavilla kielillä, kuten suomella. Tässä tutkielmassa on haluttu selvittää, toimiiko fastText-menetelmä hyvin suomen kielellä. Lisäksi on tutkittu, millä parametreilla malli toimii parhaiten. Tutkielmassa on kokeiltu erilaisia alimerkkijonojen pituuksia ja sanavektorin kokoja. Mallin laatua voidaan testata semanttista samankaltaisuutta mittaavilla aineistoilla sekä sana-analogiakyselyillä. Semanttista samankaltaisuutta mittaavissa testeissä tutkitaan, ovatko samaa tarkoittavat sanat lähekkäin vektoriavaruudessa. Aineistot pohjautuvat ihmisarvioijien antamiin pisteytyksiin sanojen samankaltaisuudesta. Sana-analogiatesteissä kokeillaan, onnistuuko malli löytämään analogiaparista puuttuvan sanan vektorilaskutoimituksen perusteella. Analogia-aineistot koostuvat sanapareista, jotka ovat tietyssä analogiasuhteessa keskenään. Analogiat voivat liittyä sanan merkitykseen, kuten ``mies ja nainen'' tai muotoon, kuten ``positiivi ja komparatiivi''. Tutkielmaa varten käännettiin suomeksi kaksi englannin kielellä usein käytettyä datasettiä: semanttista samankaltaisuutta mittaava WS353 ja sana-analogioita sisältävä SSWR, jonka käännöksestä käytetään nimeä SSWR-fi. Käännöksissä huomioitiin se, että monet datasettien sanat eivät käänny suomeen yksikäsitteisesti. SSWR-fi-datasetistä ongelmalliset sanat poistettiin, WS353-datasetin rinnalle taas tehtiin erillinen lyhennetty datasetti WS277-josta ongelmalliset sanat on poistettu. Tutkielmassa havaittiin, että alimerkkijonojen käyttäminen on hyödyllistä suomen kielen käsittelyssä. Semanttista samankaltaisuutta mittaavien testien mukaan mallin laatu parani alimerkkijonojen ansiosta. Sana-analogiatesteissä alimerkkijonojen käyttäminen paransi muotokyselyissä onnistumista, mutta huononsi merkityskyselyissä onnistumista. Tämä johtunee siitä, että muotokyselyt perustuvat sanojen taivuttamiselle ja johtamiselle, mutta merkityskyselyissä sananmuodoilla ei ole juuri väliä

Speech Activity and Speaker Change Point Detection for Online Streams

Disertační práce je věnována dvěma si blízkým řečovým úlohám a následně jejich použití v online prostředí. Konkrétně se jedná o úlohy detekce řeči a detekce změny mluvčího. Ty jsou často nedílnou součástí systémů pro zpracování řeči (např. pro diarizaci mluvčích nebo rozpoznávání řeči), kde slouží pro předzpracování akustického signálu. Obě úlohy jsou v literatuře velmi aktivním tématem, ale většina existujících prací je směřována primárně na offline využití. Nicméně právě online nasazení je nezbytné pro některé řečové aplikace, které musí fungovat v reálném čase (např. monitorovací systémy).Úvodní část disertační práce je tvořena třemi kapitolami. V té první jsou vysvětleny základní pojmy a následně je nastíněno využití obou úloh. Druhá kapitola je věnována současnému poznání a je doplněna o přehled existujících nástrojů. Poslední kapitola se skládá z motivace a z praktického použití zmíněných úloh v monitorovacích systémech. V závěru úvodní části jsou stanoveny cíle práce.Následující dvě kapitoly jsou věnovány teoretickým základům obou úloh. Představují vybrané přístupy, které jsou buď relevantní pro disertační práci (porovnání výsledků), nebo jsou zaměřené na použití v online prostředí.V další kapitole je předložen finální přístup pro detekci řeči. Postupný návrh tohoto přístupu, společně s experimentálním vyhodnocením, je zde detailně rozebrán. Přístup dosahuje nejlepších výsledků na korpusu QUT-NOISE-TIMIT v podmínkách s nízkým a středním zašuměním. Přístup je také začleněn do monitorovacího systému, kde doplňuje svojí funkcionalitou rozpoznávač řeči.Následující kapitola detailně představuje finální přístup pro detekci změny mluvčího. Ten byl navržen v rámci několika po sobě jdoucích experimentů, které tato kapitola také přibližuje. Výsledky získané na databázi COST278 se blíží výsledkům, kterých dosáhl referenční offline systém, ale předložený přístup jich docílil v online módu a to s nízkou latencí.Výstupy disertační práce jsou shrnuty v závěrečné kapitole.The main focus of this thesis lies on two closely interrelated tasks, speech activity detection and speaker change point detection, and their applications in online processing. These tasks commonly play a crucial role of speech preprocessors utilized in speech-processing applications, such as automatic speech recognition or speaker diarization. While their use in offline systems is extensively covered in literature, the number of published works focusing on online use is limited.This is unfortunate, as many speech-processing applications (e.g., monitoring systems) are required to be run in real time.The thesis begins with a three-chapter opening part, where the first introductory chapter explains the basic concepts and outlines the practical use of both tasks. It is followed by a chapter, which reviews the current state of the art and lists the existing toolkits. That part is concluded by a chapter explaining the motivation behind this work and the practical use in monitoring systems; ultimately, this chapter sets the main goals of this thesis.The next two chapters cover the theoretical background of both tasks. They present selected approaches relevant to this work (e.g., used for result comparisons) or focused on online processing.The following chapter proposes the final speech activity detection approach for online use. Within this chapter, a detailed description of the development of this approach is available as well as its thorough experimental evaluation. This approach yields state-of-the-art results under low- and medium-noise conditions on the standardized QUT-NOISE-TIMIT corpus. It is also integrated into a monitoring system, where it supplements a speech recognition system.The final speaker change point detection approach is proposed in the following chapter. It was designed in a series of consecutive experiments, which are extensively detailed in this chapter. An experimental evaluation of this approach on the COST278 database shows the performance of approaching the offline reference system while operating in online mode with low latency.Finally, the last chapter summarizes all the results of this thesis

Signal concentration and related concepts in time-frequency and on the unit sphere

Unit sphere signal processing is an increasingly active area of research with applications in computer vision, medical imaging, geophysics, cosmology and wireless communications. However, comparing with signal processing in time-frequency domain, characterization and processing of signals defined on the unit sphere is relatively unfamiliar for most of the engineering researchers. In order to better understand and analysis the current issues using the spherical model, such as analysis of brain neural electronic activities in medical imaging and neuroscience, target detection and tracking in radar systems, earthquake occurrence prediction and seismic origin detection in seismology, it is necessary to set up a systematic theory for unit sphere signal processing. How to efficiently analyze and represent functions defined on the unit sphere are central for the unit sphere signal processing, such as filtering, smoothing, detection and estimation in the presence of noise and interference. Slepian-Landau-Pollak time-frequency energy concentration theory and the essential dimensionality of time-frequency signals by the Fourier transform are the fundamental tools for signal processing in the time-frequency domain. Therefore, our research work starts from the analogies of signals between time-frequency and spatial-spectral. In this thesis, we first formulate the k-th moment time-duration weighting measure for a band-limited signal using a general constrained variational method, where a complete, orthonormal set of optimal band-limited functions with the minimum fourth moment time-duration measure is obtained and the prospective applications are discussed. Further, the formulation to an arbitrary signal with second and fourth moment weighting in both time and frequency domain is also developed and the corresponding optimal functions are obtained, which are helpful for practical waveform designs in communication systems. Next, we develop a k-th spatially global moment azimuthal measure (GMZM) and a k-th spatially local moment zenithal measure (LMZM) for real-valued spectral-limited signals. The corresponding sets of optimal functions are solved and compared with the spherical Slepian functions. In addition, a harmonic multiplication operation is developed on the unit sphere. Using this operation, a spectral moment weighting measure to a spatial-limited signal is formulated and the corresponding optimal functions are solved. However, the performance of these sets of functions and their perspective applications in real world, such as efficiently analysis and representation of spherical signals, is still in exploration. Some spherical quadratic functionals by spherical harmonic multiplication operation are formulated in this thesis. Next, a general quadratic variational framework for signal design on the unit sphere is developed. Using this framework and the quadratic functionals, the general concentration problem to an arbitrary signal defined on the unit sphere to simultaneously achieve maximum energy in the finite spatial region and finite spherical spectrum is solved. Finally, a novel spherical convolution by defining a linear operator is proposed, which not only specializes the isotropic convolution, but also has a well defined spherical harmonic characterization. Furthermore, using the harmonic multiplication operation on the unit sphere, a reconstruction strategy without consideration of noise using analysis-synthesis filters under three different sampling methods is discussed

On Statistical Modelling and Hypothesis Testing by Information Theoretic Methods

The main objective of this thesis is to study various information theoretic methods and criteria in the context of statistical model selection. The focus in this research is on Rissanen’s Minimum Description Length (MDL) principle and its variants, with a special emphasis on the Normalized Maximum Likelihood (NML).We extend the Rissanen methodology for coping with infinite parametric complexity and discuss two particular cases. This is applied for deriving four NMLcriteria and investigate their performance. Furthermore, we find the connection between Stochastic Complexity (SC), defined as minus logarithm of NML, and other model selection criteria.We also study the use of information theoretic criteria (ITC) for selecting the order of autoregressive (AR) models in the presence of nonstationarity. In particular, we give a modified version of Sequentially NML (SNML) when the model parameters are estimated by forgetting factor LS algorithm.Another contribution of the thesis is in connection with the new approach for composite hypothesis testing using Optimally Distinguishable Distributions (ODD). The ODD-detector for subspace signals in Gaussian noise is introduced and its performance is evaluated.Additionally, we exploit the Kolmogorov Structure Function (KSF) to derive a new criterion for cepstral nulling, which has been recently applied to the problem of periodogram smoothing.Finally, the problem of fairness in multiaccess communication systems is investigated and a new method is proposed. The new approach is based on partitioning the network into subnetworks and employing two different multiple-access schemes within and across subnetworks. It is also introduced an algorithm for selecting optimally the subnetworks such that to achieve the max-min fairness

Cooperative diversity techniques for high-throughput wireless relay networks

Relay communications has attracted a growing interest in wireless communications with application to various enhanced technologies. This thesis considers a number of issues related to data throughput in various wireless relay network models. Particularly, new implementations of network coding (NC) and space-time coding (STC) techniques are investigated to offer various means of achieving high-throughput relay communications. Firstly, this thesis investigates different practical automatic repeat request (ARQ) retransmission protocols based on NC for two-way wireless relay networks to improve throughput efficiency. Two improved NC-based ARQ schemes are designed based on go-back-N and selective-repeat (SR) protocols. Addressing ARQ issues in multisource multidestination relay networks, a new NC-based ARQ protocol is proposed and two packet-combination algorithms are developed for retransmissions at relay and sources to significantly improve the throughput. In relation to the concept of channel quality indicator (CQI) reporting in two-way relay networks, two new efficient CQI reporting schemes are designed based on NC to improve the system throughput by allowing two terminals to simultaneously estimate the CQI of the distant terminal-relay link without incurring additional overhead. The transmission time for CQI feedback at the relays is reduced by half while the increase in complexity and the loss of performance are shown to be negligible. Furthermore, a low-complexity relay selection scheme is suggested to reduce the relay searching complexity. For the acknowledgment (ACK) process, this thesis proposes a new block ACK scheme based on NC to significantly reduce the ACK overheads and therefore produce an enhanced throughput. The proposed scheme is also shown to improve the reliability of block ACK transmission and reduce the number of data retransmissions for a higher system throughput. Additionally, this thesis presents a new cooperative retransmission scheme based on relay cooperation and NC to considerably reduce the number of retransmission packets and im- prove the reliability of retransmissions for a more power efficient and higher throughput system with non-overlapped retransmissions. Moreover, two relay selection schemes are recommended to determine the optimised number of relays for the retransmission. Finally, with respect to cognitive wireless relay networks (CWRNs), this thesis proposes a new cooperative spectrum sensing (CSS) scheme to improve the spectrum sensing performance and design a new CSS scheme based on NC for three-hop CWRNs to improve system throughput. Furthermore, a new distributed space-time-frequency block code (DSTFBC) is designed for a two- hop nonregenerative CWRN over frequency-selective fading channels. The proposed DSTFBC design achieves higher data rate, spatial diversity gain, and decoupling detection of data blocks at all destination nodes with a low-complexity receiver structure

Modélisation stochastique pour l'analyse d'images texturées (approches Bayésiennes pour la caractérisation dans le domaine des transformées)

Le travail présenté dans cette thèse s inscrit dans le cadre de la modélisation d images texturées à l aide des représentations multi-échelles et multi-orientations. Partant des résultats d études en neurosciences assimilant le mécanisme de la perception humaine à un schéma sélectif spatio-fréquentiel, nous proposons de caractériser les images texturées par des modèles probabilistes associés aux coefficients des sous-bandes. Nos contributions dans ce contexte concernent dans un premier temps la proposition de différents modèles probabilistes permettant de prendre en compte le caractère leptokurtique ainsi que l éventuelle asymétrie des distributions marginales associées à un contenu texturée. Premièrement, afin de modéliser analytiquement les statistiques marginales des sous-bandes, nous introduisons le modèle Gaussien généralisé asymétrique. Deuxièmement, nous proposons deux familles de modèles multivariés afin de prendre en compte les dépendances entre coefficients des sous-bandes. La première famille regroupe les processus à invariance sphérique pour laquelle nous montrons qu il est pertinent d associer une distribution caractéristique de type Weibull. Concernant la seconde famille, il s agit des lois multivariées à copules. Après détermination de la copule caractérisant la structure de la dépendance adaptée à la texture, nous proposons une extension multivariée de la distribution Gaussienne généralisée asymétrique à l aide de la copule Gaussienne. L ensemble des modèles proposés est comparé quantitativement en terme de qualité d ajustement à l aide de tests statistiques d adéquation dans un cadre univarié et multivarié. Enfin, une dernière partie de notre étude concerne la validation expérimentale des performances de nos modèles à travers une application de recherche d images par le contenu textural. Pour ce faire, nous dérivons des expressions analytiques de métriques probabilistes mesurant la similarité entre les modèles introduits, ce qui constitue selon nous une troisième contribution de ce travail. Finalement, une étude comparative est menée visant à confronter les modèles probabilistes proposés à ceux de l état de l art.In this thesis we study the statistical modeling of textured images using multi-scale and multi-orientation representations. Based on the results of studies in neuroscience assimilating the human perception mechanism to a selective spatial frequency scheme, we propose to characterize textures by probabilistic models of subband coefficients.Our contributions in this context consist firstly in the proposition of probabilistic models taking into account the leptokurtic nature and the asymmetry of the marginal distributions associated with a textured content. First, to model analytically the marginal statistics of subbands, we introduce the asymmetric generalized Gaussian model. Second, we propose two families of multivariate models to take into account the dependencies between subbands coefficients. The first family includes the spherically invariant processes that we characterize using Weibull distribution. The second family is this of copula based multivariate models. After determination of the copula characterizing the dependence structure adapted to the texture, we propose a multivariate extension of the asymmetric generalized Gaussian distribution using Gaussian copula. All proposed models are compared quantitatively using both univariate and multivariate statistical goodness of fit tests. Finally, the last part of our study concerns the experimental validation of the performance of proposed models through texture based image retrieval. To do this, we derive closed-form metrics measuring the similarity between probabilistic models introduced, which we believe is the third contribution of this work. A comparative study is conducted to compare the proposed probabilistic models to those of the state-of-the-art.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF