在此篇論文中，我們提供二維非線性動態系統之非振盪解的一個分類法，此分類法是依據解的漸近值作分類，同時我們也得到具有此漸近值之非振盪解的存在性的充分必要條件。 / In this thesis, we provide a classification scheme for nonoscillatory solutions of a class of two-dimensional dynamical systems in terms of their asymptotic values. In addition, we find the sufficient and necessary conditions for the existence of these solutions.

Abstract This study fills an urgent need for qualitative analyses of relationships resulting in human change. It is a result of sixteen years of independent study by the author. It combines postgraduate study of nonlinear methodology, applied research of children’s pretend play, experience in educational psychology and Gestalt-counselling, as well as the practical training of graduate students at the Karelian State Pedagogical University (Petrozavodsk, Russia), and the Kajaani Department of Teacher Education (Kajaani, Finland). In this thesis, an attempt is made to reveal the fundamental reality of relationships between human beings. Using theories of helping relationships and data from developmental psychology, a qualitative nonlinear dynamical model of human relationships is elaborated. The scientific findings of Kurt Lewin and the Gestalt-therapy theory are widely used. To illustrate the explanatory potential of the proposed relationship model and the possibility of qualitative analyses, children’s pretend play is analyzed. In the first chapter, the basic connectedness between humans is studied. The author is focused on theories of relationships and their application to the organizing of relationships’ flow. The second chapter is devoted to detailed analyses of dynamic features of these theories and Kurt Lewin’s conception of tension system. The ontological philosophy of relationships is briefly reviewed. This helps to formulate the main problem of the research – how is a nonlinear phenomenological model of human relationships possible? In the third chapter, a new nonlinear dynamic model of human relationships is elaborated. Several conceptions from Lewin’s dynamic psychology and Gestalt-therapy are further developed in the model. A number of examples are analyzed. Video-data on children’s pretend play is analyzed in the fourth chapter. In the subsequent discussions some advantages and shortcomings of the suggested dynamic nonlinear model are examined. / Tiivistelmä Tämä tutkimus pyrkii vastaamaan kysymykseen miten inhimilliset suhteet voivat johtaa laadullisiin muutoksiin. Työssä paneudutaan ihmisten välisten suhteiden psykologisiin perusteisiin. Siinä kehitellään ihmisten välisten suhteiden ei-lineaarinen dynaaminen malli käyttäen kehityspsykologian ja auttamissuhteiden teorioita. Analyysi pohjautuu Kurt Lewinin ja hahmoterapian teoreettisiin oivalluksiin. Kehitellyn mallin selitysvoiman ja laadullisen analyysin mahdollisuuksien osoittamiseksi mallia sovelletaan lasten juonellisen roolileikin erittelyyn. Ensimmäisessä luvussa pohditaan esimerkkien avulla ihmisten välisten kontaktien perusluonnetta. Erityisesti keskitytään suhteiden teorioihin ja niiden sovelluksiin suhteiden jatkumon rakentamiseksi. Toinen luku paneutuu näiden teorioiden kuvaamien suhteiden dynaamisten piirteiden yksityiskohtaiseen tarkasteluun ja Kurt Lewinin ”tension system” käsitteeseen. Siinä esitellään myöskin lyhyesti suhteiden yksilökehityksen filosofiaa. Tältä pohjalta muotoillaan tutkimuksen pääongelma: Kuinka inhimillisten suhteiden ei-lineaarinen fenomenologinen malli on mahdollinen? Kolmannessa luvussa kehitellään uusi ei-lineaarinen inhimillisten suhteiden malli. Mallissa on kehitelty ja annettu uusi tulkinta useille Lewinin dynaamisen psykologian ja hahmoterapian käsitteille. Kehittelyä on tuettu käytännön esimerkein. Neljännessä luvussa on analysoitu lasten juonellisen roolileikin videotallenteita mallia käyttäen. Pohdinta tuo esille joitakin uuden mallin etuja ja jatkokehittelyn tarpeita.

In this dissertation, we address the problem of understanding human activities in videos by developing a two-pronged approach: coarse level modeling of scene activities and fine level modeling of individual activities. At the coarse level, where the resolution of the video is low, we rely on person tracks. At the fine level, richer features are available to identify different parts of the human body, therefore we rely on the body joint tracks. There are three main goals of this dissertation: (1) identify unusual activities at the coarse level, (2) recognize different activities at the fine level, and (3) predict the behavior for synthesizing and tracking activities at the fine level. The first goal is addressed by modeling activities at the coarse level through two novel and complementing approaches. The first approach learns the behavior of individuals by capturing the patterns of motion and size of objects in a compact model. Probability density function (pdf) at each pixel is modeled as a multivariate Gaussian Mixture Model (GMM), which is learnt using unsupervised expectation maximization (EM). In contrast, the second approach learns the interaction of object pairs concurrently present in the scene. This can be useful in detecting more complex activities than those modeled by the first approach. We use a 14-dimensional Kernel Density Estimation (KDE) that captures motion and size of concurrently tracked objects. The proposed models have been successfully used to automatically detect activities like unusual person drop-off and pickup, jaywalking, etc. The second and third goals of modeling human activities at the fine level are addressed by employing concepts from theory of chaos and non-linear dynamical systems. We show that the proposed model is useful for recognition and prediction of the underlying dynamics of human activities. We treat the trajectories of human body joints as the observed time series generated from an underlying dynamical system. The observed data is used to reconstruct a phase (or state) space of appropriate dimension by employing the delay-embedding technique. This transformation is performed without assuming an exact model of the underlying dynamics and provides a characteristic representation that will prove to be vital for recognition and prediction tasks. For recognition, properties of phase space are captured in terms of dynamical and metric invariants, which include the Lyapunov exponent, correlation integral, and correlation dimension. A composite feature vector containing these invariants represents the action and will be used for classification. For prediction, kernel regression is used in the phase space to compute predictions with a specified initial condition. This approach has the advantage of modeling dynamics without making any assumptions about the exact form (polynomial, radial basis, etc.) of the mapping function. We demonstrate the utility of these predictions for human activity synthesis and tracking.

La maladie de Newcastle (MN) grève lourdement les productions aviaires malgaches, essentielles à l'alimentation et à l'économie familiales. La MN est une dominante pathologique en l'absence de vaccination généralisée. L'objectif de cette thèse est la modélisation, la validation et l'analyse mathématique de modèles de transmission du virus de la MN (VMN) dans les systèmes avicoles villageois en général et à Madagascar en particulier. Nous proposons de nouveaux modèles basés sur les connaissances actuelles de l'histoire naturelle de la transmission du VMN. Ainsi, nous présentons deux modèles mathématiques à compartiments de la transmission du VMN dans une population de poules : un premier modèle avec transmission environnementale et un deuxième modèle où la vaccination contre la maladie est prise en compte. Nous présentons une analyse complète de la stabilité de ces modèles à l'aide des techniques de Lyapunov suivant la valeur du taux de reproduction de base R0. Le travail s'est appuyé sur des enquêtes de terrain pour comprendre les pratiques de vaccination actuelles à Madagascar. / Newcastle disease (ND) severely harms Malagasy bird productions, mainly uses to food and family economy. ND is a pathological dominant without general vaccination. The objective of this thesis is modelling the transmission of ND virus (NDV) in smallholder chicken farms in general and, Madagascar in particular. We propose new models based on the state of art and the epidemiology currently known from the transmission of the NDV. Thus, we present two models of the transmission of NDV: a first model with environmental transmission and a second model in which imperfect vaccination of chickens is considered. We present a thorough analysis of the stability of the models using the Lyapunov techniques and obtain the basic reproduction ratio R0. This work is based on field surveys to understand the current vaccination practices in Madagascar.

Les particules soumises à un écoulement peuvent manifester des orientations préférées et une variété de déformations en fonction de leur géométrie et élasticité et du champ de vitesse de l'écoulement. A l’inverse, les flux peuvent être modifiés lorsque les contraintes des particules sont non négligeables. Cette thèse présente des résultats théoriques et numériques sur cette relation bilatérale en deux parties. La Ière partie commence par une analyse de stabilité et des simulations numériques qui montrent une particule brownienne semi-flexible dans un écoulement élongationnel effectuant un retournement, un phénomène associé aux flux de cisaillement. Le Chap. 2 étend les outils analytiques dédiés aux modèles simples ou aux flux indépendante du temps pour les modèles perle-barre-bond généraux dans les flux aléatoires. En partant des résultats des chapitres précédents, Le Chap. 3 aboutit à l'étude d'un degré de liberté lagrangien inexploré dans un écoulement turbulent : la flexion. Une particule semi-flexible courbe différemment dans les flux aléatoires bidimensionnels et tridimensionnels. La IIème partie concerne la turbulence élastique, un régime chaotique créé dans un écoulement de faibles forces inertielles par l'ajout de polymères élastiques. Le Chap. 4 estime le nombre de degrés de liberté d'une solution de ce régime via la dimension Lyapunov de l'attracteur du modèle Oldroyd-B bidimensionnel, un modèle connu de reproduire la turbulence élastique dans les simulations numériques. Le Chap. 5 pose des questions sur la nécessité d'élasticité pour produire un régime chaotique et conclut qu'une solution de polymère de barres peut créer un régime comparable à la turbulence élastique. / Particles, when subjected into a flow, may display preferred orientations and a variety of deformations depending on their geometry and elasticity and the flow velocity field. Flows can conversely be modified when the particle stresses are sufficiently large. This thesis presents theoretical and numerical results on this two-way relationship between particles and flows in two parts. Part I starts with a stability analysis and numerical simulations that show a simple semiflexible Brownian particle in an extensional flow undergo tumbling, a phenomenon normally associated to shear flows. Chapter 2 extends analytical tools available only for elementary polymer models or for steady flows to general bead-rod-spring models in random flows. By building on the results from the previous chapters, Chap. 3 culminates with the study of an unexplored Lagrangian degree of freedom in a turbulent flow: bending. A semiflexible particle is shown to display different bending behaviours in two- and three-dimensional random flows. This prediction is confirmed via direct numerical simulations of the particle in a turbulent flow. Part II concerns “elastic turbulence", a chaotic regime created in a flow with low inertial forces by the addition of elastic polymers. Chapter 4 provides an estimate for the number of degrees of freedom of a solution of this chaotic system via the Lyapunov dimension of the attractor of the two-dimensional Oldroyd-B model, a model known to reproduce elastic turbulence in numerical simulations. Chapter 5 questions the necessity of elasticity in producing a chaotic regime and concludes that a rodlike polymer solution can create a regime similar to elastic turbulence.

The focus of the thesis is on the development of a few stochastic search schemes for inverse problems and their applications in medical imaging. After the introduction in Chapter 1 that motivates and puts in perspective the work done in later chapters, the main body of the thesis may be viewed as composed of two parts: while the first part concerns the development of stochastic search algorithms for inverse problems (Chapters 2 and 3), the second part elucidates on the applicability of search schemes to inverse problems of interest in tomographic imaging (Chapters 4 and 5). The chapter-wise contributions of the thesis are summarized below. Chapter 2 proposes a Monte Carlo stochastic filtering algorithm for the recursive estimation of diffusive processes in linear/nonlinear dynamical systems that modulate the instantaneous rates of Poisson measurements. The same scheme is applicable when the set of partial and noisy measurements are of a diffusive nature. A key aspect of our development here is the filter-update scheme, derived from an ensemble approximation of the time-discretized nonlinear Kushner Stratonovich equation, that is modified to account for Poisson-type measurements. Specifically, the additive update through a gain-like correction term, empirically approximated from the innovation integral in the filtering equation, eliminates the problem of particle collapse encountered in many conventional particle filters that adopt weight-based updates. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth, first with application to filtering problems with diffusive or Poisson-type measurements and then to an automatic control problem wherein the exterminations of the associated cost functional is achieved simply by an appropriate redefinition of the innovation process. The aim of one of the numerical examples in Chapter 2 is to minimize the structural response of a duffing oscillator under external forcing. We pose this problem of active control within a filtering framework wherein the goal is to estimate the control force that minimizes an appropriately chosen performance index. We employ the proposed filtering algorithm to estimate the control force and the oscillator displacements and velocities that are minimized as a result of the application of the control force. While Fig. 1 shows the time histories of the uncontrolled and controlled displacements and velocities of the oscillator, a plot of the estimated control force against the external force applied is given in Fig. 2. (a) (b) Fig. 1. A plot of the time histories of the uncontrolled and controlled (a) displacements and (b) velocities. Fig. 2. A plot of the time histories of the external force and the estimated control force Stochastic filtering, despite its numerous applications, amounts only to a directed search and is best suited for inverse problems and optimization problems with unimodal solutions. In view of general optimization problems involving multimodal objective functions with a priori unknown optima, filtering, similar to a regularized Gauss-Newton (GN) method, may only serve as a local (or quasi-local) search. In Chapter 3, therefore, we propose a stochastic search (SS) scheme that whilst maintaining the basic structure of a filtered martingale problem, also incorporates randomization techniques such as scrambling and blending, which are meant to aid in avoiding the so-called local traps. The key contribution of this chapter is the introduction of yet another technique, termed as the state space splitting (3S) which is a paradigm based on the principle of divide-and-conquer. The 3S technique, incorporated within the optimization scheme, offers a better assimilation of measurements and is found to outperform filtering in the context of quantitative photoacoustic tomography (PAT) to recover the optical absorption field from sparsely available PAT data using a bare minimum ensemble. Other than that, the proposed scheme is numerically shown to be better than or at least as good as CMA-ES (covariance matrix adaptation evolution strategies), one of the best performing optimization schemes in minimizing a set of benchmark functions. Table 1 gives the comparative performance of the proposed scheme and CMA-ES in minimizing a set of 40-dimensional functions (F1-F20), all of which have their global minimum at 0, using an ensemble size of 20. Here, 10 5 is the tolerance limit to be attained for the objective function value and MAX is the maximum number of iterations permissible to the optimization scheme to arrive at the global minimum. Table 1. Performance of the SS scheme and Chapter 4 gathers numerical and experimental evidence to support our conjecture in the previous chapters that even a quasi-local search (afforded, for instance, by the filtered martingale problem) is generally superior to a regularized GN method in solving inverse problems. Specifically, in this chapter, we solve the inverse problems of ultrasound modulated optical tomography (UMOT) and diffraction tomography (DT). In UMOT, we perform a spatially resolved recovery of the mean-squared displacements, p r of the scattering centres in a diffusive object by measuring the modulation depth in the decaying autocorrelation of the incident coherent light. This modulation is induced by the input ultrasound focussed to a specific region referred to as the region of interest (ROI) in the object. Since the ultrasound-induced displacements are a measure of the material stiffness, in principle, UMOT can be applied for the early diagnosis of cancer in soft tissues. In DT, on the other hand, we recover the real refractive index distribution, n r of an optical fiber from experimentally acquired transmitted intensity of light traversing through it. In both cases, the filtering step encoded within the optimization scheme recovers superior reconstruction images vis-à-vis the GN method in terms of quantitative accuracies. Fig. 3 gives a comparative cross-sectional plot through the centre of the reference and reconstructed p r images in UMOT when the ROI is at the centre of the object. Here, the anomaly is presented as an increase in the displacements and is at the centre of the ROI. Fig. 4 shows the comparative cross-sectional plot of the reference and reconstructed refractive index distributions, n r of the optical fiber in DT. Fig. 3. Cross-sectional plot through the center of the reference and reconstructed p r images. Fig. 4. Cross-sectional plot through the center of the reference and reconstructed n r distributions. In Chapter 5, the SS scheme is applied to our main application, viz. photoacoustic tomography (PAT) for the recovery of the absorbed energy map, the optical absorption coefficient and the chromophore concentrations in soft tissues. Nevertheless, the main contribution of this chapter is to provide a single-step method for the recovery of the optical absorption field from both simulated and experimental time-domain PAT data. A single-step direct recovery is shown to yield better reconstruction than the generally adopted two-step method for quantitative PAT. Such a quantitative reconstruction maybe converted to a functional image through a linear map. Alternatively, one could also perform a one-step recovery of the chromophore concentrations from the boundary pressure, as shown using simulated data in this chapter. Being a Monte Carlo scheme, the SS scheme is highly parallelizable and the availability of such a machine-ready inversion scheme should finally enable PAT to emerge as a clinical tool in medical diagnostics. Given below in Fig. 5 is a comparison of the optical absorption map of the Shepp-Logan phantom with the reconstruction obtained as a result of a direct (1-step) recovery. Fig. 5. The (a) exact and (b) reconstructed optical absorption maps of the Shepp-Logan phantom. The x- and y-axes are in m and the colormap is in mm-1. Chapter 6 concludes the work with a brief summary of the results obtained and suggestions for future exploration of some of the schemes and applications described in this thesis.