Improving the performance of low-gain designs for bounded control of linear systems

An online scheduling of the parameter ensuring in addition to closed loop stability was presented. Attention was given to saturated linear low-gain control laws. Null controllability of the considered linear systems was assumed. The family of low gain control laws achieved semiglobal stabilization.

The interaction of riblets with wall-bounded turbulence

The purpose of this thesis is to give answer to the question: why do riblets stop working for a certain size? Riblets are small surface grooves aligned in the mean direction of an overlying turbulent flow, designed specifically to reduce the friction between the flow and the surface. They were inspired by biological surfaces, like the oriented denticles in the skin of fastswimming sharks, and were the focus of a significant amount of research in the late eighties and nineties. Although it was found that the drag reduction depends on the riblet size scaled in wall units, the physical mechanisms implicated have not been completely understood up to now. It has been explained how riblets of vanishing size interact with the turbulent flow, producing a change in the drag proportional to their size, but that is not the regime of practical interest. The optimum performance is achieved for larger sizes, once that linear behavior has broken down, but before riblets begin adopting the character of regular roughness and increasing drag. This regime, which is the most relevant from a technological perspective, was precisely the less understood, so we have focused on it. Our efforts have followed three basic directions. First, we have re-assessed the available experimental data, seeking to identify common characteristics in the optimum regime across the different existing riblet geometries. This study has led to the proposal of a new length scale, the square root of the groove crosssection, to substitute the traditional peak-to-peak spacing. Scaling the riblet dimension with this length, the size of breakdown of the linear behavior becomes roughly universal. This suggests that the onset of the breakdown is related to a certain, fixed value of the cross-section of the groove. Second, we have conducted a set of direct numerical simulations of the turbulent flow over riblets, for sizes spanning the full drag reduction range. We have thus been able to reproduce the gradual transition between the different regimes. The spectral analysis of the flows has proven particularly fruitful, since it has made possible to identify spanwise rollers immediately above the riblets, which begin to appear when the riblet size is close to the optimum. This is a quite surprising feature of the flow, not because of the uniqueness of the phenomenon, which had been reported before for other types of complex and porous surfaces, but because most previous studies had focused on the detail of the flow above each riblet as a unit. Our novel approach has provided the adequate tools to capture coherent structures with an extended spanwise support, which interact with the riblets not individually, but collectively. We have also proven that those spanwise structures are responsible for the increase in drag past the viscous breakdown. Finally, we have analyzed the stability of the flow with a simplified model that connects the appearance of rollers to a Kelvin–Helmholtz-like instability, as is the case also for the flow over plant canopies and porous surfaces. In spite of the model emulating the presence of riblets only in an averaged, general fashion, it succeeds to capture the essential attributes of the breakdown, and provides a theoretical justification for the scaling with the groove cross-section.

Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.

A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy) synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments.

Dense active appearance models using a bounded diameter minimum spanning tree

We present a method for producing dense Active Appearance Models (AAMs), suitable for video-realistic synthesis. To this end we estimate a joint alignment of all training images using a set of pairwise registrations and ensure that these pairwise registrations are only calculated between similar images. This is achieved by defining a graph on the image set whose edge weights correspond to registration errors and computing a bounded diameter minimum spanning tree (BDMST). Dense optical flow is used to compute pairwise registration and we introduce a flow refinement method to align small scale texture. Once registration between training images has been established we propose a method to add vertices to the AAM in a way that minimises error between the observed flow fields and a flow field interpolated between the AAM mesh points. We demonstrate a significant improvement in model compactness using the proposed method and show it dealing with cases that are problematic for current state-of-the-art approaches.