Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Investigation of Memory Related Cortical Thalamic Circuitry in the Human Brain

This dissertation examined the role of medial prefrontal cortex (mPFC) and the hippocampus (HC) in episodic memory, and provides a novel approach to identify the midline thalamus mediating mPFC-HC interactions in humans. The mPFC and HC are critical to the temporal organization of episodic memory, and these interactions are disrupted in several mental health and neurological disorders. In the first study, I provide evidence that the mPFC is involved in ordinal retrieval, and the HC is active in temporal context retrieval in remembering the order of when events happen. In the second study, I focus on the anatomical basis of the mPFC-HC interactions which is reliant on the midline thalamus. I review in detail the anatomy of the midline thalamus both in location, and connectivity profile with the rest of the brain comparing the extensive anatomical evidence in rodents with the available evidence in monkeys and humans. This section also elaborates on the role of the midline thalamus in memory, stress regulation, wakefulness, and feeding behavior, and how pathological markers along the midline thalamus are a vanguard of several neurological disorders including Alzheimer’s Disease, schizophrenia, depression, and drug addiction. Lastly, I devised a new approach to identify the midline thalamus in humans in vivo using diffusion weighted imaging, capitalizing on known fiber connections gleaned from non-human animals, focusing on connections between the midline thalamus and the mPFC, medial temporal lobe and the nucleus accumbens. The success of this approach is promising for translational imaging. Overall, this dissertation provides new evidence on 1) complementary functional roles of the mPFC and HC in sequence memory, 2) a cross-species anatomical framework for understanding the midline thalamus in humans and neurological disorders, and 3) a new method for non-invasive identification of the midline thalamus in humans in vivo. Thus, this dissertation provides a new fundamental understanding of mPFC-midline thalamic-HC circuit in humans and tools for its non-invasive study in human disease

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Immersed boundary method for cavitating and biological flows

The aim of the present work is the development of a computational tool to ease the numerical simulation of cavitating flows in domains of complex topology or with arbitrary moving boundaries. Within the framework of Computational Fluid Dynamics(CFD), an Immersed Boundary (IB) Method has been developed. According to the IB methodology, the grid that discretises the computational domain does not need to conform to the geometry and the solid boundaries are modelled on a fixed canonical grid by alternations of the governing equations in their vicinity. This modelling strategy is beneficial in terms of both computational cost and numerical solution. The grid generation, which is a complex and time consuming process, is simplified as a regular canonical grid, non-conformal to the boundaries, can be used. In addition, when moving boundaries are present, a conformal grid would need to adapt or deform following the motion of boundaries, which would increase the computational cost of the simulations in the first case and affect the solution in the latter case; the use of IB method alleviates these issues. The developed method follows the direct-forcing approach, which simply adds to the governing equations a source term to account for the body force acting on the fluid. The simplicity of the method makes it suitable for complex flow regimes, including phase change, strong shocks and compressibility effects, as well as Fluid Structure Interaction (FSI). Since cavitation dynamics regard a wide range of applications of engineering interest, from hydraulic machines to novel therapeutic techniques, the method is designed to be applicable in a wide range of flow regimes. Turbulent modelling and flow induced motion has been taken into account. The method has been successfully applied to cavitating and incompressible cases where conventional techniques are not easily or at all applicable. The shock-wave interaction with material interfaces is studied via the high-speed impact of a solid projectile on a water jet, which has been studied only experimentally before and only qualitative observations existed. The numerical investigation with the proposed methodology unveiled rich information regarding the physics of the impact, the resulting shock formation, cavitation development and interface instabilities initiation. Moreover, the methodology was applied on the thoroughly studied pulsatile flow through a bi leaflet Mechanical Heart Valve, to provide additional information regarding shear stress development. The methodology aids an experimental campaign employing novel shear stress measuring techniques, carried out by our collaborators. The research work and the developed method described in the present Thesis, intend to set the foundations for more elaborate numerical investigations of highly complex problems of Fluid Dynamics

Soil-Water Conservation, Erosion, and Landslide

The predicted climate change is likely to cause extreme storm events and, subsequently, catastrophic disasters, including soil erosion, debris and landslide formation, loss of life, etc. In the decade from 1976, natural disasters affected less than a billion lives. These numbers have surged in the last decade alone. It is said that natural disasters have affected over 3 billion lives, killed on average 750,000 people, and cost more than 600 billion US dollars. Of these numbers, a greater proportion are due to sediment-related disasters, and these numbers are an indication of the amount of work still to be done in the field of soil erosion, conservation, and landslides. Scientists, engineers, and planners are all under immense pressure to develop and improve existing scientific tools to model erosion and landslides and, in the process, better conserve the soil. Therefore, the purpose of this Special Issue is to improve our knowledge on the processes and mechanics of soil erosion and landslides. In turn, these will be crucial in developing the right tools and models for soil and water conservation, disaster mitigation, and early warning systems