A Fast Alpha-tree Algorithm for Extreme Dynamic Range Pixel Dissimilarities

The α-tree algorithm is a useful hierarchical representation technique which facilitates comprehension of imagessuch as remote sensing and medical images. Most α-tree algorithms make use of priority queues to process image edgesin a correct order, but because traditional priority queues areinefficient in α-tree algorithms using extreme-dynamic-rangepixel dissimilarities, they run slower compared with other relatedalgorithms such as component tree. In this paper, we proposea novel hierarchical heap priority queue algorithm that canprocess α-tree edges much more efficiently than other stateof-the-art priority queues. Experimental results using 48-bitSentinel-2A remotely sensed images and randomly generatedimages have shown that the proposed hierarchical heap priorityqueue improved the timings of the flooding α-tree algorithm byreplacing the heap priority queue with the proposed queue: 1.68times in 4-N and 2.41 times in 8-N on Sentinel-2A images, and2.56 times and 4.43 times on randomly generated images

Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition

Modern machine learning models are beginning to rival human performance on some realistic object recognition tasks, but we still lack a full understanding of how the human brain solves this same problem. This thesis combines knowledge from machine learning and computational neuroscience to create models of human object recognition that are increasingly realistic both in their treatment of low-level neural mechanisms and in their reproduction of high-level human behaviour. First, I present extensions to the Neural Engineering Framework to make its preferred type of model---the “fixed-encoding” network---more accurate for object recognition tasks. These extensions include better distributions---such as Gabor filters---for the encoding weights, and better loss functions---namely weighted squared loss, softmax loss, and hinge loss---to solve for decoding weights. Second, I introduce increased biological realism into deep convolutional neural networks trained with backpropagation, by training them to run using spiking leaky integrate-and-fire (LIF) neurons. These models have been successful in machine learning, and I am able to convert them to spiking networks while retaining similar levels of performance. I present a novel method to smooth the LIF rate response function in order to avoid the common problems associated with differentiating spiking neurons in general and LIF neurons in particular. I also derive a number of novel characterizations of spiking variability, and use these to train spiking networks to be more robust to this variability. Finally, to address the problems with implementing backpropagation in a biological system, I train spiking deep neural networks using the more biological Feedback Alignment algorithm. I examine this algorithm in depth, including many variations on the core algorithm, methods to train using non-differentiable spiking neurons, and some of the limitations of the algorithm. Using these findings, I construct a spiking model that learns online in a biologically realistic manner. The models developed in this thesis help to explain both how spiking neurons in the brain work together to allow us to recognize complex objects, and how the brain may learn this behaviour. Their spiking nature allows them to be implemented on highly efficient neuromorphic hardware, opening the door to object recognition on energy-limited devices such as cell phones and mobile robots

Physics of Complex Plasmas.

Physics of complex plasmas is a wide and varied field. In the context of this PhD thesis I present the major results from my research on fundamental properties of the plasma sheath, the plasma dust interaction, non-Hamiltonian dynamics, and on non-equilibrium phase transitions, using complex plasmas as a model system. The first chapter provides a short overview of the development of physics of Complex Plasmas. From fundamental plasma physics, properties of dust in plasmas, to the exceptional and unique features of complex plasmas. A summary of twenty years of research topics is also presented. This is followed by three chapters that illustrate publications based on experiments I did during my PhD. These publications, in my opinion, reflect nicely the large diversity of complex plasma research. • The investigation of nonlinear vertical oscillations of a particle in a sheath of an rf discharge was a simultaneous test of (pre-)sheath models and parameters. The nonlinear oscillations were shown to derive from a (strong) nonlinearity of the local sheath potential. They could be described quantitatively applying the theory of anharmonic oscillations, and the first two anharmonic terms in an expansion of the sheath potential were measured. On top of that we provided a simple experimentally, theoretically and mathematically based method that allows for in situ measurement of these coefficients for other experimental conditions. • The vertical pairing of identical particles suspended in the plasma sheath demonstrated some of the unique features that complex plasmas have as an open (non-Hamiltonian) system. Particle interaction becomes non-reciprocal in the presence of streaming ions. The symmetry breaking allows for mode-coupling of in plane and out of plane motion of particles. • Lane formation is a non-equilibrium phase transition. I summarize the main result of my papers on the dynamics of lane formation, i.e., the temporal evolution of lanes. This is followed by an outlook on my future research on non-equilibrium phase transitions, how they relate to our research of systems at the critical point, and how they allow us to test fundamental theories of charging of particles and the shielding of the resulting surface potential. Finally there is an appendix on the scaling index method. A versatile mathematical tool to quantify structural differences / peculiarities in data, that I used to define a suitable order parameter for lane formation

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform

Harnessing Neural Dynamics as a Computational Resource

Researchers study nervous systems at levels of scale spanning several orders of magnitude, both in terms of time and space. While some parts of the brain are well understood at specific levels of description, there are few overarching theories that systematically bridge low-level mechanism and high-level function. The Neural Engineering Framework (NEF) is an attempt at providing such a theory. The NEF enables researchers to systematically map dynamical systems—corresponding to some hypothesised brain function—onto biologically constrained spiking neural networks. In this thesis, we present several extensions to the NEF that broaden both the range of neural resources that can be harnessed for spatiotemporal computation and the range of available biological constraints. Specifically, we suggest a method for harnessing the dynamics inherent in passive dendritic trees for computation, allowing us to construct single-layer spiking neural networks that, for some functions, achieve substantially lower errors than larger multi-layer networks. Furthermore, we suggest “temporal tuning” as a unifying approach to harnessing temporal resources for computation through time. This allows modellers to directly constrain networks to temporal tuning observed in nature, in ways not previously well-supported by the NEF. We then explore specific examples of neurally plausible dynamics using these techniques. In particular, we propose a new “information erasure” technique for constructing LTI systems generating temporal bases. Such LTI systems can be used to establish an optimal basis for spatiotemporal computation. We demonstrate how this captures “time cells” that have been observed throughout the brain. As well, we demonstrate the viability of our extensions by constructing an adaptive filter model of the cerebellum that successfully reproduces key features of eyeblink conditioning observed in neurobiological experiments. Outside the cognitive sciences, our work can help exploit resources available on existing neuromorphic computers, and inform future neuromorphic hardware design. In machine learning, our spatiotemporal NEF populations map cleanly onto the Legendre Memory Unit (LMU), a promising artificial neural network architecture for stream-to-stream processing that outperforms competing approaches. We find that one of our LTI systems derived through “information erasure” may serve as a computationally less expensive alternative to the LTI system commonly used in the LMU

Diagnosis of the sleep apnea-hypopnea syndrome : a comprehensive approach through an intelligent system to support medical decision

[Abstract] This doctoral thesis carries out the development of an intelligent system to support medical decision in the diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS). SAHS is the most common disorder within those affecting sleep. The estimates of the disease prevalence range from 3% to 7%. Diagnosis of SAHS requires of a polysomnographic test (PSG) to be done in the Sleep Unit of a medical center. Manual scoring of the resulting recording entails too much effort and time to the medical specialists and as a consequence it implies a high economic cost. In the developed system, automatic analysis of the PSG is accomplished which follows a comprehensive perspective. Firstly an analysis of the neurophysiological signals related to the sleep function is carried out in order to obtain the hypnogram. Then, an analysis is performed over the respiratory signals which have to be subsequently interpreted in the context of the remaining signals included in the PSG. In order to carry out such a task, the developed system is supported by the use of artificial intelligence techniques, specially focusing on the use of reasoning mechanisms capable of handling data imprecision. Ultimately, it is the aim of the proposed system to improve the diagnostic procedure and help physicians in the diagnosis of SAHS.[Resumen] Esta tesis aborda el desarrollo de un sistema inteligente de apoyo a la decisión clínica para el diagnóstico del Síndrome de Apneas-Hipopneas del Sueño (SAHS). El SAHS es el trastorno más común de aquellos que afectan al sueño. Afecta a un rango del 3% al 7% de la población con consecuencias severas sobre la salud. El diagnóstico requiere la realización de un análisis polisomnográfico (PSG) en una Unidad del Sueño de un centro hospitalario. El análisis manual de dicha prueba resulta muy costoso en tiempo y esfuerzo para el médico especialista, y como consecuencia en un elevado coste económico. El sistema desarrollado lleva a cabo el análisis automático del PSG desde una perspectiva integral. A tal efecto, primero se realiza un análisis de las señales neurofisiológicas vinculadas al sueño para obtener el hipnograma, y seguidamente, se lleva a cabo un análisis neumológico de las señales respiratorias interpretándolas en el contexto que marcan las demás señales del PSG. Para lleva a cabo dicha tarea el sistema se apoya en el uso de distintas técnicas de inteligencia artificial, con especial atención al uso mecanismos de razonamiento con soporte a la imprecisión. El principal objetivo del sistema propuesto es la mejora del procedimiento diagnóstico y ayudar a los médicos en diagnóstico del SAHS.[Resumo] Esta tese aborda o desenvolvemento dun sistema intelixente de apoio á decisión clínica para o diagnóstico do Síndrome de Apneas-Hipopneas do Sono (SAHS). O SAHS é o trastorno máis común daqueles que afectan ao sono. Afecta a un rango do 3% ao 7% da poboación con consecuencias severas sobre a saúde. O diagnóstico pasa pola realización dunha análise polisomnográfica (PSG) nunha Unidade do Sono dun centro hospitalario. A análise manual da devandita proba resulta moi custosa en tempo e esforzo para o médico especialista, e como consecuencia nun elevado custo económico. O sistema desenvolvido leva a cabo a análise automática do PSG dende unha perspectiva integral. A tal efecto, primeiro realizase unha análise dos sinais neurofisiolóxicos vinculados ao sono para obter o hipnograma, e seguidamente, lévase a cabo unha análise neumolóxica dos sinais respiratorios interpretándoos no contexto que marcan os demais sinais do PSG. Para leva a cabo esta tarefa o sistema apoiarase no uso de distintas técnicas de intelixencia artificial, con especial atención a mecanismos de razoamento con soporte para a imprecisión. O principal obxectivo do sistema proposto é a mellora do procedemento diagnóstico e axudar aos médicos no diagnóstico do SAHS

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010