Modern parallel computing techniques are subject to poor scalability. Their performance tends to suffer diminishing returns and even losses with increasing parallelism. Some methods of intelligent computing, such as neural networks and genetic algorithms, lend themselves well to massively parallel systems but come with other drawbacks that can limit their usefulness such as the requirement of a training phase and/or sensitivity to randomness. This thesis investigates the feasibility of a novel method of intelligent parallel computing by implementing a true multiple instruction stream, single data stream (MISD) computing system that is theoretically nearly perfectly scalable. Computational dreaming (CD) is inspired by the structure and dreaming process of the human brain. It examines previously observed input data during a 'dream phase' and is able to develop and select a simplified model to use during the day phase of computation. Using mazes as an example problem space, a CD simulator is developed and successfully used to demonstrate the viability and robustness of CD. Experiments that focused on CD viability resulted in the CD system solving 15% of mazes (ranging from small and simple to large and complex) compared with 2.2% solved by random model selection. Results also showed that approximately 50% of successful solutions generated match up with those that would be generated by algorithms such as depth first search and Dijkstra's algorithm. Experiments focusing on robustness performed repeated trials with identical parameters. Results demonstrated that CD is capable of achieving this result consistently, solving over 32% of mazes across 10 trials compared to only 3.6% solved by random model selection. A significant finding is that CD does not get stuck on local minima, always converging on a solution model. Thus, CD has the potential to enable significant contributions to computing by potentially finding elegant solutions to, for example, NP-hard or previously intractable problems. / Master of Science

The wings of most species of owl have been shown to possess three unique physical attributes which allow them to hunt in effective silence: a comb of evenly-spaced bristles along the wing leading-edge; a compliant and porous fringe of feathers at the trailing-edge; and a velvety down material distributed over the upper wing surface. This investigation focuses on the last of the mechanisms as a means to reduce noise from flow over surface roughness. A microscopic study of several owl feathers revealed the structure of the velvety down to be very similar to that of a forest or a field of crops. Analogous surface treatments (suspended canopies) were designed which simulated the most essential geometric features of the velvety down material. The Virginia Tech Anechoic Wall-Jet Facility was used to perform far-field noise and surface pressure fluctuation measurements in the presence of various combinations of rough surfaces and suspended canopies. All canopies were demonstrated to have a strong influence on the surface pressure spectra, and attenuations of up to 30 dB were observed. In addition, all canopies were shown to have some positive effects on far-field noise, and optimized canopies yielded far-field noise reductions of up to 8 dB across all frequencies at which roughness noise was observed. This development represents a new passive method for roughness noise control with possibility for future optimization and application to engineering structures. / Master of Science

Noise from fluid flow over rough surfaces is an important consideration in the design and performance of certain vehicles with high surface-area-to-perimeter ratios. A new method of noise control based on the anatomy of owls is developed and consists of fabric or fibrous canopies suspended above the surface. The method is tested experimentally and is found to reduce the total far-field noise emitted by the surface. The treatment also is found to reduce the magnitude of pressure fluctuations felt by the underlying surface by up to three orders of magnitude. Experimental investigations into the effects of geometric parameters of the canopies lead to an optimized design which maximizes noise reduction. The results obtained during the canopy experiment inspired a separate new device for the reduction of trailing edge noise. This type of noise is generated by flow past the wing of an aircraft or the blades of a wind turbine, and is a source of annoyance for those in surrounding communities. The newly developed treatment consists of small fins, or "finlets," placed near the trailing edge of an airfoil. The treatment is tested experimentally at near-full-scale conditions and is found to reduce the magnitude of far-field noise by up to 10 dB. Geometric parameters of the finlets are tested to determine the optimal size and spacing of the finlets to maximize noise reduction. Follow-up computational and experimental studies reveal the fluid mechanics behind the noise reduction by showing that the finlets produce a velocity deficit in the flow near the trailing edge and limit the magnitude and spanwise correlation lengthscale of turbulence near the trailing edge, factors which determine the magnitude of far-field noise. In a final experiment, the finlets are applied to a marine propeller and are found to reduce not only trailing edge noise, but also noise caused by the bluntness of the trailing edge. The results of this experiment show the potential usefulness of finlets to reduce noise from rotating systems, such as fans or propellers, as well as from structures which feature blunt trailing edges. / Ph. D.

In this study, structure-property relationships in the ironclad beetle (Zopherus nodulosus haldemani) exoskeleton are quantified to develop novel bio-inspired impact resistance technologies. The hierarchical structure of this exoskeleton was observed at various length scales for both the ironclad beetle pronotum and elytron. The exocuticle and endocuticle layers provide the bulk of the structural integrity and consist of chitiniber planes arranged in a Bouligand structure. The pronotum consists of a layered structure, while elytron consists of an extra layer with “tunnel-like” voids running along the anteroposterior axis along with smaller interconnecting “tunnel-like” voids in the lateral plane. Energy dispersive X-ray diffraction revealed the existence of minerals such as calcium carbonate, iron oxide, zinc oxide, and manganese oxide. We assert that the strength of this exoskeleton could be attributed to its overall thickness, the epicuticle layer thickness, the existence of various minerals embedded in the exoskeleton, and its structural hierarchy. The thickness of the exoskeleton correlates to a higher number of chitiniber planes to increase fracture toughness, while the increased thickness of the epicuticle prevents hydration of the chitiniber planes. In previous studies, the existence of minerals in the exoskeleton has been shown to create a tougher material compared to non-mineralized exoskeletons.

Mycorrhizal Networks (MNs) facilitate the exchange of resources including energy, water, nutrients, and information between trees and plants in forest ecosystems. This work explored MNs as an inspiration for new market models in transactive energy networks, which similarly involve exchanges of energy and information between buildings in local communities. Specific insights from the literature on the structure and function of MNs were translated into an energy model with the aim of addressing challenges associated with the proliferation of distributed energy resources (DERs) at the grid edge and the incorporation of DER aggregations into wholesale energy markets. First, a systematic review of bio-inspired computing interventions applied to microgrids and their interactions with modern energy markets established a technical knowledge base within the context of distributed electrical systems. Second, a bio-inspired design process built on this knowledge base to yield a structural and functional blueprint for a computational mycorrhizal energy market simulation. Lastly, that computational model was implemented and simulated on a blockchain-compatible, multi-agent software platform to determine the effect that mycorrhizal strategies have on transactive energy market performance. The structural translation of a mapped ectomycorrhizal network of Douglas-firs in Oregon, USA called the 'wood-wide web' created an effective framework for the organization of a novel mycorrhizal energy market model that enabled participating buildings to redistribute percentages of their energy assets on different competing exchanges throughout a series of week-long simulations. No significant changes in functional performance –- as determined by economic, technical, and ecological metrics – were observed when the mycorrhizal results were compared to those of a baseline transactive energy community without periodic energy asset redistribution. Still, the model itself is determined to be a useful tool for further exploration of innovative, automated strategies for DER integration into modern energy market structures and electrical infrastructure in the age of Web3, especially as new science emerges to better explain trigger and feedback mechanisms for carbon exchange through MNs and how mycorrhizae adapt to changes in the environment. This dissertation concludes with a brief discussion of policy implications and an analysis applying the ecological principles of robustness, biodiversity, and altruism to the collective energy future of the human species. / Doctor of Philosophy / Beneath the forest floor, a network of fungi connects trees and plants and allows them to exchange energy and other resources. This dissertation compares this mycorrhizal network (mycorrhiza = fungus + root) to a group of solar-powered buildings generating energy and exchanging it in a local community marketplace (transactive energy markets). In the analogy, the buildings become the plants, the solar panels become the leaves, and the electrical grid represents the mycorrhizal network. Trees and plants produce their own energy through photosynthesis and then send large portions of it down to the roots, where they can trade it or send it to neighbors via the mycorrhizal network. Similarly, transactive energy markets are designed to allow buildings to sell the energy they produce on-site to neighbors, usually at better rates. This helps address a major infrastructure challenge that is arising with more people adding roof-top solar to their homes. The grid that powers our buildings is old now and it was designed to send power from a central power plant out to its edges where most homes and businesses are located. When too many homes produce solar power at the same time, there is nowhere for it to go, and it can easily overload the grid leading to fires, equipment failures, and power outages. Mycorrhizal networks solve this problem in part through local energy balancing driven by cooperative feedback patterns that have evolved over millennia to sustain forest ecosystems. This work applies scientific findings on the structure and function of mycorrhizal networks (MNs) to energy simulation methods in order to better understand the potential for building bio-inspired energy infrastructure in local communities. Specifically, the mapped structure of a MN of douglas-fir trees in Oregon, USA was adapted into a digital transactive energy market (TEM) model. This adaptation process revealed that a single building can connect to many TEMs simultaneously and that the number of connections can change over time just as symbiotic connections between organisms grow, decay, and adapt to a changing environment. The behavior of MNs in terms of when those connections are added and subtracted informed the functionality of the TEM model, which adds connections when community energy levels are high and subtracts connections when energy levels are low. The resulting 'mycorrhizal' model of the TEM was able to change how much energy each connected household traded on it by changing the number of connections (more connections mean more energy and vice versa). Though the functional performance of the mycorrhizal TEM did not change significantly from that of a typical TEM when they were the context of decentralized computer networks (blockchains) and distributed artificial intelligence. A concluding discussion addresses ways in which elements of this new model could transform energy distribution in communities and improve the resilience of local energy systems in the face of a changing climate.

L’étude du fonctionnement de notre système nerveux et des mécanismes sensoriels a mené à la création de capteurs événementiels. Ces capteurs ont un fonctionnement qui retranscrit les atouts de nos yeux et oreilles par exemple. Cette thèse se base sur la recherche de méthodes bio-inspirés et peu coûteuses en énergie permettant de traiter les données envoyées par ces nouveaux types de capteurs. Contrairement aux capteurs conventionnels, nos rétines et cochlées ne réagissent qu’à l’activité perçue dans l’environnement sensoriel. Les implémentations de type « rétine » ou « cochlée » artificielle, que nous appellerons capteurs dynamiques, fournissent des trains d’évènements comparables à des impulsions neuronales. La quantité d’information transmise est alors étroitement liée à l’activité présentée, ce qui a aussi pour effet de diminuer la redondance des informations de sortie. De plus, n’étant plus contraint à suivre une cadence d’échantillonnage, les événements créés fournissent une résolution temporelle supérieure. Ce mode bio-inspiré de retrait d’information de l’environnement a entraîné la création d’algorithmes permettant de suivre le déplacement d’entité au niveau visuel ou encore reconnaître la personne parlant ou sa localisation au niveau sonore, ainsi que des implémentations d’environnements de calcul neuromorphiques. Les travaux que nous présentons s’appuient sur ces nouvelles idées pour créer de nouvelles solutions de traitement. Plus précisément, les applications et le matériel développés s’appuient sur un codage temporel de l’information dans la suite d'événements fournis par le capteur. / Studying how our nervous system and sensory mechanisms work lead to the creation of event-driven sensors. These sensors follow the same principles as our eyes or ears for example. This Ph.D. focuses on the search for bio-inspired low power methods enabling processing data from this new kind of sensor. Contrary to legacy sensors, our retina and cochlea only react to the perceived activity in the sensory environment. The artificial “retina” and “cochlea” implementations we call dynamic sensors provide streams of events comparable to neural spikes. The quantity of data transmitted is closely linked to the presented activity, which decreases the redundancy in the output data. Moreover, not being forced to follow a frame-rate, the created events provide increased timing resolution. This bio-inspired support to convey data lead to the development of algorithms enabling visual tracking or speaker recognition or localization at the auditory level, and neuromorphic computing environment implementation. The work we present rely on these new ideas to create new processing solutions. More precisely, the applications and hardware developed rely on temporal coding of the data in the spike stream provided by the sensors.

A Clusterização de dados em grupos oferece uma maneira de entender e extrair informações relevantes de grandes conjuntos de dados. A abordagem em relação a aspectos como a representação dos dados e medida de similaridade entre clusters, e a necessidade de ajuste de parâmetros iniciais são as principais diferenças entre os algoritmos de clusterização, influenciando na qualidade da divisão dos clusters. O uso cada vez mais comum de grandes conjuntos de dados aliado à possibilidade de melhoria das técnicas já existentes tornam a clusterização de dados uma área de pesquisa que permite inovações em diferentes campos. Nesse trabalho é feita uma revisão dos métodos de clusterização já existentes, e é descrito um novo método de clusterização de dados baseado na identificação de comunidades em redes complexas e modelos computacionais inspirados biologicamente. A técnica de clusterização proposta é composta por duas etapas: formação da rede usando os dados de entrada; e particionamento dessa rede para obtenção dos clusters. Nessa última etapa, a técnica de otimização por nuvens de partículas é utilizada a fim de identificar os clusters na rede, resultando em um algoritmo de clusterização hierárquico divisivo. Resultados experimentais revelaram como características do método proposto a capacidade de detecção de clusters de formas arbitrárias e a representação de clusters com diferentes níveis de refinamento. / DAta clustering is an important technique to understand and to extract relevant information in large datasets. Data representation and similarity measure adopted, and the need to adjust initial parameters, are the main differences among clustering algorithms, interfering on clusters quality. The crescent use of large datasets and the possibility to improve existing techniques make data clustering a research area that allows innovation in different fields. In this work is made a review of existing data clustering methods, and it is proposed a new data clustering technique based on community dectection on complex networks and bioinspired models. The proposed technique is composed by two steps: network formation to represent input data; and network partitioning to identify clusters. In the last step, particle swarm optimization technique is used to detect clusters, resulting in an hierarchical clustering algorithm. Experimental results reveal two main features of the algorithm: the ability to detect clusters in arbitrary shapes and the ability to generate clusters with different refinement degrees

L’éolien est un secteur industriel en pleine expansion, qui joue un rôle fondamental dans le développement des énergies renouvelables. Cependant ces machines sont performantes sur une plage de fonctionnement étroite. Afin d’adapter l’éolienne aux changements de vent, une solution actuellement mise en place sur certaines éoliennes commerciales consiste à faire varier l’angle de calage (ie l’inclinaison) des pales au cours de son fonctionnement. Cette méthode de contrôle actif élargit la plage de hauts rendements ainsi que la plage de fonctionnement global, et améliore le démarrage de l’éolienne, mais elle n’augmente pas le rendement maximal atteint par une éolienne à angle de calage optimal fixé. Cependant la complexité́ de cette méthode ainsi que ses coûts de conception, de construction et de maintenance la rende inaccessible pour beaucoup d’éoliennes, en particulier celles de petite taille. Récemment des recherches se sont orientées vers un contrôle passif de l’angle de calage. Dans cette thèse nous examinons expérimentalement et théoriquement l’intérêt d’utiliser des pales flexibles suivant la corde sur une éolienne à axe horizontal. L’étude se concentre sur deux questions : - comprendre le mécanisme de reconfiguration de la pale flexible bio-inspirée : la déformation de la pale est due à la compétition entre les forces aérodynamiques, qui augmentent l’angle de calage moyen, et la force centrifuge qui le diminue. Ces effets sont gouvernés par deux nombres adimensionnés, respectivement le nombre de Cauchy et le nombre centrifuge. - qualifier et quantifier le gain en performances de l’éolienne : une flexibilité́ de pale modérée élargit la plage de fonctionnement, et augmente significativement le rendement de l’éolienne, expérimentalement jusqu’à 35% sur la plage de hauts rendements. Une procédure d’optimisation visant à déterminer le matériau optimal de la pale flexible est présentée. Ces gains obtenus en régime stationnaire sont conservés expérimentalement en moyenne en régime instationnaire. Deux temps caractéristiques sont identifiés : le temps de reconfiguration de la pale flexible et le temps de variation de la fréquence de rotation de l’éolienne / Wind energy is a rapidly growing branch of industry, playing a significant role in the development of renewable energies. However these machines are efficient only on a narrow working range. In order to adapt wind turbines to wind changes, some commercial machines are pitch controlled during rotation. This active control method extends the high-efficiency range and the total working range, and improves the starting phase, but it does not increase the maximum efficiency reached by a wind turbine with the fixed optimal pitch angle. However this method is complex and costly (design, construction, maintenance). Thus it becomes cost-effective only for large wind turbines. Research recently focused on passive pitch control. In this thesis, the contribution of chord wise flexible blades is studied both experimentally and theoretically. The thesis concentrates on: - the reconfiguration mechanism of the bio-inspired flexible blade : the deformation is the result of the competition between aerodynamic forces, which increase the pitch angle, and the centrifugal force, which reduces it. These two effects are governed by two dimensionless numbers, respectively the Cauchy number and the centrifugal number. - how to qualify and quantify the efficiency gains : a moderate flexibility extends the working range, and significantly increases wind turbine efficiency, up to 35% on the high-efficiency working range. An optimization procedure is presented, which aims at determining the optimal material to construct the blade. These improvements measured in steady regime are maintained on average when rotational speed is unsteady. Two characteristic times are identified: the reconfiguration time of the flexible blade and the time of variation of the rotational speed of the wind turbine

The stomatal network in plants is a well-characterized biological system that hypothetically solves the constrained optimization problem of maximizing CO2 uptake from the air while constraining evaporative water loss during the process of photosynthesis. There are numerous such constrained optimization problems present in the real world as well as in computer science. This thesis work attempts to solve one such constrained optimization problem in a distributed manner by taking a cue from the dynamics of stomatal networks. The problem considered here is Dynamic Thermal Management (DTM) in a multi-processing element system in computing. There have been several approaches in the past that tried to solve the problem of DTM by varying the frequency of operation of blocks in the computing system. The selection of frequencies for DTM such that overall performance is maximized while temperature is constrained is a non-deterministic polynomial-time (NP) hard problem. In this thesis, a distributed approach to solve the problem of DTM using a cellular neural network is proposed. A cellular neural network is used to mimic the stomatal network with slight variations based on the problem considered.

Electronic noses combine an array of cross-selective gas sensors with a pattern recognition engine to identify odors. Pattern recognition of multivariate gas sensor response is usually performed using existing statistical and chemometric techniques. An alternative solution involves developing novel algorithms inspired by information processing in the biological olfactory system. The objective of this dissertation is to develop a neuromorphic architecture for pattern recognition for a chemosensor array inspired by key signal processing mechanisms in the olfactory system. Our approach can be summarized as follows. First, a high-dimensional odor signal is generated from a chemical sensor array. Three approaches have been proposed to generate this combinatorial and high dimensional odor signal: temperature-modulation of a metal-oxide chemoresistor, a large population of optical microbead sensors, and infrared spectroscopy. The resulting high-dimensional odor signals are subject to dimensionality reduction using a self-organizing model of chemotopic convergence. This convergence transforms the initial combinatorial high-dimensional code into an organized spatial pattern (i.e., an odor image), which decouples odor identity from intensity. Two lateral inhibitory circuits subsequently process the highly overlapping odor images obtained after convergence. The first shunting lateral inhibition circuits perform gain control enabling identification of the odorant across a wide range of concentration. This shunting lateral inhibition is followed by an additive lateral inhibition circuit with center-surround connections. These circuits improve contrast between odor images leading to more sparse and orthogonal patterns than the one available at the input. The sharpened odor image is stored in a neurodynamic model of a cortex. Finally, anti-Hebbian/ Hebbian inhibitory feedback from the cortical circuits to the contrast enhancement circuits performs mixture segmentation and weaker odor/background suppression, respectively. We validate the models using experimental datasets and show our results are consistent with recent neurobiological findings.