Les nano-oscillateurs à transfert de spin sont des composants radiofréquences magnétiques non-linéaires, nanométrique, de faible consommation en énergie et accordables en fréquence. Ce sont aussi potentiellement des candidats prometteurs pour l’élaboration de larges réseaux d’oscillateurs couplés. Ces derniers peuvent être utilisés dans les architectures neuromorphiques qui nécessitent des assemblées très denses d’unités de calcul complexes imitant les neurones biologiques et comportant des connexions ajustables entre elles. L’approche neuromorphique permet de pallier aux limitations des ordinateurs actuels et de diminuer leur consommation en énergie. En effet pour résoudre des tâches cognitives telles que la reconnaissance vocale, le cerveau fonctionne bien plus efficacement en terme d’énergie qu’un ordinateur classique. Au vu du grand nombre de neurone dans le cerveau (100 milliards) une puce neuro-inspirée requière des oscillateurs de très petite taille tels que les nano-oscillateurs à transfert de spin. Récemment, une première démonstration de calcul neuromorphique avec un unique nano-oscillateur à transfert de spin a été établie. Cependant, pour aller au-delà, il faut démontrer le calcul neuromorphique avec plusieurs nano-oscillateurs et pouvoir réaliser l’apprentissage. Une difficulté majeure dans l’apprentissage des réseaux de nano-oscillateurs est qu’il faut ajuster le couplage entre eux. Dans cette thèse, en exploitant l'accordabilité en fréquence des nano-oscillateurs magnétiques, nous avons démontré expérimentalement l'apprentissage des nano-oscillateurs couplés pour classifier des voyelles prononcées avec un taux de reconnaissance de 88%. Afin de réaliser cette tache de classification, nous nous sommes inspirés de la synchronisation des taux d’activation des neurones biologiques et nous avons exploité la synchronisation des nano-oscillateurs magnétiques à des stimuli micro-ondes extérieurs. Les taux de reconnaissances observés sont dus aux fortes accordabilités et couplage intermédiaire des nano-oscillateurs utilisés. Enfin, afin de réaliser des taches plus difficiles nécessitant de larges réseaux de neurones, nous avons démontré numériquement qu’un réseau d’une centaine de nano-oscillateurs magnétiques peut être conçu avec les contraintes standards de nano-fabrication. / Spin-torque nano-oscillators are non-linear, nano-scale, low power consumption, tunable magnetic microwave oscillators which are promising candidates for building large networks of coupled oscillators. Those can be used as building blocks for neuromorphic hardware which requires high-density networks of neuron-like complex processing units coupled by tunable connections. The neuromorphic approach allows to overcome the limitation of nowadays computers and to reduce their energy consumption. Indeed, in order to perform cognitive tasks as voice recognition or image recognition, the brain is much more efficient in terms of energy consumption. Due to the large number of required neurons (100 billions), a neuromorphic chip requires very small oscillators such as spin-torque nano-oscillators to emulate neurons. Recently a first demonstration of neuromorphic computing with a single spin-torque nano-oscillator was established, allowing spoken digit recognition with state of the art performance. However, to realize more complex cognitive tasks, it is still necessary to demonstrate a very important property of neural networks: learning an iterative process through which a neural network can be trained using an initial fraction of the inputs and then adjusting internal parameters to improve its recognition or classification performance. One difficulty is that training networks of coupled nano-oscillators requires tuning the coupling between them. Here, through the high frequency tunability of spin-torque nano-oscillators, we demonstrate experimentally the learning ability of coupled nano-oscillators to classify spoken vowels with a recognition rate of 88%. To realize this classification task, we took inspiration from the synchronization of rhythmic activity of biological neurons and we leveraged the synchronization of spin-torque nano-oscillators to external microwave stimuli. The high experimental recognition rates stem from the weak-coupling regime and the high tunability of spin-torque nano-oscillators. Finally, in order to realize more difficult cognitive tasks requiring large neural networks, we show numerically that arrays of hundreds of spin-torque nano-oscillators can be designed with the constraints of standard nano-fabrication techniques.

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The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model for evaluation, conducts a robust set of experiments to ascertain the efficacy of the AIS-IDS, and answers key research questions aimed at evaluating the validity of the AIS-IDS. Finally, two expansions to the AIS-IDS are proposed with the goal of further infusing the HIS into AIS-IDS design.

Online monitoring applications requiring advanced pattern recognition capabilities implemented in resource-constrained wireless sensor systems are challenging to construct using standard digital computers. An interesting alternative solution is to use a low-power neuromorphic processor like the ROLLS, with subthreshold mixed analog/digital circuits and online learning capabilities that approximate the behavior of real neurons and synapses. This requires that the monitoring algorithm is implemented with spiking neural networks, which in principle are efficient computational models for tasks such as pattern recognition. In this work, I investigate how spiking neural networks can be used as a pre-processing and feature learning system in a condition monitoring application where the vibration of a machine with healthy and faulty rolling-element bearings is considered. Pattern recognition with spiking neural networks is investigated using simulations with Brian -- a Python-based open source toolbox -- and an implementation is developed for the ROLLS neuromorphic processor. I analyze the learned feature-response properties of individual neurons. When pre-processing the input signals with a neuromorphic cochlea known as the AER-EAR system, the ROLLS chip learns to classify the resulting spike patterns with a training error of less than 1 %, at a combined power consumption of approximately 30 mW. Thus, the neuromorphic hardware system can potentially be realized in a resource-constrained wireless sensor for online monitoring applications.However, further work is needed for testing and cross validation of the feature learning and pattern recognition networks.i

Spiking neural networks (SNN) are dynamic models of biological neurons, that communicates with event-based signals called spikes. SNN that reproduce observed properties of biological senses like vision are developed to better understand how such systems function, and to learn how more efficient sensor systems can be engineered. A branching parameter describes the average probability for spikes to propagate between two different neuron populations. The adaptation of branching parameters towards critical values is known to be important for maximizing the sensitivity and dynamic range of SNN. In this thesis, a recently proposed SNN model for visual feature learning and pattern recognition known as the E-I Net model is studied and extended with a critical branching mechanism. The resulting modified E-I Net model is studied with numerical experiments and two different types of sensory queues. The experiments show that the modified E-I Net model demonstrates critical branching and power-law scaling behavior, as expected from SNN near criticality, but the power-laws are broken and the stimuli reconstruction error is higher compared to the error of the original E-I Net model. Thus, on the basis of these experiments, it is not clear how to properly extend the E-I Net model properly with a critical branching mechanism. The E-I Net model has a particular structure where the inhibitory neurons (I) are tuned to decorrelate the excitatory neurons (E) so that the visual features learned matches the angular and frequency distributions of feature detectors in visual cortex V1 and different stimuli are represented by sparse subsets of the neurons. The broken power-laws correspond to different scaling behavior at low and high spike rates, which may be related to the efficacy of inhibition in the model.

No description available.

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.

This thesis addresses orchestration of servers amongst multiple co-hosted internet services such as e-Banking, e-Auction and e-Retail in hosting centres. The hosting paradigm entails levying fees for hosting third party internet services on servers at guaranteed levels of service performance. The orchestration of server ensemble in hosting centres is considered in the context of maximising the hosting centre's revenue over a lengthy time horizon. The inspiration for the server orchestration approach proposed in this thesis is drawn from nature and generally classed as swarm intelligence, specifically, sophisticated collective behaviour of social insects borne out of primitive interactions amongst members of the group to solve problems beyond the capability of individual members. Consequently, the approach is self-organising, adaptive and robust. A new scheme for server ensemble orchestration is introduced in this thesis. This scheme exploits the many similarities between server orchestration in an internet hosting centre and forager allocation in a honeybee (Apis mellifera) colony. The scheme mimics the way a honeybee colony distributes foragers amongst flower patches to maximise nectar influx, to orchestrate servers amongst hosted internet services to maximise revenue. The scheme is extended by further exploiting inherent feedback loops within the colony to introduce self-tuning and energy-aware server ensemble orchestration. In order to evaluate the new server ensemble orchestration scheme, a collection of server ensemble orchestration methods is developed, including a classical technique that relies on past history to make time varying orchestration decisions and two theoretical techniques that omnisciently make optimal time varying orchestration decisions or an optimal static orchestration decision based on complete knowledge of the future. The efficacy of the new biomimetic scheme is assessed in terms of adaptiveness and versatility. The performance study uses representative classes of internet traffic stream behaviour, service user's behaviour, demand intensity, multiple services co-hosting as well as differentiated hosting fee schedule. The biomimetic orchestration scheme is compared with the classical and the theoretical optimal orchestration techniques in terms of revenue stream. This study reveals that the new server ensemble orchestration approach is adaptive in a widely varying external internet environments. The study also highlights the versatility of the biomimetic approach over the classical technique. The self-tuning scheme improves on the original performance. The energy-aware scheme is able to conserve significant energy with minimal revenue performance degradation. The simulation results also indicate that the new scheme is competitive or better than classical and static methods.

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / Em diversas situações, o volume de tarefas a serem cumpridas não pode ser realizado por um único robô. Assim, um campo que tem despertado crescente interesse é a investigação do comportamento de enxame de robôs de busca. Estratégias de cooperação e controle desse enxame devem ser consideradas para um desempenho eficiente do time de robôs. Existem várias técnicas clássicas em inteligência artificial que são capazes de resolver este problema. Neste trabalho um conjunto de técnicas bio-inspiradas, que engloba um modelo baseado em autômatos celulares com memória e feromônio invertido, foi considerado inicialmente para coordenar um time de robôs na tarefa de forrageamento para ambientes previamente conhecidos. Os robôs do time compartilham o mesmo ambiente, comunicando-se através do feromônio invertido, que é depositado por todos os agentes a cada passo de tempo, resultando em forças de repulsão e maior cobertura do ambiente. Por outro lado, o processo de retorno para o ninho é baseado no comportamento social observado no processo de evacuação de pedestres, resultando em forças de atração. Todos os movimentos deste processo são de primeira escolha e a resolução de conflitos proporciona uma característica não-determinista ao modelo. Posteriormente, o modelo base foi adaptado para a aplicação nas tarefas de coleta seletiva e busca e resgate. Os resultados das simulações foram apresentados em diferentes condições de ambiente. Além disso, parâmetros como quantidade e disposição da comida, posição dos ninhos e largura, constantes relacionadas ao feromônio, e tamanho da memória foram analisados nos experimentos. Em seguida, o modelo base proposto neste trabalho para tarefa de forrageamento, foi implementado usando os robôs e-Puck no ambiente de simulação Webots, com as devidas adaptações. Por fim, uma análise teórica do modelo investigado foi analisado através da teoria dos grafos e das filas. O método proposto neste trabalho mostrou-se eficiente e passível de ser implementado num alto nível de paralelismo e distribuição. Assim, o modelo torna-se interessante para a aplicação em outras tarefas robóticas, especialmente em problemas que envolvam busca multi-objetiva paralela. / In several situations, the volume of tasks to be accomplished can not be performed by a single robot. Thus, a field that has attracted growing interest is the behavior investigation of the search swarm robots. Cooperation and control strategies of this swarm should be considered for an efficient performance of the robot team. There are several classic techniques in artificial intelligence that are able to solve this problem. In this work a set of bio-inspired techniques, which includes a model based on cellular automata with memory and inverted pheromone, was initially considered to coordinate a team of robots in the task of foraging to previously known environments. The team's robots share the same environment, communicating through the inverted pheromone, which is deposited by all agents at each step of time, resulting in repulsive forces and increasing environmental coverage. On the other hand, the return process to the nest is based on the social behavior observed in the process of pedestrian evacuation, resulting in forces of attraction. All movements in this process are first choice and conflict resolution provides a non-deterministic characteristic to the model. Subsequently, the base model was adapted for the application in the tasks of selective collection and search and rescue. The results of the simulations were presented under different environment conditions. In addition, parameters such as amount and arrangement of food, nest position and width, pheromone-related constants, and memory size were analyzed in the experiments. Then, the base model proposed in this work for foraging task, was implemented using the e-Puck robots in the simulation environment Webots, with the appropriate adaptations. Finally, a theoretical analysis of the investigated model was analyzed through the graphs and queuing theory. The method proposed in this work proved to be efficient and capable of being implemented at a high level of parallelism and distribution. Thus, the model becomes interesting for the application in other robotic tasks, especially in problems that involve parallel multi-objective search. / Tese (Doutorado)

- / Doctorat en Sciences / info:eu-repo/semantics/nonPublished