Das drahtlose Sensornetzwerk (WSN) ist eine Technologie, die Umgebungsbedingungen oder physikalische Parameter misst, weiterleitet und per Fernüberwachung zur Verfügung stellt. Normalerweise werden die Sensorknoten, die diese Netzwerke bilden, von Batterien gespeist. Diese sollen aus verschiedenen Gründen nicht mehr verwendet werden, sondern es wird auf eine eigenständige Stromversorgung gesetzt. Dies soll den aufwendigen Austausch und die Wartung minimieren. Energy Harvesting kann mit den Knoten verwendet werden, um die Batterien zu unterstützen und die Lebensdauer der Netzwerke zu verlängern. Aufgrund der hohen Leistungsdichte der Solarenergie im Vergleich zu verschiedenen anderen Umweltenergien sind Solarzellen die am häufigsten eingesetzten Wandler, allerdings stellt die schwankende und intermittierende Natur der Solarenergie eine Herausforderung dar, einen funktionalen und zuverlässigen Sensorknoten zu versorgen. Um den Sensorknoten effektiv zu betreiben, sollte sein Energieverbrauch sinnvoll gesteuert werden. Ein interessanter Ansatz zu diesem Zweck ist die Steuerung der Aktivitäten des Knotens in Abhängigkeit von der zukünftig verfügbaren Energie. Dies erfordert eine Vorhersage der wandelbaren Sonnenenergie für die kommenden Betriebszeiten einschließlich der freien Zeiten der Sonne. Einige Vorhersagealgorithmen wurden mit stochastischen und statistischen Prinzipien sowie mit Methoden der künstlichen Intelligenz (KI) erstellt. Durch diese Algorithmen bleibt ein erheblicher Vorhersagefehler von 5-70%, der den zuverlässigen Betrieb der Knoten beeinträchtigt. Beispielsweise verwenden die stochastischen Methoden einen diskreten Energiezustand, der meist nicht zu den tatsächlichen Messwerten passt. Die statistischen Methoden verwenden einen Gewichtungsfaktor für die zuvor registrierten Messwerte. Daher sind sie nur geeignet, um Energieprofile bei konstanten Wetterbedingungen vorherzusagen. KI-Methoden erfordern große Beobachtungen im Trainingsprozess, die den benötigten Speicherplatz erhöhen. Dementsprechend ist die Leistung hinsichtlich der Vorhersagegenauigkeit dieser Algorithmen nicht ausreichend. In dieser Arbeit wird ein Vorhersagealgorithmus mit einem neuronalen Netzwerk entwickelt und eingebunden in einen Mikrocontroller, um die Verwaltung des Energieverbrauchs von solarzellengesteuerten Sensorknoten zu optimieren. Das verwendete neuronale Netzwerk wurde mit einer Kombination aus meteorologischen und statistischen Eingangsparametern realisiert. Dies hat zum Ziel, die erforderlichen Designkriterien für Sensorknoten zu erfüllen und eine Leistung zu erreichen, die in ihrer Genauigkeit die Leistung der oben genannten traditionellen Algorithmen übersteigt. Die Vorhersagegenauigkeit die durch den Korrelationskoeffizienten repräsentiert wird, wurde für das entwickelte neuronale Netzwerk auf 0,992 bestimmt. Das genaueste traditionelle Netzwerk erreicht nur einen Wert von 0,963. Das entwickelte neuronale Netzwerk wurde in einen Prototyp eines Sensorknotens integriert, um die Betriebszustände oder -modi über einen Simulationszeitraum von einer Woche anzupassen. Während dieser Zeit hat der Sensorknoten 6 Stunden zusätzlich im Normalbetrieb gearbeitet. Dies trug dazu bei, eine effektive Nutzung der verfügbaren Energie um ca. 3,6% besser zu erfüllen als das genaueste traditionelle Netz. Dadurch wird eine längere Lebensdauer und Zuverlässigkeit des Sensorknotens erreicht. / Wireless Sensor Network (WSN) is a technology that measures an environmental or physical parameters in order to use them by decision makers with a possibility of remote monitoring. Normally, sensor nodes that compose these networks are powered by batteries which are no longer feasible, especially when they used as fixed and standalone power source. This is due to the costly replacement and maintenance. Ambient energy harvesting systems can be used with these nodes to support the batteries and to prolong the lifetime of these networks. Due to the high power density of solar energy in comparison with different environmental energies, solar cells are the most utilized harvesting systems. Although that, the fluctuating and intermittent nature of solar energy causes a real challenge against fulfilling a functional and reliable sensor node. In order to operate the sensor node effectively, its energy consumption should be well managed. One interesting approach for this purpose is to control the future node’s activities according to the prospective energy available. This requires performing a prior prediction of the harvestable solar energy for the upcoming operation periods including the sun’s free times. A few prediction algorithms have been created using stochastic and statistical principles as well as artificial intelligence (AI) methods. A considerable prediction error of 5-70% is realized by these algorithms affecting the reliable operation of the nodes. For example, the stochastic ones use a discrete energy states which are mostly do not fit the actual readings. The statistical methods use a weighting factors for the previous registered readings. Thus, they are convenient only to predict energy profiles under consistent weather conditions. AI methods require large observations to be used in the training process which increase the memory space needed. Accordingly, the performance concerning the prediction accuracy of these algorithms is not sufficient. In this thesis, a prediction algorithm using a neural network has been proposed and implemented in a microcontroller for managing energy consumption of solar cell driven sensor nodes. The utilized neural network has been developed using a combination of meteorological and statistical input parameters. This is to meet a required design criteria for the sensor nodes and to fulfill a performance exceeds in its accuracy the performance of aforementioned traditional algorithms. The prediction accuracy represented by the correlation coefficient has been registered for the developed neural network to be 0.992, which increases the most accurate traditional network which has a value 0.963. The developed neural network has been embedded into a sensor node prototype to adjust the operating states or modes over a simulation period of one week. During this period, the sensor node has worked 6 hours more towards normal operation mode. This in its role helped to fulfill an effective use of available energy approximately 3.6% better than the most accurate traditional network. Thus, longer lifetime and more reliable sensor node.

Denna uppsats presenterar resultaten av en kvalitativ undersökning som syftar till att ge en djupare förståelse för användningen av AI inom radiologi, dess framtida påverkan på yrket och hur det används idag. Genom att genomföra tre intervjuer med personer som arbetar inom radiologi, har datainsamlingen fokuserat på att identifiera de positiva och negativa aspekterna av AI i radiologi, samt dess potentiella konsekvenser på yrket. Resultaten visar på en allmän acceptans för AI inom radiologi och dess förmåga att förbättra diagnostiska processer och effektivisera arbetet. Samtidigt finns det en viss oro för att AI kan ersätta människor och minska behovet av mänskliga bedömningar. Denna uppsats ger en grundläggande förståelse för hur AI används inom radiologi och dess möjliga framtida konsekvenser. / This essay presents the results of a qualitative study aimed at gaining a deeper understanding of the use of artificial intelligence (AI) in radiology, its potential impact on the profession and how it’s used today. By conducting three interviews with individuals working in radiology, data collection focused on identifying the positive and negative aspects of AI in radiology, as well as its potential consequences on the profession. The results show a general acceptance of AI in radiology and its ability to improve diagnostic processes and streamline work. At the same time, there is a certain concern that AI may replace humans and reduce the need for human judgments. This report provides a basic understanding of how AI is used in radiology and its possible future consequences.

Les Composés Organiques Volatils biogènes (COVB) sont des molécules issues du métabolisme secondaire des végétaux, dont l'émission peut être modulée par les conditions environnementales. Parmi ces composés, l'isoprène a été très étudié du fait des flux d'émission important et de son implication dans la photochimie troposphérique. Cependant, les mécanismes d'action des facteurs environnementaux sont encore mal connus, et notamment celui de l'impact du stress hydrique. Dans le contexte de changements climatiques, ce type de stress va particulièrement impacter la région méditerranéenne.Nous avons étudié l'impact du stress hydrique sur les émissions d'isoprène de Quercus pubescens Willd. Cette espèce, très présente dans cette région, serait la seconde source d'isoprène en Europe.Deux étude ont été menées.La première, effectuée en pépinière, a consisté à appliquer un stress hydrique modéré et sévère d'avril à octobre. Une augmentation des émissions d'isoprène des arbres modérément stressés a été observée alors qu'il n'y a eu aucune modification des émissions pour les arbres très stressés.La seconde a consisté à faire un suivi saisonnier du stress hydrique au sein d'une chênaie pubescente. Un stress hydrique amplifié a été appliqué par un système d'exclusion de pluie, permettant de diminuer la quantité de pluie de 30%. Nous avons observé que le stress hydrique amplifié augmentait les facteurs d'émission d'isoprène des arbres.Cette base de données a permis le développement, par Réseau de Neurones Artificiels (RNA), d'un algorithme d'émission d'isoprène. Nous avons ainsi mis en évidence l'impact prédominant du contenu en eau du sol sur les émissions d'isoprène. / Biogenic Volatile Organic Compounds (BVOC) are plants secondary-metabolism-molecules. Their emissions are modulated by environmental conditions. Among these compounds, isoprene has been particularly studied due to its intense emission fluxes as well as its major contribution to tropospheric photochemistry. However, the impacts of environmental constraints on isoprene emission are still not yet well known. In particular, water stress impact is still a contradictory issue. In a world facing multiple climatic changes, models expect this kind of stress to hit Mediterranean area.This work focused on the impact of water stress on Quercus pubescens Willd. isoprene emissions. This species, widely spread in this area, is the second isoprene emitter in Europe.Two types of study were used.First, during an experimental carried out in a nursery, Q. pubescens saplings were grown under a moderate and severe water stress from April to October. This experimentation highlighted an increase of isoprene emissions for mid-stressed trees, while no emission changes were observed for the highly stressed trees.Secondly, an experimentation was conducted on a pubescent oak forest with trees acclimated to long lasting stress periods. We followed, during a whole season, the impact, on isoprene emissions, of a water stress created by artificially reducing 30% of the rains by means of a specific deploying roof. Isoprene emission factors were observed to increase under water stress.The database thus obtained was used in an Artificial Neural Network (ANN) to develop an appropriate isoprene emission algorithm. We underlined the predominant impact of soil water content on isoprene emissions.

The present work focuses on the system identification method of aerodynamic parameter estimation which is used to calculate the stability and control derivatives required for aircraft flight mechanics. A new rotorcraft parameter estimation technique is proposed which uses a type of artificial neural network (ANN) called radial basis function network (RBFN). Rotorcraft parameter estimation using ANN is an unexplored research topic and the earlier works in this area have used the output error, equation error and filter error methods which are conventional parameter estimation methods. However, the conventional methods require an accurate non-linear rotorcraft simulation model which is not required by the ANN based method. The application of RBFN overcomes the drawbacks of multilayer perceptron (MLP) based delta method of parameter estimation and gives satisfactory results at either end of the ordered set of estimates. This makes the RBFN based delta method for parameter estimation suitable for rotorcraft studies, as both transition and high speed flight regime characteristics can be studied. The RBFN based delta method for parameter estimation is used for computation of aerodynamic parameters from both simulated and real time flight data. The simulated data is generated from an 8-DoF non-linear simulation model based on the Level-1 criteria of rotorcraft simulation modeling. The generated simulated data is used for computation of the quasi-steady and the time-variant stability and control parameters for different flight conditions using the RBFN based delta method. The performance of RBFN based delta method is also analyzed in the presence of state and measurement noise as well as outliers. The established methodology is then applied to compute parameters directly from real time flight test data for a BO 105 S123 helicopter obtained from DLR (German Aerospace Center). The parameters identified using the RBFN based delta method are compared with the identified values for the BO 105 helicopter from published literature which have used conventional parameter estimation techniques for parameter estimation using a 6-DoF and a 9-DoF rotorcraft simulation model. Finally, the estimated parameters are verified from the flight data generated by a frequency sweep pilot control input for assessing the predictive capability of the RBFN based delta method. Since the approach directly computes the parameters from flight data, it can be used for a reliable description of the higher frequency range, which is needed for high bandwidth flight control and in-flight simulation.

As flight flutter testing on an airplane progresses to high dynamic pressures and high Mach number region, it becomes very difficult for engineers to predict the level of the remaining stability in a flutter-prone mode and flutter-prone mechanism when response data is infested with uncertainty. Uncertainty and ensuing scatter in modal data trends always leads to diminished confidence amidst the possibility of sudden decrease in modal damping of a flutter-prone mode. Since the safety of the instrumented prototype and the crew cannot be compromised, a large number of test-points are planned, which eventually results in increased development time and associated costs. There has been a constant demand from the flight test community to improve understanding of the con-ventional methods and develop new methods that could enable ground-station engineers to make better decision with regard to flutter susceptibility of structural components on the airframe. An extensive literature survey has been done for many years to take due cognizance of the ground realities, historical developments, and the state of the art. Besides, discussion on the results of a survey carried on occurrences of flutter among general aviation airplanes has been provided at the very outset. Data for research comprises results of Computational Aero elasticity Analysis (CAA) and limited Flight Flutter Tests (FFTs) on two slightly different structural designs of the airframe of a supersonic fixed-wing airplane. Detail discussion has been provided with regard to the nature of the data, the certification requirements for an airplane to be flutter-free in the flight-envelope, and the adopted process of flight flutter testing. Four flutter-prone modes - with two modes forming a symmetric bending-pitching flutter mechanism and the other two forming an anti-symmetric bending-pitching mechanism have been identified based on the analysis of computational data. CAA and FFT raw data of these low frequency flutter modes have been provided followed by discussion on its quality and flutter susceptibility of the critical mechanisms. Certain flight-conditions, at constant altitude line and constant Mach number lines, have been chosen on the basis of availability of FFT data near the same flight conditions. Modal damping is often a highly non-linear function of airspeed and scatter in such trends of modal damping can be very misleading. Flutter margin (FM) parameter, a measure of the remaining stability in a binary flutter mechanism, exhibits smooth and gradual variation with dynamic pressure. First, this thesis brings out the established knowledge of the flutter margin method and marks the continuing knowledge-gaps, especially about the applicable form of the flutter margin prediction equation in transonic region. Further theoretical developments revealed that the coefficients of this equation are flight condition depended to a large extent and the equation should be only used in small ‘windows’ of the flight-envelope by making the real-time flutter susceptibility assessment ‘progressive’ in nature. Firstly, it is brought out that lift curve slope should not be treated as a constant while using the prediction equation at constant altitudes on an airplane capable of transonic flight. Secondly, it was realized that the effect of shift in aerodynamic canter must be considered as it causes a ‘transonic-hump’. Since the quadratic form of flutter margin prediction equation developed 47 years ago, does not provide a valid explanation in that region, a general equation has been derived. Furthermore, flight test data from only supersonic region must be used for making acceptable predictions in supersonic region. The ‘ameliorated’ flutter margin prediction equation too provides bad predictions in transonic region. This has been attributed to the non-validity of quasi-steady approximation of aerodynamic loads and other additional non-linear effects. Although the equation with effect of changing lift curve slope provides inconsistent predictions inside and near the region of transonic-hump, the errors have been acceptable in most cases. No consistent congruency was discovered to some earlier reports that FM trend is mostly parabolic in subsonic region and linear in supersonic region. It was also found that the large scatter in modal frequencies of the constituent modes can lead to scatter in flutter margin values which can render flutter margin method as ineffective as the polynomial fitting of modal damping ratios. If the modal parameters at a repeated test-point exhibit Gaussian spread, the distribution in FM is non-Gaussian but close to gamma-type. Fifteen uncertainty factors that cause scatter in modal data during FFT and factor that cause modelling error in a computational model have been enumerated. Since scatter in modal data is ineluctable, it was realized that a new predictive tool is needed in which the probable uncertainty can be incorporated proactively. Given the recent shortcomings of NASA’s flutter meter, the neural network based approach was recognized as the most suitable one. MLP neural network have been used successfully in such scenarios for function approximation through input-output mapping provided the domains of the two are remain finite. A neural network requires ample data for good learning and some relevant testing data for the evaluation of its performance. It was established that additional data can be generated by perturbing modal mass matrix in the computational model within a symmetric bound. Since FFT is essentially an experimental process, it was realized that such bound should be obtained from experimental data only, as the full effects of uncertainty factors manifest only during flight tests. The ‘validation FFT program’, a flight test procedure for establishing such bound from repeated tests at five diverse test-points in safe region has been devised after careful evaluation of guide-lines and international practice. A simple statistical methodology has been devised to calculate the bound-of-uncertainty when modal parameters from repeated tests show Gaussian distribution. Since no repeated tests were conducted on the applicable airframe, a hypothetical example with compatible data was considered to explain the procedure. Some key assumptions have been made and discussion regarding their plausibility has been provided. Since no updated computational model was made available, the next best option of causing random variation in nominal values of CAA data was exercised to generate additional data for arriving at the final form of neural network architecture and making predictions of damping ratios and FM values. The problem of progressive flutter susceptibility assessment was formulated such that the CAA data from four previous test-points were considered as input vectors and CAA data from the next test-point was the corresponding output. General heuristics for an optimal learning performance has been developed. Although, obtaining an optimal set of network parameters has been relatively easy, there was no single set of network parameters that would lead to consistently good predictions. Therefore some fine-tuning, of network parameters about the optimal set was often needed to achieve good generalization. It was found that data from the four already flown test-points tend to dominate network prediction and the availability of flight-test data from these previous test-points within the bound about nominal is absolutely important for good predictions. The performance improves when all the five test-points are closer. If above requirements were met, the predictive performance of neural network has been much more consistent in flutter margin values than in modal damping ratios. A new algorithm for training MLP network, called Particle Swarm Optimization (PSO) has also been tested. It was found that the gradient descent based algorithm is much more suitable than PSO in terms of training time, predictive performance, and real-time applicability. In summary, the main intellectual contributions of this thesis are as follows: • Realization of that the fact that secondary causes lead incidences of flutter on airplanes than primary causes. • Completion of theoretical understanding of data-based flutter margin method and flutter margin prediction equation for all ranges of flight Mach number, including the transonic region. • Vindication of the fact that including lift-curve slope in the flutter margin pre-diction equation leads to improved predictions of flutter margins in subsonic and supersonic regions and progressive flutter susceptibility assessment is the best way of reaping benefits of data-based methods. • Explanation of a plausible recommended process for evaluation of uncertainty in modal damping and flutter margin parameter. • Realization of the fact that a MLP neural network, which treats a flutter mechanism as a stochastic non-linear system, is a indeed a promising approach for real-time flutter susceptibility assessment.

Multidisciplinary design optimization (MDO) can be used in computer aided engineering (CAE) to efficiently improve and balance performance of automotive structures. However, large-scale MDO is not yet generally integrated within automotive product development due to several challenges, of which excessive computing times is the most important one. In this thesis, a metamodel-based MDO process that fits normal company organizations and CAE-based development processes is presented. The introduction of global metamodels offers means to increase computational efficiency and distribute work without implementing complicated multi-level MDO methods. The presented MDO process is proven to be efficient for thickness optimization studies with the objective to minimize mass. It can also be used for spot weld optimization if the models are prepared correctly. A comparison of different methods reveals that topology optimization, which requires less model preparation and computational effort, is an alternative if load cases involving simulations of linear systems are judged to be of major importance. A technical challenge when performing metamodel-based design optimization is lack of accuracy for metamodels representing complex responses including discontinuities, which are common in for example crashworthiness applications. The decision boundary from a support vector machine (SVM) can be used to identify the border between different types of deformation behaviour. In this thesis, this information is used to improve the accuracy of feedforward neural network metamodels. Three different approaches are tested; to split the design space and fit separate metamodels for the different regions, to add estimated guiding samples to the fitting set along the boundary before a global metamodel is fitted, and to use a special SVM-based sequential sampling method. Substantial improvements in accuracy are observed, and it is found that implementing SVM-based sequential sampling and estimated guiding samples can result in successful optimization studies for cases where more conventional methods fail.

No description available.

蓄電池は携帯機器，電気自動車をはじめ，自然エネルギー有効利用に至るまで広範囲に利用され，その重要性はますます高まっている。これら機器の使用時間や特性は蓄電池の特性に大きく依存することから，電池自体の特性改善に加え，劣化を診断してより効率的に電池を運用することが求められている。本論文は，非線形情報処理を得意とする人工知能を用いた2次電池の劣化診断法を開発し，エネルギーの有効利用に資する技術を確立した。機器動作時の電池電圧・電流波形と電池劣化特性との関連性を，人工知能を用い学習することにより，機器稼働時に電池の劣化を診断することができる。なお，この関連性は非線形で複雑であるが，非線形分析を得意とする人工知能は劣化診断に適している。学習には時間を要するものの，診断は短時間になし得ることから，提案法は稼働時劣化診断に適している。本論文では，この特徴を生かし，電池の等価回路（ECM）を導出し，充電率（SOC），容量維持率（SOH）を推定している。また，本論文では現在産業応用分野で用いられている，リチウムイオン電池，ニッケル水素電池，鉛蓄電池を対象とし，提案法はあらゆる電池使用機器に応用可能である。また，提案法を電池状態監視装置（BMU）や，マイコンなどを用いた組み込みシステムに応用可能とし，実証している。以上のことから，本論文は，新たな蓄電池の劣化診断法の確立し，その有効性を確認している。 / The importance of rechargeable batteries nowadays is increasing from the portable electronic devices and solar energy industry up to the development of new EV models. The rechargeable batteries have a crucial role in the storage system, mostly in mobile applications and transportation, because the period of its usage and the flexibility of the function are determined by the battery. Due to the black box approach of the ANN it is possible to connect the complex physical phenomenon with a specific physical meaning expressed with a nonlinear logic between inputs and output. Using specific input data to relate with the desired output, makes possible to create a pattern connection with input and output. This ability helps to estimate in real time the desired outputs, behaviors, phenomes and at the same time it can be used as a real time diagnosis method. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

<p dir="ltr">In response to the growing significance of wood as a sustainable resource and the challenges within the wood products industry, there is a pressing need for innovation and collaboration across sectors. This study underscores the importance of mapping the wood products industry to gain a comprehensive understanding of material flows, which is essential for educational and research endeavors. The findings aim to uncover new economic opportunities and advocate for sustainable resource management. To address the complexities of the wood products industry, we developed a Generic Map, including a version tailored for the U.S. hardwood sector. Moreover, Dive-in Chain Maps were introduced to elaborate on the main production chains: Sawmill (I), Veneer Mill (II), Reconstituted Wood Manufacturing (III), and Pulp and Paper Mill (IV).</p><p dir="ltr">The study suggests four strategies to augment the value of hardwood through production, design, material modification, and by-products management. We showcased some strategies through two case studies.</p><p dir="ltr">The first focuses on Cross-laminated Timber (CLT), demonstrating value addition to hardwood. We conducted a literature review on the availability of raw materials in the US region and evaluated their performance across various stages of laboratory testing. This was followed by evaluating the feasibility and environmental effects of utilizing yellow poplar for CLT production. Additionally, we compared the Life Cycle Analysis (LCA) outcomes of yellow poplar CLT with those of traditional softwood CLT. This comparison aims to provide further insights for developing future by-product management or end-of-life strategies.</p><p dir="ltr">The second case study examines thermal modification, proposing an innovative method for efficient thermal treatment and employing an Artificial Neural Network (ANN) model to analyze the correlation between temperature, duration, and color change. We also compared the physical and mechanical properties of surface thermally treated samples to those of traditionally treated ones, discussing how different thermal treatments affect material properties.</p><p dir="ltr">Our findings illuminate the path for effective material flow and utilization, unveiling avenues for innovation and the creation of high-value products. Furthermore, the study provides strategies for waste minimization and informed end-of-life decision-making, thereby enhancing circularity and sustainability in the wood products industry.</p>

Depuis son introduction en 1982, la carte auto-organisatrice de Kohonen (Self-Organizing Map : SOM) a prouvé ses capacités de classification et visualisation des données multidimensionnelles dans différents domaines d’application. Les implémentations matérielles de la carte SOM, en exploitant le taux de parallélisme élevé de l’algorithme de Kohonen, permettent d’augmenter les performances de ce modèle neuronal souvent au détriment de la flexibilité. D’autre part, la flexibilité est offerte par les implémentations logicielles qui quant à elles ne sont pas adaptées pour les applications temps réel à cause de leurs performances temporelles limitées. Dans cette thèse nous avons proposé une architecture matérielle distribuée, adaptable, flexible et extensible de la carte SOM à base de NoC dédiée pour une implantation matérielle sur FPGA. A base de cette approche, nous avons également proposé une architecture matérielle innovante d’une carte SOM à structure croissante au cours de la phase d’apprentissage / Since its introduction in 1982, Kohonen’s Self-Organizing Map (SOM) showed its ability to classify and visualize multidimensional data in various application fields. Hardware implementations of SOM, by exploiting the inherent parallelism of the Kohonen algorithm, allow to increase the overall performances of this neuronal network, often at the expense of the flexibility. On the other hand, the flexibility is offered by software implementations which on their side are not suited for real-time applications due to the limited time performances. In this thesis we proposed a distributed, adaptable, flexible and scalable hardware architecture of SOM based on Network-on-Chip (NoC) designed for FPGA implementation. Moreover, based on this approach we also proposed a novel hardware architecture of a growing SOM able to evolve its own structure during the learning phase