In factory automation cameras and image processing algorithms can be used to inspect objects. This can decrease the faulty objects that leave the factory and reduce manual labour needed. A vision sensor is a system where camera and image processing is delivered together, and that only needs to be configured for the application that it is to be used for. Thus no programming knowledge is needed for the customer. In this Master’s thesis a way to make the configuration of a vision sensor even easier is developed and evaluated. The idea is that the customer knows his or her product much better than he or she knows image processing. The customer could take images of positive and negative samples of the object that is to be inspected. The algorithm should then, given these images, configure the vision sensor automatically. The algorithm that is developed to solve this problem is described step by step with examples to illustrate the problems that needed to be solved. Much of the focus is on how to compare two configurations to each other, in order to find the best one. The resulting configuration from the algorithm is then evaluated with respect to types of applications, computation time and representativeness of the input images.

The increasing use of electronic components in today&rsquo / s automobiles demands more powerful in-vehicle network communication protocols. FlexRay protocol, which is expected to be the de-facto standard in the near future, is a deterministic, fault tolerant and fast protocol designed for in vehicle communication. For proper operation of a FlexRay network the communication schedule needs to be computed and the nodes need to be configured before startup. Current software tools that are geared towards FlexRay only deal with the configuration process. The schedule needs to be computed by a network designer manually and it is necessary to input the designed schedule and the configurable parameters by hand. This thesis improves upon a previous scheduling software to automatically compute the network schedule, and then generate a universally acceptable FIBEX file that can be imported to available software tools to produce the necessary FlexRay node configuration files.

Multi-objective optimization is a growing field of interest for both theoretical and applied research, mostly due to the higher accuracy with which multi-objective problems (MOPs) model real- world scenarios. While single-objective models simplify real-world problems, MOPs can contain several (and often conflicting) objective functions to be optimized at once. This increased accuracy, however, comes at the expense of a higher difficulty that MOPs pose for optimization algorithms in general, and so a significant research effort has been dedicated to the development of approximate and heuristic algorithms. In particular, a number of proposals concerning the adaptation of evolutionary algorithms (EAs) for multi-objective problems can be seen in the literature, evidencing the interest they have received from the research community.This large number of proposals, however, does not mean that the full search power offered by multi- objective EAs (MOEAs) has been properly exploited. For instance, in an attempt to propose significantly novel algorithms, many authors propose a number of algorithmic components at once, but evaluate their proposed algorithms as monolithic blocks. As a result, each time a novel algorithm is proposed, several questions that should be addressed are left unanswered, such as (i) the effectiveness of individual components, (ii) the benefits and drawbacks of their interactions, and (iii) whether a better algorithm could be devised if some of the selected/proposed components were replaced by alternative options available in the literature. This component-wise view of MOEAs becomes even more important when tackling a new application, since one cannot antecipate how they will perform on the target scenario, neither predict how their components may interact. In order to avoid the expensive experimental campaigns that this analysis would require, many practitioners choose algorithms that in the end present suboptimal performance on the application they intend to solve, wasting much of the potential MOEAs have to offer.In this thesis, we take several significant steps towards redefining the existng algorithmic engineering approach to MOEAs. The first step is the proposal of a flexible and representative algorithmic framework that assembles components originally used by many different MOEAs from the literature, providing a way of seeing algorithms as instantiations of a unified template. In addition, the components of this framework can be freely combined to devise novel algorithms, offering the possibility of tailoring MOEAs according to the given application. We empirically demonstrate the efficacy of this component-wise approach by designing effective MOEAs for different target applications, ranging from continuous to combinatorial optimization. In particular, we show that the MOEAs one can tailor from a collection of algorithmic components is able to outperform the algorithms from which those components were originally gathered. More importantly, the improved MOEAs we present have been designed without manual assistance by means of automatic algorithm design. This algorithm engineering approach considers algorithmic components of flexible frameworks as parameters of a tuning problem, and automatically selects the component combinations that lead to better performance on a given application. In fact, this thesis also represents significant advances in this research direction. Primarily, this is the first work in the literature to investigate this approach for problems with any number of objectives, as well as the first to apply it to MOEAs. Secondarily, our efforts have led to a significant number of improvements in the automatic design methodology applied to multi-objective scenarios, as we have refined several aspects of this methodology to be able to produce better quality algorithms.A second significant contribution of this thesis concerns understanding the effectiveness of MOEAs (and in particular of their components) on the application domains we consider. Concerning combina- torial optimization, we have conducted several investigations on the multi-objective permutation flowshop problem (MO-PFSP) with four variants differing as to the number and nature of their objectives. Through thorough experimental campaigns, we have shown that some components are only effective when jointly used. In addition, we have demonstrated that well-known algorithms could easily be improved by replacing some of their components by other existing proposals from the literature. Regarding continuous optimization, we have conducted a thorough and comprehensive performance assessment of MOEAs and their components, a concrete first step towards clearly defining the state-of-the-art for this field. In particular, this assessment also encompasses many-objective optimization problems (MaOPs), a sub-field within multi-objective optimization that has recently stirred the MOEA community given its theoretical and practical demands. In fact, our analysis is instrumental to better understand the application of MOEAs to MaOPs, as we have discussed a number of important insights for this field. Among the most relevant, we highlight the empirical verification of performance metric correlations, and also the interactions between structural problem characteristics and the difficulty increase incurred by the high number of objectives.The last significant contribution from this thesis concerns the previously mentioned automatically generated MOEAs. In an initial feasibility study, we have shown that MOEAs automatically generated from our framework are able to consistently outperform the original MOEAs from where its components were gathered both for the MO-PFSP and for MOPs/MaOPs. The major contribution from this subset, however, regards continuous optimization, as we significantly advance the state-of-the-art for this field. To accomplish this goal, we have extended our framework to encompass approaches that are primarily used for this continuous problems, although the conceptual modeling we use is general enough to be applied to any domain. From this extended framework we have then automatically designed state-of- the-art MOEAs for a wide range of experimental scenarios. Moreover, we have conducted an in-depth analysis to explain their effectiveness, correlating the role of algorithmic components with experimental factors such as the stopping criterion or the performance metric adopted.Finally, we highlight that the contributions of this thesis have been increasingly recognized by the scientific community. In particular, the contributions to the research of MOEAs applied to continuous optimization are remarkable given that this is the primary application domain for MOEAs, having been extensively studied for a couple decades now. As a result, chapters from this work have been accepted for publication in some of the best conferences and journals from our field. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Les systèmes distribués collaboratifs, en particulier les systèmes pair-à-pair, forment l’infrastructure sous-jacente de nombreuses applications Internet, certaines parmi les plus populaires (ex : partage de fichiers, streaming multimédia). Ils se situent également à la base d’un ensemble de technologies émergentes telles que la blockchain et l’Internet des Objets. Le succès de ces systèmes repose sur la contribution volontaire, de la part des nœuds participants, aux ressources partagées (ex : bande passante réseau, puissance de calcul, stockage de données). Or ces nœuds sont des entités autonomes qui peuvent considérer comme plus avantageux de se comporter de manière égoïste, c’est-à-dire de refuser de collaborer. De tels comportements peuvent fortement impacter les performances et la stabilité opérationnelles du système cible. Prendre en compte et prévenir les comportements égoïstes des nœuds est donc essentiel pour garantir l’efficacité et la fiabilité des systèmes coopératifs. Cependant, cela exige du développeur, en dépit de la grande quantité de techniques et d’approches proposées dans la littérature, des connaissances multisectorielles approfondies. L'objectif de cette thèse est de concevoir et étudier de nouveaux outils théoriques et pratiques pour aider les concepteurs de systèmes distribués collaboratifs à faire face à des nœuds égoïstes. La première contribution, basée sur une analyse exhaustive de la littérature sur les comportements égoïstes dans les systèmes distribués, propose un modèle de classification pour identifier et analyser les comportements égoïstes les plus importants sur lesquels il est important de se concentrer lors de la conception d'un système coopératif. Dans la deuxième contribution, nous proposons RACOON, un framework pour la conception et la configuration de systèmes coopératifs résilients aux comportements égoïstes. Outre un ensemble de mécanismes d'incitation à la coopération, RACOON fournit une méthodologie semi-automatique d’intégration et de calibration de ces mécanismes de manière à garantir le niveau de performance souhaité. RACOON s’appuie sur une analyse du système cible fondée sur la théorie des jeux et sur des simulations pour prédire l’existence de nœuds égoïstes dans le système. RACOON a été étendu en un deuxième framework, RACOON++. Plus précis, plus flexible, RACOON++ offre également une plus grande facilité d'utilisation. Une dernière contribution, SEINE, propose un framework pour la modélisation et l'analyse des différents types de comportements égoïstes dans un système coopératif. Basé sur un langage dédié, développé pour décrire les scénarios de comportement égoïstes, SEINE fournit un support semi-automatique pour la mise en œuvre et l'étude de ces scénarios dans un simulateur choisi sur la base de l’état de l’art (PeerSim). / Cooperative distributed systems, particularly peer-to-peer systems, are the basis of several mainstream Internet applications (e.g., file-sharing, media streaming) and the key enablers of new and emerging technologies, including blockchain and the Internet of Things. Essential to the success of cooperative systems is that nodes are willing to cooperate with each other by sharing part of their resources, e.g., network bandwidth, CPU capability, storage space. However, as nodes are autonomous entities, they may be tempted to behave in a selfish manner by not contributing their fair share, potentially causing system performance degradation and instability. Addressing selfish nodes is, therefore, key to building efficient and reliable cooperative systems. Yet, it is a challenging task, as current techniques for analysing selfishness and designing effective countermeasures remain manual and time-consuming, requiring multi-domain expertise. In this thesis, we aim to provide practical and conceptual tools to help system designers in dealing with selfish nodes. First, based on a comprehensive survey of existing work on selfishness, we develop a classification framework to identify and understand the most important selfish behaviours to focus on when designing a cooperative system. Second, we propose RACOON, a unifying framework for the selfishness-aware design and configuration of cooperative systems. RACOON provides a semi-automatic methodology to integrate a given system with practical and finely tuned mechanisms to meet specified resilience and performance objectives, using game theory and simulations to predict the behaviour of the system when subjected to selfish nodes. An extension of the framework (RACOON++) is also proposed to improve the accuracy, flexibility, and usability of RACOON. Finally, we propose SEINE, a framework for fast modelling and evaluation of various types of selfish behaviour in a given cooperative system. SEINE relies on a domain-specific language for describing the selfishness scenario to evaluate and provides semi-automatic support for its implementation and study in a state-of-the-art simulator.

With the increasing need for larger and morepowerful distributed embedded systems comes the need formore tools to manage them. One area where such a tool isneeded is the internal communication for a distributed embeddedsystem. This project focuses on the development of such atool, namely the development of a communication middlewarewhose network configuration is automatically generated by apredefined network model. The middleware is responsible forrouting information on the network and relieves the applicationdeveloper of this responsibility. By using a multi-layer approach,additional functions can be easily implemented.The middleware demonstrated reasonable results for simplenetworks. However, it does not take into account the timingcharacteristics of the platform. A fact that currently preventsits use in most large networks. / I takt med att behovet av större och kraftfullaredistribuerade inbyggda system ökar, ökar även behovetav verktyg för att hantera dessa system. Ett område där ettsådant verktyg behövs är den interna kommunikationen i ettdistribuerade inbyggt system. Det här projektet fokuserar på att utveckla ett sådant verktyg, genom att utveckla en modellbaseratprogramvara som automatiskt konfigureras med kodgenereringutifrån en fördefinierad modell över nätverket. Eftersom programvaranhanterar datatransmissionen i nätverket, avlastasdetta ansvar från applikationens utvecklare. Genom att användaen modellbaserat metod kan framtida funktioner enkelt implementeras.Programvaran visade ett rimligt resultat för enklare nätverk.Men programvaran tar inte hänsyn till plattformens egenskaper.Något som förhindrar programvaran från att tillämpas på storskaliga nätverk. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Overlay networks have been the subject of significant research and practical interest recently in addressing the inefficiency and ossification of the current Internet. In this thesis, we cover various aspects of overlay network design, including overlay routing algorithms, overlay network assignment and multihomed overlay networks. We also examine the behavior of overlay networks under a wide range of network settings and identify several key factors that affect the performance of overlay networks. Based on these findings, practical design guidelines are also given. Specifically, this thesis addresses the following problems: 1) Dynamic overlay routing: We perform an extensive simulation study to investigate the performance of available bandwidth-based dynamic overlay routing from three important aspects: efficiency, stability, and safety margin. Based on the findings, we propose a hybrid routing scheme that achieves good performance in all three aspects. We also examine the effects of several factors on overlay routing performance, including network load, traffic variability, link-state staleness, number of overlay hops, measurement errors, and native sharing effects. 2) Virtual network assignment: We investigate the virtual network (VN) assignment problem in the scenario of network virtualization. Specifically, we develop a basic VN assignment scheme without reconfiguration and use it as the building block for all other advanced algorithms. Subdividing heuristics and adaptive optimization strategies are presented to further improve the performance. We also develop a selective VN reconfiguration scheme that prioritizes the reconfiguration for the most critical VNs. 3) Overlay network configuration tool for PlanetLab: We develop NetFinder, an automatic overlay network configuration tool to efficiently allocate PlanetLab resources to individual overlays. NetFinder continuously monitors the resource utilization of PlanetLab and accepts a user-defined overlay topology as input and selects the set of PlanetLab nodes and their interconnection for the user overlay. 4) Multihomed overlay network: We examine the effectiveness of combining multihoming and overlay routing from the perspective of an overlay service provider (OSP). We focus on the corresponding design problem and examine, with realistic network performance and pricing data, whether the OSP can provide a network service that is profitable, better (in terms of round-trip time), and less expensive than the competing native ISPs.

<p>Математички модели за естимацију тешко мерљивих величина називају<br />се софт сензорима. Процес формирања софт сензора није тривијалан и<br />квалитет естимације тешко мерљиве величине директно зависи од<br />начина формирања. Недостаци постојећих алгоритама за формирање<br />спречавају аутоматску конфигурацију софт сензора. У овом раду су<br />реализовани нови алгоритми који имају за сврху аутоматизацију<br />конфигурације софт сензора. Реализовани алгоритми решавају<br />проблеме проналаска оптималног сета улаза у софт сензор и кашњења<br />сваког од њих као и одабира структуре и начина обуке софт сензора<br />заснованих на вештачким неуронским мрежама са радијално базираним<br />функцијама.</p> / <p>Matematički modeli za estimaciju teško merljivih veličina nazivaju<br />se soft senzorima. Proces formiranja soft senzora nije trivijalan i<br />kvalitet estimacije teško merljive veličine direktno zavisi od<br />načina formiranja. Nedostaci postojećih algoritama za formiranje<br />sprečavaju automatsku konfiguraciju soft senzora. U ovom radu su<br />realizovani novi algoritmi koji imaju za svrhu automatizaciju<br />konfiguracije soft senzora. Realizovani algoritmi rešavaju<br />probleme pronalaska optimalnog seta ulaza u soft senzor i kašnjenja<br />svakog od njih kao i odabira strukture i načina obuke soft senzora<br />zasnovanih na veštačkim neuronskim mrežama sa radijalno baziranim<br />funkcijama.</p> / <p>Mathematical models that are used for estimation of variables that can not be<br />measured in real time are called soft sensors. Creation of soft sensor is a<br />complex process and quality of estimation depends on the way soft sensor is<br />created. Restricted applicability of existing algorithms is preventing automatic<br />configuration of soft sensors. This paper presents new algorithms that are<br />providing automatic configuration of soft sensors. Presented algorithms are<br />capable of determing optimal subset of soft sensor inputs and their time<br />delays, as well as optimal architecture and automatic training of the soft<br />sensors that are based on artificial radial basis function networks.</p>