Optical Communication System for Remote Monitoring and Adaptive Control of Distributed Ground Sensors Exhibiting Collective Intelligence

Comprehensive management of the battle-space has created new requirements in information management, communication, and interoperability as they effect surveillance and situational awareness. The objective of this proposal is to expand intelligent controls theory to produce a uniquely powerful implementation of distributed ground-based measurement incorporating both local collective behavior, and interoperative global optimization for sensor fusion and mission oversight. By using a layered hierarchal control architecture to orchestrate adaptive reconfiguration of autonomous robotic agents, we can improve overall robustness and functionality in dynamic tactical environments without information bottlenecks. In this concept, each sensor is equipped with a miniaturized optical reflectance modulator which is interactively monitored as a remote transponder using a covert laser communication protocol from a remote mothership or operative. Robot data-sharing at the ground level can be leveraged with global evaluation criteria, including terrain overlays and remote imaging data. Information sharing and distributed intelli- gence opens up a new class of remote-sensing applications in which small single-function autono- mous observers at the local level can collectively optimize and measure large scale ground-level signals. AS the need for coverage and the number of agents grows to improve spatial resolution, cooperative behavior orchestrated by a global situational awareness umbrella will be an essential ingredient to offset increasing bandwidth requirements within the net. A system of the type described in this proposal will be capable of sensitively detecting, tracking, and mapping spatial distributions of measurement signatures which are non-stationary or obscured by clutter and inter- fering obstacles by virtue of adaptive reconfiguration. This methodology could be used, for example, to field an adaptive ground-penetrating radar for detection of underground structures in urban environments and to detect chemical species concentrations in migrating plumes. Given is our research in these areas and a status report of our progress

Validation of protein models by a neural network approach

Background: The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results: In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE) which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods. Conclusion: In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the quality of protein structures. The use of AIDE in combination with other evaluation tools is expected to further enhance protein refinement effort

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions

Reliable Machine Learning Model for IIoT Botnet Detection

Due to the growing number of Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network’s hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model’s performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks

Deriving Protein Structures Efficiently by Integrating Experimental Data into Biomolecular Simulations

Proteine sind molekulare Nanomaschinen in biologischen Zellen. Sie sind wesentliche Bausteine aller bekannten Lebensformen, von Einzellern bis hin zu Menschen, und erfüllen vielfältige Funktionen, wie beispielsweise den Sauerstofftransport im Blut oder als Bestandteil von Haaren. Störungen ihrer physiologischen Funktion können jedoch schwere degenerative Krankheiten wie Alzheimer und Parkinson verursachen. Die Entwicklung wirksamer Therapien für solche Proteinfehlfaltungserkrankungen erfordert ein tiefgreifendes Verständnis der molekularen Struktur und Dynamik von Proteinen. Da Proteine aufgrund ihrer lichtmikroskopisch nicht mehr auflösbaren Größe nur indirekt beobachtet werden können, sind experimentelle Strukturdaten meist uneindeutig. Dieses Problem lässt sich in silico mittels physikalischer Modellierung biomolekularer Dynamik lösen. In diesem Feld haben sich datengestützte Molekulardynamiksimulationen als neues Paradigma für das Zusammenfügen der einzelnen Datenbausteine zu einem schlüssigen Gesamtbild der enkodierten Proteinstruktur etabliert. Die Strukturdaten werden dabei als integraler Bestandteil in ein physikbasiertes Modell eingebunden. In dieser Arbeit untersuche ich, wie sogenannte strukturbasierte Modelle verwendet werden können, um mehrdeutige Strukturdaten zu komplementieren und die enthaltenen Informationen zu extrahieren. Diese Modelle liefern eine effiziente Beschreibung der aus der evolutionär optimierten nativen Struktur eines Proteins resultierenden Dynamik. Mithilfe meiner systematischen Simulationsmethode XSBM können biologische Kleinwinkelröntgenstreudaten mit möglichst geringem Rechenaufwand als physikalische Proteinstrukturen interpretiert werden. Die Funktionalität solcher datengestützten Methoden hängt stark von den verwendeten Simulationsparametern ab. Eine große Herausforderung besteht darin, experimentelle Informationen und theoretisches Wissen in geeigneter Weise relativ zueinander zu gewichten. In dieser Arbeit zeige ich, wie die entsprechenden Simulationsparameterräume mit Computational-Intelligence-Verfahren effizient erkundet und funktionale Parameter ausgewählt werden können, um die Leistungsfähigkeit komplexer physikbasierter Simulationstechniken zu optimieren. Ich präsentiere FLAPS, eine datengetriebene metaheuristische Optimierungsmethode zur vollautomatischen, reproduzierbaren Parametersuche für biomolekulare Simulationen. FLAPS ist ein adaptiver partikelschwarmbasierter Algorithmus inspiriert vom Verhalten natürlicher Vogel- und Fischschwärme, der das Problem der relativen Gewichtung verschiedener Kriterien in der multivariaten Optimierung generell lösen kann. Neben massiven Fortschritten in der Verwendung von künstlichen Intelligenzen zur Proteinstrukturvorhersage ermöglichen leistungsoptimierte datengestützte Simulationen detaillierte Einblicke in die komplexe Beziehung von biomolekularer Struktur, Dynamik und Funktion. Solche computergestützten Methoden können Zusammenhänge zwischen den einzelnen Puzzleteilen experimenteller Strukturinformationen herstellen und so unser Verständnis von Proteinen als den Grundbausteinen des Lebens vertiefen

Guidage magnétique par champs de dipôles pour l’administration ciblée d’agents thérapeutiques

Les chimiothérapies modernes utilisées pour le traitement des cancers consistent souvent à l’injection systémique de molécules toxiques dont généralement une infime partie atteint la tumeur. Pour augmenter l’efficacité de ces traitements et réduire leurs effets secondaires, une solution consiste à guider magnétiquement des agents thérapeutiques afin de les diriger dans le réseau vasculaire, à partir du point d’injection directement vers la zone à traiter. Ceci peut être accompli en appliquant des champs et des gradients magnétiques de manière contrôlée sur les agents, qui sont alors soumis à des forces de propulsion permettant de les attirer à travers les bifurcations artérielles désirées. Pour le guidage de micro-agents, cette approche requiert des champs et des gradients magnétiques forts. Le champ permet de magnétiser les agents et doit idéalement être suffisamment fort pour les amener à saturation magnétique. Les gradients (variations spatiales du champ) peuvent alors induire des forces magnétiques de propulsion, mais doivent atteindre une certaine amplitude pour que ces forces soient suffisantes. Avec les limites technologiques actuelles, il est difficile de rencontrer ces deux critères pour le guidage de micro-agents à l’échelle humaine. Dans les tissus profonds, les méthodes existantes sont généralement limitées à des champs de <0.1T et des gradients de <400 mT/m, ou peuvent générer un champ assez fort pour obtenir une magnétisation à saturation mais au détriment de gradients faibles (e.g. <100mT/m ou typiquement <40 mT/m). Dans le cadre de ce projet de recherche, une nouvelle méthode de guidage magnétique, baptisée guidage par champs de dipôles, ou Dipole Field Navigation (DFN), est proposée et étudiée pour surmonter les limitations des méthodes précédentes pour le guidage de micro-agents. Contrairement aux autres méthodes de guidage magnétique, DFN bénéficie à la fois d’un champ magnétique fort et de gradients d’amplitudes élevées dans les tissus profonds chez l’humain. Ceci est accompli à l’aide de corps ferromagnétiques précisément positionnés autour du patient à l’intérieur d’un appareil clinique d’imagerie par résonance magnétique. Ces appareils génèrent un puissant champ magnétique, typiquement de 1.5-3 T, qui est suffisant pour atteindre la saturation magnétique des agents. Les corps ferromagnétiques ont pour effet de distordre le champ de l’appareil de sorte que des gradients excédant 400mT/m peuvent être générés à une profondeur de 10 cm dans le patient. Grâce aux distorsions complexes du champ autour de ceux-ci, il est théoriquement possible d’induire, dans une certaine mesure, les forces magnétiques nécessaires au guidage des agents le long de trajectoires prédéfinies dans le réseau vasculaire. Le paramétrage adéquat d’une disposition de corps ferromagnétiques, dont le nombre requis est a priori inconnu, est toutefois complexe et doit être effectué en fonction de la trajectoire vasculaire désirée, spécifique à chaque patient. Différentes contraintes reliées à l’environnement d’IRM, dont l’espace restreint à l’intérieur de l’appareil, doivent également être prises en compte. Ainsi, des modèles et algorithmes d’optimisation permettant de résoudre ce problème sont développés et présentés. Le fonctionnement de la méthode est validé in vitro par le guidage de particules à travers des réseaux ayant jusqu’à trois bifurcations consécutives avec un taux de ciblage supérieur à 90%. Il est démontré que la taille et la forme des corps ferromagnétiques peuvent être variées afin d’augmenter les capacités de génération de gradients. En particulier, les formes de disque et de demie-sphère sont identifiées comme étant les plus efficaces. Par ailleurs, l’environnement d’IRM n’étant typiquement pas compatible avec la présence de matériaux magnétiques, les effets des corps ferromagnétiques sur l’imagerie sont étudiés. Il est démontré que l’imagerie demeure possible, dans une certaine mesure malgré les distorsions, dans des régions spécifiques autour d’une sphère magnétisée à l’intérieur de l’appareil. La qualité des images obtenues dans ces conditions est suffisante pour permettre de valider le succès du ciblage. Ainsi, des vérifications périodiques du déroulement de l’intervention seraient possibles en éloignant momentanément le ou les corps ferromagnétiques du patient. D’autre part, à cause des forces magnétiques exercées sur ceux-ci, le nombre et la taille des corps ferromagnétiques doivent être limités afin de faciliter leur insertion et leur positionnement sécuritaire dans l’appareil. Bien que certaines trajectoires puissent nécessiter plusieurs corps ferromagnétiques de grande taille, un certain compromis doit donc être recherché par rapport à la qualité des gradients générés. Enfin, le potentiel de la méthode pour le guidage de microagents dans les tissus profonds chez l’humain est évalué en utilisant un modèle du réseau vasculaire du foie d’un patient. Les résultats indiquent que, pour des trajectoires vasculaires multi-bifurcations relativement complexes, un compromis est inévitable entre les amplitudes et la précision angulaire des gradients générés. Par exemple, des gradients d’environ 150mT/m ont été obtenus pour le guidage à travers trois bifurcations consécutives dans ce modèle, mais avec une erreur angulaire moyenne d’environ 20_. Finalement, les capacités de DFN à générer des gradients forts dépendent de nombreux paramètres, comme la complexité et la profondeur de la trajectoire vasculaire visée, mais peuvent, selon les conditions, surpasser grandement celles des méthodes existantes pour le guidage de micro-agents dans les tissus profonds. À la lumière des résultats présentés dans cette thèse, le potentiel de la méthode est prometteur et justifie la poursuite du projet, notamment vers la réalisation des premiers essais in vivo. À ce titre, différentes pistes de recherches et de travaux futurs sont discutées.----------ABSTRACT Modern chemotherapies used in cancer treatment often involve the systemic administration of toxic molecules, of which usually a tiny fraction reaches the tumor. To increase the efficacy of these treatments while significantly reducing their secondary effects, a solution consists in magnetically guiding therapeutic agents in the vascular network, from an injection point directly towards the diseased site. This can be accomplished by applying controlled combinations of magnetic fields and gradients on the agents, which are then subjected to propulsive directional forces that can be used to steer them through the desired arterial bifurcations. For the navigation of micro-agents, this approach requires both a strong magnetic field and high gradients. The field strength is required to magnetize the agents and is ideally high enough to bring them at saturation magnetization. The gradients (spatial variations of the field) can then induce magnetic propulsion forces, but must reach a certain magnitude so that these forces are sufficient. Because of current technological limitations, it is challenging to meet both criteria for the navigation of micro-agents at the human scale. In deep tissues, current methods are in fact usually limited to <0.1T fields and <400mT/m gradients, or can provide the field to reach saturation magnetization but at the expense of weak gradients (e.g. <100mT/m or typically <40 mT/m). In this research project, a new method dubbed Dipole Field Navigation (DFN) is proposed and studied to overcome the limitations of existing magnetic navigation methods for guiding micro-agents. Unlike other methods, DFN can provide both a strong magnetic field and high gradients in deep tissues for whole-body interventions. This is achieved by precisely positioning ferromagnetic cores around the patient inside a clinical magnetic resonance imaging scanner. Conventional scanners generate a strong magnetic field, typically of 1.5-3 T, which is sufficient to bring the agents at saturation magnetization. The ferromagnetic cores distort the scanner’s field such that gradients exceeding 400mT/m can be generated at a 10 cm depth inside the patient. Due to the complex distortion patterns around the cores, it is theoretically possible to induce, to a certain extent, the magnetic forces required for navigating agents along predefined vascular routes. The parameterization of core configurations, in which the required number of cores is a priori unknown, is however complex and must be performed according to the specific vasculature of a given patient. Several constraints related to the MRI environment must also be considered, such as the limited space inside the scanner. Therefore, models and optimization algorithms are developed and presented for solving this problem. The feasibility of the method is validated in vitro by guiding particles through up to three consecutive bifurcations, achieving a targeting efficiency of over 90%. It is shown that the size and shape of the cores can be varied to increase the capabilities of the method for generating gradients. In particular, discs and hemispheres are shown to be the most effective shapes. Moreover, the MRI environment typically no being compatible with the presence of magnetic materials, the effects of the cores on imaging are studied. It is shown that, despite distortions, imaging is still possible, to a certain extent, in specific regions around a magnetized sphere placed in the scanner. The images obtained in these conditions are of sufficient quality for targeting assessment. Thus, periodic validations of the procedure could be achieved by momentarily moving the cores away from the patient. On another hand, due to the potentially strong magnetic forces exerted on the cores, their number and sizes must be limited to ensure their safe insertion and positioning in the scanner. Consequently, although the navigation in some vascular routes may require several large ferromagnetic cores, a certain compromise must be made with respect to the quality of the gradients generated. Finally, the potential of the method for guiding micro-agents in a human vasculature in deep tissues is evaluated using the vascular model of a patient liver. The results indicate that, for relatively complex vascular routes having multiple bifurcations, a compromise is also required between the amplitudes and the angular precision of the gradients. For example, gradient strengths around 150mT/m were obtained for routes having three consecutive bifurcations in this model, but with an average angular error of about 20_. Overall, the capabilities of DFN for generating strong gradients depend on several parameters, such as the complexity and depth of the desired vascular route, but can in a range of cases greatly exceed those achievable by previous methods for the navigation of micro-agents in deep tissues. In view of the results presented in this thesis, the promising potential of DFN motivates the continuation of this project, in particular towards the first in vivo experiments. As such, different avenues of research and future works are discussed

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Protein structure and function relationships: application of computational approaches to biological and biomedical problems

In this work we have studied several cases by means of different computational approaches for the analysis of the structure and function relationships. In chapter 2 we describe a method, based on multiple neural networks, that we developed for evaluate the accuracy of predicted threedimensional protein structures. This tool has been used in different studies described in this work, in which the prediction of the 3D structure of the protein under study, has been necessary. In chapter 3, the interaction among a new class of natural sweeteners (steviol glycosides) and the human sweet taste receptor, has been analyzed by means of an insilico docking study, which allowed to identify the preferential binding site for the steviol glycosides. In chapter 4 the relationship between the dynamical properties and the function of some psychrophilic enzyme has been studied. A comparative study (psychrophile vs mesophile) of the thermodynamic properties of two different enzymes belonging to the elastases and the uracilDNAglycosylases families has been done. This study, carried out with molecular dynamic simulations, revealed that the low temperature adaptation is related to the different flexibility of the psychrophilic compared to the mesophilic enzyme. In chapter 5, we have studied the structural and functional impact of point mutations on three different proteins which are involved in serious rare diseases which cause grave metabolic disorders