Indoor Outdoor Scene Classification in Digital Images

In this paper, we present a method to classify real-world digital images into indoor and outdoor scenes. Indoor class consists of four groups: bedroom, kitchen, laboratory and library. Outdoor class consists of four groups: landscape, roads, buildings and garden. Application considers real-time system and has a dedicated data-set. Input images are pre-processed and converted into gray-scale and is re-sized to “128x128” dimensions. Pre-processed images are sent to “Gabor filters”, which pre-computes filter transfer functions, which are performed on Fourier domain. The processed signal is finally sent to GIST feature extraction and the images are classified using “kNN classifier”. Most of the techniques have been based on the use of texture and color space features. As of date, we have been able to achieve 80% accuracy with respect to image classification

An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from Motion Capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database

Visual-based SLAM configurations for cooperative multi-UAV systems with a lead agent: an observability-based approach

In this work, the problem of the cooperative visual-based SLAM for the class of multi-UA systems that integrates a lead agent has been addressed. In these kinds of systems, a team of aerial robots flying in formation must follow a dynamic lead agent, which can be another aerial robot, vehicle or even a human. A fundamental problem that must be addressed for these kinds of systems has to do with the estimation of the states of the aerial robots as well as the state of the lead agent. In this work, the use of a cooperative visual-based SLAM approach is studied in order to solve the above problem. In this case, three different system configurations are proposed and investigated by means of an intensive nonlinear observability analysis. In addition, a high-level control scheme is proposed that allows to control the formation of the UAVs with respect to the lead agent. In this work, several theoretical results are obtained, together with an extensive set of computer simulations which are presented in order to numerically validate the proposal and to show that it can perform well under different circumstances (e.g., GPS-challenging environments). That is, the proposed method is able to operate robustly under many conditions providing a good position estimation of the aerial vehicles and the lead agent as well.Peer ReviewedPostprint (published version

Nature-inspired algorithms for solving some hard numerical problems

Optimisation is a branch of mathematics that was developed to find the optimal solutions, among all the possible ones, for a given problem. Applications of optimisation techniques are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of methods to solve specific problems to its optimality. This dissertation focuses on the adaptation of two nature inspired algorithms that, based on optimisation techniques, are able to compute approximations for zeros of polynomials and roots of non-linear equations and systems of non-linear equations. Although many iterative methods for finding all the roots of a given function already exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results due to the problem of accumulating rounding errors, (b) good initial approximations to the roots for the algorithm converge, or (c) the computation of first or second order derivatives, which besides being computationally intensive, it is not always possible. The drawbacks previously mentioned served as motivation for the use of Particle Swarm Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are known, respectively, for their ability to explore high-dimensional spaces (not requiring good initial approximations) and for their capability to model complex problems. Besides that, both methods do not need repeated deflations, nor derivative information. The algorithms were described throughout this document and tested using a test suite of hard numerical problems in science and engineering. Results, in turn, were compared with several results available on the literature and with the well-known Durand–Kerner method, depicting that both algorithms are effective to solve the numerical problems considered.A Optimização é um ramo da matemática desenvolvido para encontrar as soluções óptimas, de entre todas as possíveis, para um determinado problema. Actualmente, são várias as técnicas de optimização aplicadas a problemas de engenharia, de informática e da indústria. Dada a grande panóplia de aplicações, existem inúmeros trabalhos publicados que propõem métodos para resolver, de forma óptima, problemas específicos. Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que, tendo como base técnicas de optimização, são capazes de calcular aproximações para zeros de polinómios e raízes de equações não lineares e sistemas de equações não lineares. Embora já existam muitos métodos iterativos para encontrar todas as raízes ou zeros de uma função, eles usualmente exigem: (a) deflações repetidas, que podem levar a resultados muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada iteração; (b) boas aproximações iniciais para as raízes para o algoritmo convergir, ou (c) o cálculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente intensivo, para muitas funções é impossível de se calcular. Estas desvantagens motivaram o uso da Optimização por Enxame de Partículas (PSO) e de Redes Neurais Artificiais (RNAs) para o cálculo de raízes. Estas técnicas são conhecidas, respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo boas aproximações iniciais) e pela sua capacidade de modelar problemas complexos. Além disto, tais técnicas não necessitam de deflações repetidas, nem do cálculo de derivadas. Ao longo deste documento, os algoritmos são descritos e testados, usando um conjunto de problemas numéricos com aplicações nas ciências e na engenharia. Os resultados foram comparados com outros disponíveis na literatura e com o método de Durand–Kerner, e sugerem que ambos os algoritmos são capazes de resolver os problemas numéricos considerados

Development, Implementation, and Validation of an Acoustic Emission-based Structural Health Monitoring System

Entwicklung, Implementierung und Validierung eines schallemissionsbasierten Strukturüberwachungssystems Die Strukturüberwachung eng. Structural Health Monitoring (SHM) ist ein grundlegender Prozess für die Kontrolle der Betriebssicherheit und Zuverlässigkeit von Strukturen und Bauteilen während des Betriebs. Ein Überwachungssystem soll die Strukturdegradation in einer frühen Phase erkennen und quantifizieren, um den Totalausfall zu verhindern und somit menschliche und finanzielle Verluste zu vermeiden. Mit der wachsenden Nachfrage nach kosteneffizienten und robusten Produkten ist SHM mit besonders hohen Anforderungen konfrontiert. Diese Arbeit befasst sich mit der Entwicklung, Implementierung und experimenteller Validierung eines innovativen SHM-Systems, das auf umfassende Weise Schädigungsmechanismen von unterschiedlichen Materialen erkennt, identifiziert und klassifiziert. Für in-situ-Strukturüberwachung können verschiedene Methoden angewendet werden. Hier wird die Schallemissionsanalyse eng. Acoustic Emission Technik (AET) eingesetzt. Acoustic Emission ist eine passive zerstörungsfreie Prüfund Überwachungsmethode. Sie basiert auf der Analyse elastischer Wellen, die durch freigesetzte Energie während mikrostrukturelle Änderungen wie z. B. Risse, Brüche, und Verschleiß entstehen. Unter Verwendung geeigneter Hardware und fortgeschrittener Signalverarbeitungsverfahren können diese Wellen kontinuierlich und in Echtzeit erfasst und analysiert werden. Die Leistungsfähigkeit und Zuverlässigkeit einer AE-basierten Schadensdiagnose sind stark abhängig von Material/Werkstoff, Konstruktion und möglichen Schadensszenarien. Der Fokus dieser Arbeit liegt daher auf der Entwicklung einer hocheffizienten und leicht anpassbaren Field Programmable Gate Array (FPGA)–basierten Messkette zum Abtasten und Erfassen der erzeugten AE-Signale. Neben der Verwendung von sehr leistungsfähiger Hardware ist eine zuverlässige Interpretation der AE Signale von zentraler Bedeutung. Deswegen erfordern die Entwicklung und Umsetzung von Multi-Level-Signalverarbeitungsansätzen und Mustererkennungsverfahren eine besondere Beachtung. Die experimentelle Validierung des entwickelten Systems erfolgt durch die Untersuchungen von drei verschiedenen Materialien/Strukturen: Verschleißfeste Metallbleche, Faserverbundwerkstoff Platten und elektrochemische Zelle. Aufgrund der Diversität der untersuchten Strukturen werden drei Verarbeitungsprozesse entwickelt. Die implementierten Algorithmen können AE-Signale erkennen, quantifizieren und qualifizieren, so dass AE-basierte Eigenschaften identifiziert und mit den entsprechenden AE-Quellen korreliert sind. Die Diagnose konzentriert sich hauptsächlich auf die Schadenserkennung (Merkmalsextraktion), Schadensabschätzung (Merkmalsauswahl) und Schadensklassifizierung unter Anwendung von Zeit-Frequenz-Analyse, statistischen Ansätzen und überwachten Klassifikationsverfahren. Die gewonnenen Ergebnisse zeigen eine bemerkbare Verbesserung der Identifizierung und Klassifizierung von Schadensmechanismen und beweisen die Effizienz des angewandten Multi-Level-Verarbeitungsansätze. Die vorgestellte Methodik ermöglicht eine automatisierte Zustandsüberwachung und stellt daher einen wichtigen Schritt in der Entwicklung von sicheren und zuverlässigen Strukturen dar.In engineering, Structural Health Monitoring (SHM) is an important field of study representing a fundamental process to control the longevity and reliability of structures during service. The objective of an SHM is to detect and quantify the structure degradation at an earlier stage. The acquisition of such information can contribute to prevention of total failure and hence avoiding human and financial losses becomes more possible. With the growing demands for cost-efficient and robust products, SHM is facing particularly high requirements. This thesis focuses on the development, implementation, and experimental validation of an innovative SHM system able to detect, identify, and classify in an extensive way damage mechanisms occurring in different materials. Several techniques can be applied for in situ health monitoring. In this work, Acoustic Emission Technique (AET) is used. Acoustic Emission is a passive nondestructive evaluation technique referring to the elastic waves generated by energy release during microstructural changes in the material. Those changes arise as a result of mechanical and environmental stresses. Monitoring of such a conversion can be continuously done in real-time using suitable hardware and advanced signal processing methods. The performance and reliability of an AE-based damage diagnosis approach are highly dependent on material, structure design and the damage scenarios. Therefore, a Field Programmable Gate Array (FPGA)-based measurement chains developed for sensing and acquiring the generated AE signals. This chain is easily adaptable to different structures and materials. It was therefore kept so far constant as possible throughout all tests conducted. Additionally to the use of highly efficient hardware that enhance the sensing quality and the data acquisition speed, the implementation of advanced filtering techniques with high processing accuracy is of central importance. The main objective of this thesis is to prove the function of the system developed to analyze AE waves under different damage scenarios. For this purpose, three different materials namely wear resistant plates, laminated composite plates, and electrochemical cells are investigated. Owing to the diversity of the studied materials, special attention is paid to the development and implementation of multilevel signal processing approach and pattern recognition methods. The processing chains are capable to detect, quantify and qualify the AE data, whereby AE-based characteristics are identified and correlated with the corresponding AE sources. The designed diagnosis methodology concentrates/focuses on damage detection (feature extraction), damage estimation (feature selection), and damage classification by using time-frequency analysis, multilevel statistical approaches, and supervised classification methods. The results obtained show a noticeable/remarkable enhancement of the identification and classification of damage mechanisms. The efficiency of applying multilevel processing approach is/(could be) thus proved. The methodology presented here, allows an automated structural health monitoring. Hereby, an important step forward in future development of safe and reliable structures is represented