A Decentralized Architecture for Active Sensor Networks

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Discrete Wavelet Methods for Interference Mitigation: An Application To Radio Astronomy

The field of wavelets concerns the analysis and alteration of signals at various resolutions. This is achieved through the use of analysis functions which are referred to as wavelets. A wavelet is a signal defined for some brief period of time that contains oscillatory characteristics. Generally, wavelets are intentionally designed to posses particular qualities relevant to a particular signal processing application. This research project makes use of wavelets to mitigate interference, and documents how wavelets are effective in the suppression of Radio Frequency Interference (RFI) in the context of radio astronomy. This study begins with the design of a library of smooth orthogonal wavelets well suited to interference suppression. This is achieved through the use of a multi-parameter optimization applied to a trigonometric parameterization of wavelet filters used for the implementation of the Discrete Wavelet Transform (DWT). This is followed by the design of a simplified wavelet interference suppression system, from which measures of performance and suitability are considered. It is shown that optimal performance metrics for the suppression system are that of Shannon’s entropy, Root Mean Square Error (RMSE) and normality testing using the Lilliefors test. From the application of these heuristics, the optimal thresholding mechanism was found to be the universal adaptive threshold and entropy based measures were found to be optimal for matching wavelets to interference. This in turn resulted in the implementation of the wavelet suppression system, which consisted of a bank of matched filters used to determine which interference source is present in a sampled time domain vector. From this, the astronomy based application was documented and results were obtained. It is shown that the wavelet based interference suppression system outperforms existing flagging techniques. This is achieved by considering measures of the number of sources within a radio-image of the Messier 83 (M83) galaxy and the power of the main source in the image. It is shown that designed system results in an increase of 27% in the number of sources in the recovered radio image and a 1.9% loss of power of the main source

Multiresolutional Fault-Tolerant Sensor Integration and Object Recognition in Images.

This dissertation applies multiresolution methods to two important problems in signal analysis. The problem of fault-tolerant sensor integration in distributed sensor networks is addressed, and an efficient multiresolutional algorithm for estimating the sensors\u27 effective output is proposed. The problem of object/shape recognition in images is addressed in a multiresolutional setting using pyramidal decomposition of images with respect to an orthonormal wavelet basis. A new approach to efficient template matching to detect objects using computational geometric methods is put forward. An efficient paradigm for object recognition is described

Fracture mechanics of volcanic eruptions

Seismology is a key tool in the forecasting of volcanic eruptions. The onset of an eruption is often preceded and accompanied by an increase in local seismic activity, driven by fracturing within the edifice. For closed systems, with a repose interval of the order of a century or more, this fracturing must occur in order to create a pathway for the magma to reach the surface. Time-to-failure forecasting models have been shown to be consistent with seismic acceleration patterns prior to eruptions at volcanoes in subduction zone settings. The aim of this research is to investigate the patterns in seismic activity produced by a failure model based on fundamental fracture mechanics, applied to a volcanic setting. In addition to the time series of earthquake activity, statistical measures such as seismic b-value are also analysed and compared with corresponding data from the field and laboratory studies. A greater understanding of the physical factors controlling fracture development and volcano-tectonic activity is required to enhance our forecasting capability. The one dimensional, fracture mechanics grid model developed in this work is consistent with the theory of growth and coalescence of multi-scale fractures as a controlling factor on magma ascent. The multi-scale fracture model predicts an initial exponential increase in the rate of seismicity, progressing to a hyperbolic increase that leads to eruption. The proposed model is run with variations in material and load properties, and produces exponential accelerations in activity with further development to a hyperbolic increase in some instances. In particular, the model reproduces patterns of acceleration in seismicity observed prior to eruptions at Mt. Pinatubo (1991) and Soufriere Hills (1995). The emergence of hyperbolic activity is associated with a mechanism of crack growth dominated by interaction and coalescence of neighbouring cracks, again consistent with the multi-scale fracture model. The model can also produce increasing sequences of activity that do not culminate in an eruption; an occurrence often observed in the field. Scaling properties of propagating fractures are also considered. The seismic bvalue reaches a minimum at the time of failure, similar to observations from the field and measurements of acoustic emissions in the laboratory. Similarly, the fractal dimension describing the fracture magnitude distribution follows trends consistent with other observations for failing materials. The spatial distribution of activity in the model emerges as a fractal distribution, even with an initially random location of fractures along the grid. Significant shifts in the temporal or spatial scaling parameters have been proposed as an indication of change in controlling factors on a volcanic system, and therefore represent a relatively unexplored approach in the art of eruption forecasting

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Leveraging input features for testing and debugging

When debugging software, it is paramount to understand why the program failed in the first place. How can we aid the developer in this process, and automate the process of understanding a program failure? The behavior of a program is determined by its inputs. A common step in debugging is to determine how the inputs influences the program behavior. In this thesis, I present Alhazen, a tool which combines a decision tree and a test generator into a feedback loop to create an explanation for which inputs lead to a specific program behavior. This can be used to generate a diagnosis for a bug automatically. Alhazen is evaluated for generating additional inputs, and recognizing bug-triggering inputs. The thesis makes first advances towards a user study on whether Alhazen helps software developers in debugging. Results show that Alhazen is effective for both use cases, and indicate that machine-learning approaches can help to understand program behavior. Considering Alhazen, the question what else can be done with input features follows naturally. In the thesis I present Basilisk, a tool which focuses testing on a specific part of the input. Basilisk is evaluated on its own, and an integration with Alhazen is explored. The results show promise, but also some challenges. All in all, the thesis shows that there is some potential in considering input features.Bei der Fehlersuche in Softwaresystemen ist es wichtig, die Ursache des Fehlers zu verstehen. Wie können wir den Entwickler hierbei unterstützen, und den Prozess des Programmverstehens automatisieren? Das Verhalten eines Programms wird durch seinen Input bestimmt. Ein typischer Schritt bei der Fehlersuche ist daher, zu verstehen wie der Input das Programmverhalten beeinflust. In dieser Arbeit präsentiere ich Alhazen, ein Programm, das eine Feedback Loop zwischen einem Decision Tree und einen Testgenerator einrichtet, um eine Erklärung dafür zu generieren, welche Inputs ein bestimmtes Programmverhalten auslösen. Das kann benutzt werden, um einen Programmfehler automatisch zu diagnostizieren. Es wird evaluiert, ob Alhazen eingesetzt werden kann um zusätzliche Inputs zu generieren und Inputs, die den Fehler auslösen, zu erkennen. Die Arbeit enhält erste Schritte für eine Nutzerstudie, die zeigen soll, ob Alhazen Softwareentwicklern bei der Fehlersuche hilft. Die Ergebnisse zeigen, dass Alhazen in beiden Szenarien effektiv ist, und deuten darauf hin, dass maschinelle Lernverfahren beim Programmverstehen helfen können. Alhazen wirft die Frage auf, ob die Betrachtung von Input Features andere Möglichkeiten bietet. Diese Thesis präsentiert Basilisk, ein Werkzeug, das Testgenerierung auf bestimmte Teile des Inputs fokussiert. Basilisk wird als eigenständiges Tool evaluiert, und eine Integration mit Alhazen wird ausprobiert. Die Ergebnisse sind vielversprechend, zeigen aber auch Probleme mit der Idee auf. Insgesamt zeigt die Arbeit das Protential von Input Features