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

A Flexible Design Space Exploration Platform for Wireless Sensor Networks

My dissertation presents a flexible design space exploration platform for wireless sensor networks and an extensible design flow. The conceived platform enables the fast creation and evaluation of custom sensor node hard- and software architectures without developing custom hardware. One important feature of my platform is that it allows the evaluation of the computational- and communication domain of a sensor node in respect to power consumption

3D Sensor Placement and Embedded Processing for People Detection in an Industrial Environment

Papers I, II and III are extracted from the dissertation and uploaded as separate documents to meet post-publication requirements for self-arciving of IEEE conference papers.At a time when autonomy is being introduced in more and more areas, computer vision plays a very important role. In an industrial environment, the ability to create a real-time virtual version of a volume of interest provides a broad range of possibilities, including safety-related systems such as vision based anti-collision and personnel tracking. In an offshore environment, where such systems are not common, the task is challenging due to rough weather and environmental conditions, but the result of introducing such safety systems could potentially be lifesaving, as personnel work close to heavy, huge, and often poorly instrumented moving machinery and equipment. This thesis presents research on important topics related to enabling computer vision systems in industrial and offshore environments, including a review of the most important technologies and methods. A prototype 3D sensor package is developed, consisting of different sensors and a powerful embedded computer. This, together with a novel, highly scalable point cloud compression and sensor fusion scheme allows to create a real-time 3D map of an industrial area. The question of where to place the sensor packages in an environment where occlusions are present is also investigated. The result is algorithms for automatic sensor placement optimisation, where the goal is to place sensors in such a way that maximises the volume of interest that is covered, with as few occluded zones as possible. The method also includes redundancy constraints where important sub-volumes can be defined to be viewed by more than one sensor. Lastly, a people detection scheme using a merged point cloud from six different sensor packages as input is developed. Using a combination of point cloud clustering, flattening and convolutional neural networks, the system successfully detects multiple people in an outdoor industrial environment, providing real-time 3D positions. The sensor packages and methods are tested and verified at the Industrial Robotics Lab at the University of Agder, and the people detection method is also tested in a relevant outdoor, industrial testing facility. The experiments and results are presented in the papers attached to this thesis.publishedVersio

Kommunikation und Bildverarbeitung in der Automation

In diesem Open-Access-Tagungsband sind die besten Beiträge des 9. Jahreskolloquiums "Kommunikation in der Automation" (KommA 2018) und des 6. Jahreskolloquiums "Bildverarbeitung in der Automation" (BVAu 2018) enthalten. Die Kolloquien fanden am 20. und 21. November 2018 in der SmartFactoryOWL, einer gemeinsamen Einrichtung des Fraunhofer IOSB-INA und der Technischen Hochschule Ostwestfalen-Lippe statt. Die vorgestellten neuesten Forschungsergebnisse auf den Gebieten der industriellen Kommunikationstechnik und Bildverarbeitung erweitern den aktuellen Stand der Forschung und Technik. Die in den Beiträgen enthaltenen anschaulichen Beispiele aus dem Bereich der Automation setzen die Ergebnisse in den direkten Anwendungsbezug

Heterogeneous Architectures For Parallel Acceleration

To enable a new generation of digital computing applications, the greatest challenge is to provide a better level of energy efficiency (intended as the performance that a system can provide within a certain power budget) without giving up a systems's flexibility. This constraint applies to digital system across all scales, starting from ultra-low power implanted devices up to datacenters for high-performance computing and for the "cloud". In this thesis, we show that architectural heterogeneity is the key to provide this efficiency and to respond to many of the challenges of tomorrow's computer architecture - and at the same time we show methodologies to introduce it with little or no loss in terms of flexibility. In particular, we show that heterogeneity can be employed to tackle the "walls" that impede further development of new computing applications: the utilization wall, i.e. the impossibility to keep all transistors on in deeply integrated chips, and the "data deluge", i.e. the amount of data to be processed that is scaling up much faster than the computing performance and efficiency. We introduce a methodology to improve heterogeneous design exploration of tightly coupled clusters; moreover we propose a fractal heterogeneity architecture that is a parallel accelerator for low-power sensor nodes, and is itself internally heterogeneous thanks to an heterogeneous coprocessor for brain-inspired computing. This platform, which is silicon-proven, can lead to more than 100x improvement in terms of energy efficiency with respect to typical computing nodes used within the same domain, enabling the application of complex algorithms, vastly more performance-hungry than the current state-of-the-art in the ULP computing domain

Methods and Techniques for Dynamic Deployability of Software-Defined Security Services

With the recent trend of “network softwarisation”, enabled by emerging technologies such as Software-Defined Networking and Network Function Virtualisation, system administrators of data centres and enterprise networks have started replacing dedicated hardware-based middleboxes with virtualised network functions running on servers and end hosts. This radical change has facilitated the provisioning of advanced and flexible network services, ultimately helping system administrators and network operators to cope with the rapid changes in service requirements and networking workloads. This thesis investigates the challenges of provisioning network security services in “softwarised” networks, where the security of residential and business users can be provided by means of sets of software-based network functions running on high performance servers or on commodity devices. The study is approached from the perspective of the telecom operator, whose goal is to protect the customers from network threats and, at the same time, maximize the number of provisioned services, and thereby revenue. Specifically, the overall aim of the research presented in this thesis is proposing novel techniques for optimising the resource usage of software-based security services, hence for increasing the chances for the operator to accommodate more service requests while respecting the desired level of network security of its customers. In this direction, the contributions of this thesis are the following: (i) a solution for the dynamic provisioning of security services that minimises the utilisation of computing and network resources, and (ii) novel methods based on Deep Learning and Linux kernel technologies for reducing the CPU usage of software-based security network functions, with specific focus on the defence against Distributed Denial of Service (DDoS) attacks. The experimental results reported in this thesis demonstrate that the proposed solutions for service provisioning and DDoS defence require fewer computing resources, compared to similar approaches available in the scientific literature or adopted in production networks

A Scalable Method for Nonlinear Dimensionality Reduction with Applications to Single-Cell Data

Current generation biological measurement technologies enable quantifying cellular characteristics and processes at a genome-wide scale and single-cell resolution, producing invaluable data for research on complex phenomena such as cancer. As such high dimensional data is difficult to process and reason about directly, dimensionality reduction is commonly employed in the analysis of molecular data, either as a preprocessing step for downstream analysis or for purposes of exploratory data analysis by visualizing the data in lower dimensions to gain further insights. However, these vast amounts of high dimensional data pose several challenges to dimensionality reduction methods in common use today. Linear methods like PCA are incapable of capturing the nonlinear, heteroscedastic nature of the data being transformed, whereas the computational complexity of nonlinear methods such as t-SNE becomes an obstacle when dealing with large data sets. In addition, the prevailing nonlinear methods are based on distance metrics, rendering them prone to the curse of dimensionality. In this work a method for nonlinear dimensionality reduction is proposed which aims to address these issues. By exploiting the properties of two separate neural network architectures, namely a stochastic variant of autoencoders and a parametric variant of t-SNE, we are demonstrably able to mitigate the outlined issues. The proposed method is compared to existing methods with excellent results in terms of its scalability to large data sets, robustness to sparse and corrupt data, as well as its ability to combat the curse of dimensionality. Additionally, the practical application of the proposed method to single-cell data sets obtained from cancer patients' tissue samples is demonstrated. We believe that such methodological developments benefit more efficiently utilizing emerging single-cell data, which could in turn translate to biologically testable hypotheses and benefits in patient care