TEASER: Simulation-based CAN Bus Regression Testing for Self-driving Cars Software

Software systems for safety-critical systems like self-driving cars (SDCs) need to be tested rigorously. Especially electronic control units (ECUs) of SDCs should be tested with realistic input data. In this context, a communication protocol called Controller Area Network (CAN) is typically used to transfer sensor data to the SDC control units. A challenge for SDC maintainers and testers is the need to manually define the CAN inputs that realistically represent the state of the SDC in the real world. To address this challenge, we developed TEASER, which is a tool that generates realistic CAN signals for SDCs obtained from sensors from state-of-the-art car simulators. We evaluated TEASER based on its integration capability into a DevOps pipeline of aicas GmbH, a company in the automotive sector. Concretely, we integrated TEASER in a Continous Integration (CI) pipeline configured with Jenkins. The pipeline executes the test cases in simulation environments and sends the sensor data over the CAN bus to a physical CAN device, which is the test subject. Our evaluation shows the ability of TEASER to generate and execute CI test cases that expose simulation-based faults (using regression strategies); the tool produces CAN inputs that realistically represent the state of the SDC in the real world. This result is of critical importance for increasing automation and effectiveness of simulation-based CAN bus regression testing for SDC software. Tool: https://doi.org/10.5281/zenodo.7964890 GitHub: https://github.com/christianbirchler-org/sdc-scissor/releases/tag/v2.2.0-rc.1 Documentation: https://sdc-scissor.readthedocs.i

TEASER : simulation-based CAN bus regression testing for self-driving cars software

Software systems for safety-critical systems like self-driving cars (SDCs) need to be tested rigorously. Especially electronic control units (ECUs) of SDCs should be tested with realistic input data. In this context, a communication protocol called Controller Area Network (CAN) is typically used to transfer sensor data to the SDC control units. A challenge for SDC maintainers and testers is the need to manually define the CAN inputs that realistically represent the state of the SDC in the real world. To address this challenge, we developed TEASER, which is a tool that generates realistic CAN signals for SDCs obtained from sensors from state-of-the-art car simulators. We evaluated TEASER based on its integration capability into a DevOps pipeline of aicas GmbH, a company in the automotive sector. Concretely, we integrated TEASER in a Continous Integration (CI) pipeline configured with Jenkins. The pipeline executes the test cases in simulation environments and sends the sensor data over the CAN bus to a physical CAN device, which is the test subject. Our evaluation shows the ability of TEASER to generate and execute CI test cases that expose simulation-based faults (using regression strategies); the tool produces CAN inputs that realistically represent the state of the SDC in the real world. This result is of critical importance for increasing automation and effectiveness of simulation-based CAN bus regression testing for SDC software

DevOps for Digital Leaders

DevOps; continuous delivery; software lifecycle; concurrent parallel testing; service management; ITIL; GRC; PaaS; containerization; API management; lean principles; technical debt; end-to-end automation; automatio

Smart HMI for an autonomous vehicle

El presente trabajo expone la arquitectura diseñada para la implementación de un HMI (Human Machine Interface) en un vehículo autónomo desarrollado en la Universidad de Alcalá. Este sistema hace uso del ecosistema ROS (Robot Operating System) para la comunicación entre los diferentes modulos desarrollados en el vehículo. Además se expone la creación de una herramienta de captación de datos de conductores haciendo uso de la mirada de este, basada en OpenFace, una herramienta de código libre para análisis de caras. Para ello se han desarrollado dos métodos, uno basado en un método lineal y otro usando técnicas del algoritmo NARMAX. Se han desarrollado diferentes test para demostrar la precisión de ambos métodos y han sido evaluados en el dataset de accidentes DADA2000.This works presents the framework that composed the HMI (Human Machine Interface) built in an autonomous vehicle from University of Alcalá. This system has been developed using the framework ROS (Robot Operating System) for the communication between the different sub-modules developed on the vehicle. Also, a system to obtain gaze focalization data from drivers using a camera is presented, based on OpenFace, which is an open source tool for face analysis. Two different methods are proposed, one linear and other based on NARMAX algorithm. Different test has been done in order to prove their accuracy and they have been evaluated on the challenging dataset DADA2000, which is composed by traffic accidents.Máster Universitario en Ingeniería Industrial (M141

DevOps for Digital Leaders

DevOps; continuous delivery; software lifecycle; concurrent parallel testing; service management; ITIL; GRC; PaaS; containerization; API management; lean principles; technical debt; end-to-end automation; automatio

Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0

The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK). This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing. A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems. Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital ‘sense to action’ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode. Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes

Enabling Deep Intelligence on Embedded Systems

As deep learning for resource-constrained systems become more popular, we see an increased number of intelligent embedded systems such as IoT devices, robots, autonomous vehicles, and the plethora of portable, wearable, and mobile devices that are feature-packed with a wide variety of machine learning tasks. However, the performance of DNNs (deep neural networks) running on an embedded system is significantly limited by the platform's CPU, memory, and battery-size; and their scope is limited to simplistic inference tasks only. This dissertation proposes on-device deep learning algorithms and supporting hardware designs, enabling embedded systems to efficiently perform deep intelligent tasks (i.e., deep neural networks) that are high-memory-footprint, compute-intensive, and energy-hungry beyond their limited computing resources. We name such on-device deep intelligence on embedded systems as Embedded Deep Intelligence. Specifically, we introduce resource-aware learning strategies devised to overcome the four fundamental constraints of embedded systems imposed on the way towards Embedded Deep Intelligence, i.e., in-memory multitask learning via introducing the concept of Neural Weight Virtualization, adaptive real-time learning via introducing the concept of SubFlow, opportunistic accelerated learning via introducing the concept of Neuro.ZERO, and energy-aware intermittent learning, which tackles the problems of the small size of memory, dynamic timing constraint, low-computing capability, and limited energy, respectively. Once deployed in the field with the proposed resource-aware learning strategies, embedded systems are not only able to perform deep inference tasks on sensor data but also update and re-train their learning models at run-time without requiring any help from any external system. Such an on-device learning capability of Embedded Deep Intelligence makes an embedded intelligent system real-time, privacy-aware, secure, autonomous, untethered, responsive, and adaptive without concern for its limited resources.Doctor of Philosoph

Designing and Deploying Internet of Things Applications in the Industry: An Empirical Investigation

RÉSUMÉ : L’Internet des objets (IdO) a pour objectif de permettre la connectivité à presque tous les objets trouvés dans l’espace physique. Il étend la connectivité aux objets de tous les jours et o˙re la possibilité de surveiller, de suivre, de se connecter et d’intéragir plus eÿcacement avec les actifs industriels. Dans l’industrie de nos jours, les réseaux de capteurs connectés surveillent les mouvements logistiques, fabriquent des machines et aident les organisations à améliorer leur eÿcacité et à réduire les coûts. Cependant, la conception et l’implémentation d’un réseau IdO restent, aujourd’hui, une tâche particulièrement diÿcile. Nous constatons un haut niveau de fragmentation dans le paysage de l’IdO, les développeurs se complaig-nent régulièrement de la diÿculté à intégrer diverses technologies avec des divers objets trouvés dans les systèmes IdO et l’absence des directives et/ou des pratiques claires pour le développement et le déploiement d’application IdO sûres et eÿcaces. Par conséquent, analyser et comprendre les problèmes liés au développement et au déploiement de l’IdO sont primordiaux pour permettre à l’industrie d’exploiter son plein potentiel. Dans cette thèse, nous examinons les interactions des spécialistes de l’IdO sur le sites Web populaire, Stack Overflow et Stack Exchange, afin de comprendre les défis et les problèmes auxquels ils sont confrontés lors du développement et du déploiement de di˙érentes appli-cations de l’IdO. Ensuite, nous examinons le manque d’interopérabilité entre les techniques développées pour l’IdO, nous étudions les défis que leur intégration pose et nous fournissons des directives aux praticiens intéressés par la connexion des réseaux et des dispositifs de l’IdO pour développer divers services et applications. D’autre part, la sécurité étant essen-tielle au succès de cette technologie, nous étudions les di˙érentes menaces et défis de sécurité sur les di˙érentes couches de l’architecture des systèmes de l’IdO et nous proposons des contre-mesures. Enfin, nous menons une série d’expériences qui vise à comprendre les avantages et les incon-vénients des déploiements ’serverful’ et ’serverless’ des applications de l’IdO afin de fournir aux praticiens des directives et des recommandations fondées sur des éléments probants relatifs à de tels déploiements. Les résultats présentés représentent une étape très importante vers une profonde compréhension de ces technologies très prometteuses. Nous estimons que nos recommandations et nos suggestions aideront les praticiens et les bâtisseurs technologiques à améliorer la qualité des logiciels et des systèmes de l’IdO. Nous espérons que nos résultats pourront aider les communautés et les consortiums de l’IdO à établir des normes et des directives pour le développement, la maintenance, et l’évolution des logiciels de l’IdO.----------ABSTRACT : Internet of Things (IoT) aims to bring connectivity to almost every object found in the phys-ical space. It extends connectivity to everyday things, opens up the possibility to monitor, track, connect, and interact with industrial assets more eÿciently. In the industry nowadays, we can see connected sensor networks monitor logistics movements, manufacturing machines, and help organizations improve their eÿciency and reduce costs as well. However, designing and implementing an IoT network today is still a very challenging task. We are witnessing a high level of fragmentation in the IoT landscape and developers regularly complain about the diÿculty to integrate diverse technologies of various objects found in IoT systems, and the lack of clear guidelines and–or practices for developing and deploying safe and eÿcient IoT applications. Therefore, analyzing and understanding issues related to the development and deployment of the Internet of Things is utterly important to allow the industry to utilize its fullest potential. In this thesis, we examine IoT practitioners’ discussions on the popular Q&A websites, Stack Overflow and Stack Exchange, to understand the challenges and issues that they face when developing and deploying di˙erent IoT applications. Next, we examine the lack of interoper-ability among technologies developed for IoT and study the challenges that their integration poses and provide guidelines for practitioners interested in connecting IoT networks and de-vices to develop various services and applications. Since security issues are center to the success of this technology, we also investigate di˙erent security threats and challenges across di˙erent layers of the architecture of IoT systems and propose countermeasures. Finally, we conduct a series of experiments to understand the advantages and trade-o˙s of serverful and serverless deployments of IoT applications in order to provide practitioners with evidence-based guidelines and recommendations on such deployments. The results presented in this thesis represent a first important step towards a deep understanding of these very promising technologies. We believe that our recommendations and suggestions will help practitioners and technology builders improve the quality of IoT software and systems. We also hope that our results can help IoT communities and consortia establish standards and guidelines for the development, maintenance, and evolution of IoT software and systems

Design a CPW antenna on rubber substrate for multiband applications

This paper presents a compact CPW monopole antenna on rubber substrate for multiband applications. The multi band applications (2.45 and 3.65 GHz) is achieved on this antenna design with better antenna performances. Specially this antenna focused on ISM band application meanwhile some of slots (S1, S2, S3) have been used and attained another frequency band at 3.65 GHz for WiMAX application. The achievement of the antenna outcomes from this design that the bandwidth of 520 MHz for first band, the second band was 76 MHz for WiMAX application and the radiation efficiency attained around 90%. Moreover, the realized gain was at 4.27 dBi which overcome the most of existing design on that field. CST microwave studio has been used for antenna simulation

Internet of Things (IoT) for Automated and Smart Applications

Internet of Things (IoT) is a recent technology paradigm that creates a global network of machines and devices that are capable of communicating with each other. Security cameras, sensors, vehicles, buildings, and software are examples of devices that can exchange data between each other. IoT is recognized as one of the most important areas of future technologies and is gaining vast recognition in a wide range of applications and fields related to smart homes and cities, military, education, hospitals, homeland security systems, transportation and autonomous connected cars, agriculture, intelligent shopping systems, and other modern technologies. This book explores the most important IoT automated and smart applications to help the reader understand the principle of using IoT in such applications