1,213 research outputs found

    Reconfigurable middleware architectures for large scale sensor networks

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    Wireless sensor networks, in an effort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task reconfiguration and high-level object recomposition. Through the layered approach of SENSIX, the software developer creates a domain-specific sensing architecture by defining a customized task specification and utilizing object inheritance. In addition, SENSIX performs better at large scales (on the order of 1000 nodes or more) than other sensor network middleware which do not include such unified facilities for vertical integration

    Li-ion batteries monitoring for electrified vehicles applications

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Evolvable Smartphone-Based Point-of-Care Systems For In-Vitro Diagnostics

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    Recent developments in the life-science -omics disciplines, together with advances in micro and nanoscale technologies offer unprecedented opportunities to tackle some of the major healthcare challenges of our time. Lab-on-Chip technologies coupled with smart-devices in particular, constitute key enablers for the decentralization of many in-vitro medical diagnostics applications to the point-of-care, supporting the advent of a preventive and personalized medicine. Although the technical feasibility and the potential of Lab-on-Chip/smart-device systems is repeatedly demonstrated, direct-to-consumer applications remain scarce. This thesis addresses this limitation. System evolvability is a key enabler to the adoption and long-lasting success of next generation point-of-care systems by favoring the integration of new technologies, streamlining the reengineering efforts for system upgrades and limiting the risk of premature system obsolescence. Among possible implementation strategies, platform-based design stands as a particularly suitable entry point. One necessary condition, is for change-absorbing and change-enabling mechanisms to be incorporated in the platform architecture at initial design-time. Important considerations arise as to where in Lab-on-Chip/smart-device platforms can these mechanisms be integrated, and how to implement them. Our investigation revolves around the silicon-nanowire biological field effect transistor, a promising biosensing technology for the detection of biological analytes at ultra low concentrations. We discuss extensively the sensitivity and instrumentation requirements set by the technology before we present the design and implementation of an evolvable smartphone-based platform capable of interfacing lab-on-chips embedding such sensors. We elaborate on the implementation of various architectural patterns throughout the platform and present how these facilitated the evolution of the system towards one accommodating for electrochemical sensing. Model-based development was undertaken throughout the engineering process. A formal SysML system model fed our evolvability assessment process. We introduce, in particular, a model-based methodology enabling the evaluation of modular scalability: the ability of a system to scale the current value of one of its specification by successively reengineering targeted system modules. The research work presented in this thesis provides a roadmap for the development of evolvable point-of-care systems, including those targeting direct-to-consumer applications. It extends from the early identification of anticipated change, to the assessment of the ability of a system to accommodate for these changes. Our research should thus interest industrials eager not only to disrupt, but also to last in a shifting socio-technical paradigm

    Research and Technology, 1989

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    Selected research and technology activities at Ames Research Center, including the Moffett Field site and the Dryden Flight Research Facility, are summarized. These accomplishments exemplify the Center's varied and highly productive research efforts for 1989

    Hardware-software design of embedded systems for intelligent sensing applications

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    This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain, Wireless Sensor Network developments and an AI framework for vibration-based structural assessment

    Fuzzy logic traffic signal controller enhancement based on aggressive driver behavior classification

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    The rise in population worldwide and especially in Egypt, together with the increase in the number of vehicles present serious complications regarding traffic congestion and road safety. The elementary solution towards improving congestion is to expand road capacities by building new lanes. This, however, requires time and effort and therefore new methodologies are being implemented. Intelligent transportation systems (ITS) try to approach traffic congestion through the application of computational and engineering techniques. Traffic signal control is a branch of intelligent transportation systems which focuses on improving traffic signal conditions. A traffic signal controllers’ main objective is to improve this assignment in a way which reduces delays. This research proposes a new approach to enhancing traffic signal control and reducing delays of a single intersection, through the integration of an aggressive driving behavior classifier. Previous approaches dealt with traffic control and driver behavior separately, and therefore their successful integration is a new challenging area in the field. Multiple experiment sets were conducted to provide an indication to the effectiveness of our approach. Firstly, an aggressive driver behavior classifier using feed-forward neural network was successfully built utilizing Virginia Tech 100-car naturalistic driving study data. Its performance was compared against long short-term memory recurrent neural networks and support vector machines, and it resulted in better performance as shown by the area under the curve. To the best of our knowledge, this classifier is the first of its kind to be built on this 100-car study data. Secondly, a representation of aggressive driving behavior was constructed in the simulated environment, based on real life data and statistics. Finally, Mamdani’s fuzzy logic controller was modified to accommodate for the integration of the aggressive behavior classifier. The integration results were encouraging and yielded significant improvements at higher traffic flow volumes when compared against the built Mamdani’s controller. The results are promising and provide an initial step towards the integration of driver behavior classification and traffic signal control

    A Holistic Approach to Functional Safety for Networked Cyber-Physical Systems

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    Functional safety is a significant concern in today's networked cyber-physical systems such as connected machines, autonomous vehicles, and intelligent environments. Simulation is a well-known methodology for the assessment of functional safety. Simulation models of networked cyber-physical systems are very heterogeneous relying on digital hardware, analog hardware, and network domains. Current functional safety assessment is mainly focused on digital hardware failures while minor attention is devoted to analog hardware and not at all to the interconnecting network. In this work we believe that in networked cyber-physical systems, the dependability must be verified not only for the nodes in isolation but also by taking into account their interaction through the communication channel. For this reason, this work proposes a holistic methodology for simulation-based safety assessment in which safety mechanisms are tested in a simulation environment reproducing the high-level behavior of digital hardware, analog hardware, and network communication. The methodology relies on three main automatic processes: 1) abstraction of analog models to transform them into system-level descriptions, 2) synthesis of network infrastructures to combine multiple cyber-physical systems, and 3) multi-domain fault injection in digital, analog, and network. Ultimately, the flow produces a homogeneous optimized description written in C++ for fast and reliable simulation which can have many applications. The focus of this thesis is performing extensive fault simulation and evaluating different functional safety metrics, \eg, fault and diagnostic coverage of all the safety mechanisms
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