55 research outputs found

    Autonomous Sensor Signal Acquisition System

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    Tato práce se zabývá návrhem a realizací autonomního systému pro záznam signálů ze senzorů pro měření na malých a těžce přístupných místech. Zařízení podporuje připojení osmi analogových vstupů, tří digitálních I2C vstupů, čtyř univerzálních binárních vstupů a obsahuje vestavěný digitální akcelerometr. Zařízení dále poskytuje programovatelné proudové zdroje pro napájení analogových senzorů a obvody pro zesílení signálů z analogových vstupů. Zařízení podporuje dva módy. V režimu autonomního měření jsou po uživatelem nastavený čas zaznamenávána data z jednotlivých senzorů. Naměřená data jsou ukládána na interní microSD kartu. V režimu přenosu mohou být skrze webovou stránku konfigurovány parametry měření a stahovány soubory s naměřenými daty. Možnost připojení indukční nabíječky zajišťuje kompletní bezdrátovost celého zařízení. Práce se zabývá hardwarovým řešením a návrhem firmwaru do mikrokontroléru STM32F411VE sloužícímu k ovládání celého zařízení a do mikrokontroléru ESP8266 obsaženém uvnitř Wi-Fi modulu.This thesis discusses the concept and the realisation of the autonomous sensor signal acquisition system for measurement in small and hardly accessible places. The device supports eight analog inputs, onboard digital accelerometer, three I2C digital inputs and four universal binary inputs. The device also provides programmable power sources for analog sensors and signal conditioners for amplification of the measured analog signal. The device provides two modes. In the stand-alone mode, the system with specified settings records data for given time. The measured data are stored to the microSD card. In transfer mode, the system can be configured through the web page, and the files with measured data can be downloaded. The possibility of using inductive charging ensures the complete wireless device operation. This text considers the hardware solutions and the firmware design for the STM32F411VE microcontroller used to control the device and for the ESP8266 microcontroller included in the Wi-Fi module

    Building Blocks for Adaptive Modular Sensing Systems

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    This thesis contributes towards the development of systems and strategies by which sensor and actuator components can be combined to produce flexible and robust sensor systems for a given application. A set of intelligent modular blocks (building blocks) have been created from which composite sensors (made up of multiple sensor and actuator components) can be rapidly reconfigured for the construction of Adaptive Modular Sensing Systems. The composite systems are expected to prove useful in several application domains including industrial control, inspection systems, mobile robotics, monitoring and data acquisition. The intelligent building blocks, referred to as transducer interface modules, contain embedded knowledge about their capabilities and how they can interact with other modules. These modules encapsulate a general purpose modular hardware architecture that provides an interface between the sensors, the actuators, and the communication medium. The geometry of each transducer interface module is a cube. A connector mechanism implemented on each face of the module enables physical connection of the modules. Each module provides a core functionality and can be connected to other modules to form more capable composite sensors. Once the modules are combined, the capabilities (e.g., range, resolution, sample rate, etc.) and functionality (e.g., temperature measurement) of the composite sensor is determined and communicated to other sensors in the enviornment. For maximum flexibility, a distributed software architecture is executed on the blocks to enable automatic acquisition of configuration-specific algorithms. This logical algorithm imparts a collective identity to the composite group, and processes data based on the capabilities and functionalities of the transducers present in the system. A knowledge representation scheme allows each module in the composite group to store and communicate its functionality and capabilities to other connected modules in the system

    DEVELOPMENT AND EVALUATION OF A CONTROLLER AREA NETWORK BASED AUTONOMOUS VEHICLE

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    Through the work of researchers and the development of commercially availableproducts, automated guidance has become a viable option for agricultural producers.Some of the limitations of commercially available technologies are that they onlyautomate one function of the agricultural vehicle and that the systems are proprietary toa single machine model.The objective of this project was to evaluate a controller area network (CAN bus)as the basis of an automated guidance system. The prototype system utilized severalmicrocontroller-driven nodes to act as control points along a system wide CAN bus.Messages were transferred to the steering, transmission, and hitch control nodes from atask computer. The task computer utilized global positioning system data to determinethe appropriate control commands.Infield testing demonstrated that each of the control nodes could be controlledsimultaneously over the CAN bus. Results showed that the task computer adequatelyapplied a feedback control model to the system and achieved guidance accuracy levelswell within the range sought. Testing also demonstrated the system\u27s ability tocomplete normal field operations such as headland turning and implement control

    Towards Intelligent Data Acquisition Systems with Embedded Deep Learning on MPSoC

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    Large-scale scientific experiments rely on dedicated high-performance data-acquisition systems to sample, readout, analyse, and store experimental data. However, with the rapid development in detector technology in various fields, the number of channels and the data rate are increasing. For trigger and control tasks data acquisition systems needs to satisfy real-time constraints, enable short-time latency and provide the possibility to integrate intelligent data processing. During recent years machine learning approaches have been used successfully in many applications. This dissertation will study how machine learning techniques can be integrated already in the data acquisition of large-scale experiments. A universal data acquisition platform for multiple data channels has been developed. Different machine learning implementation methods and application have been realized using this system. On the hardware side, recent FPGAs do not only provide high-performance parallel logic but more and more additional features, like ultra-fast transceivers and embedded ARM processors. TSMC\u27s 16nm FinFET Plus (16FF+) 3D transistor technology enables Xilinx in the Zynq UltraScale+ FPGA devices to increase the performance/watt ratio by 2 to 5 times compared to their previous generation. The selected main processor ZU11EG owns 32 GTH transceivers where each one could operate up to 16.316.3 Gb/s and 16 GTY transceivers where each of them could operate up to 32.7532.75 Gb/s. These transceivers are routed to x16 lanes Gen 33/44 PCIe, 1212 lanes full-duplex FireFly electrical/optical data link and VITA 57.4 FMC+ connector. The new Zynq UltraScale+ device provides at least three major advantages for advanced data acquisition systems: First, the 16nm FinFET+ programmable logic (PL) provides high-speed readout capabilities by high-speed transceivers; second, built-in quad-core 64-bit ARM Cortex-A53 processor enable host embedded Linux system. Thus, webservers, slow control and monitoring application could be realized in a embedded processor environment; third, the Zynq Multiprocessor System-on-Chip technology connects programmable logic and microprocessors. In this thesis, the benefits of such architectures for the integration of machine learning algorithms in data acquisition systems and control application are demonstrated. On the algorithm side, there have been many achievements in the field of machine learning over the last decades. Existing machine learning algorithms split into several categories depending on how the learning phase is organized: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning and Reinforcement Learning. Most commonly used in scientific applications are supervised learning and reinforcement learning. Supervised learning learns from the labelled input and output, and generates a function that could predict the future different input to the appropriate output. A common application instance is a classification. They have a wide difference in basic math theory, training, inference, and their implementation. One of the natural solutions is Application Specific Integrated Circuit (ASIC) Artificial Intelligence (AI) chips. A typical example is the Google Tensor Processing Unit (TPU), it could cover the training and inference for both supervised learning and reinforcement learning. One of the major issues is that such chip could not provide high data transferring bandwidth other than high compute power. As a comparison, the Xilinx UltraScale+ FPGA could also provide raw compute power and efficiency for all different data types down to a single bit. From a deployment point of view, the training part of supervised learning is typically performed by CPU/GPU/TPU on a fixed dataset. For reinforcement learning, the training phase is more complex. The algorithm needs to periodically interact with the controlled system and execute a Markov Decision Process (MDP). There is no static training dataset, but it is obtained in real-time. The time slot between each step depends on the dynamics of the controlled system. The inference is also bound to this sampling time because the algorithm needs to interact with the environment and decide the appropriate action for a response, then a higher demand on time is proposed. This thesis gives solutions for both training and inference of reinforcement learning. At first, the requirements are analyzed, then the algorithm is deduced from scratch, and training on the PS part of Zynq device is implemented, meanwhile the inference at FPGA side is proposed which is similar solution compared with supervised learning. The results for Policy Gradient show a lot of improvement over a CPU/GPU-based machine learning framework. The Deep Deterministic Policy Gradient also has improvement regarding both training latency and stability. This implementation method provides a low-latency approach for reinforcement learning on-field training process

    Systematische Transaction-Level-Kommunikations-Modellierung mit SystemC

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    An emerging approach to embedded system design is to assemble them from a library of hardware and software component models (IP, intellectual property) using a system description language, such as SystemC. SystemC allows describing the communication among IPs in terms of abstract operations (transactions). The promise is that with transaction-level modeling (TLM), future systems-on-chip with one billion transistors and more can be composed out of IPs as simply as playing with LEGO bricks. However, reality is far out. In fact, each IP vendor promotes another proprietary interface standard and the provided design tools lack compatibility, such that heterogeneous IPs cannot be integrated efficiently. A novel generic interconnect fabric for TLM is presented which aims at enabling inter-operation between models of different levels of abstraction (mixed-mode) and models with different interfaces (heterogeneous components), with as little overhead as possible. A generic, protocol independent representation of transactions is developed, among with an abstraction level formalism. This approach is shown to support systematic simulation of state-of-the-art buses and networks-on-chip such as IBM CoreConnect and PCI Express over several levels of TLM abstraction. A layered simulation framework for SystemC, GreenBus, is developed to examine the proposed concepts. The thesis discusses new implementation techniques for communication modeling with SystemC which outperform the existing approaches in terms of flexibility, simulation accuracy, and performance. Based on these techniques, advanced concepts for TLM-based hardware/software co-design and FPGA prototyping are examined. Several experiments and a video processor case study highlight the efficiency of the approach and show its applicability in a TLM design flow.Eingebettete Systeme werden zunehmend auf Basis vorgefertigter Hard- und Softwarebausteine entwickelt, die in Form von Modellen (IP, Intellectual Property) vorliegen. Hierzu werden Systembeschreibungssprachen wie SystemC eingesetzt. SystemC ermöglicht, die Kommunikation zwischen IPs durch abstrakte Operationen, sog. Transaktionen zu beschreiben. Mit dieser Transaction-Level-Modellierung (TLM) sollen auch zukünftige Systeme mit 1 Milliarde Transistoren und mehr effizient entwickelt werden können. Idealerweise sollte das Hantieren mit IPs dabei so einfach sein wie das Spielen mit LEGO-Steinen. In der Realität sind jedoch IPs unterschiedlicher Hersteller nicht ohne weiteres integrierbar, und auch die Entwurfswerkzeuge sind nicht kompatibel. In dieser Doktorarbeit wird ein neuer, generischer Ansatz für die Transaction-Level-Modellierung mit SystemC vorgestellt, der Kommunikation zwischen Modellen auf unterschiedlichen Abstraktionsebenen (Mixed-Mode) und mit unterschiedlichen Schnittstellen (heterogene Komponenten) möglich macht. Der zusätzlich benötigte Simulations- und Code-Aufwand ist minimal. Ein protokollunabhängiges Transaktionsmodell und ein formaler Ansatz zur Beschreibung von Abstraktionsebenen werden vorgestellt, mit denen verschiedenartige Busse und Networks-on-Chip wie IBM CoreConnect und PCI Express auf verschiedenen TLM-Abstraktionsebenen simuliert werden können. Ein modulares Simulationsframework für SystemC wird entwickelt (GreenBus), um die vorgeschlagenen Konzepte zu untersuchen. Anhand von GreenBus werden neue Implementierungstechniken diskutiert, die den existierenden Ansätzen in Flexibilität, Simulationsgenauigkeit und -geschwindigkeit überlegen sind. Die Vor- und Nachteile der entwickelten Techniken werden mit Experimenten belegt, und eine Videoprozessor-Fallstudie demonstriert die Effizienz des Ansatzes in einem TLM-basierten Entwurfsfluss

    The MEG detector for μ+→e+γ{\mu}+\to e+{\gamma} decay search

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    The MEG (Mu to Electron Gamma) experiment has been running at the Paul Scherrer Institut (PSI), Switzerland since 2008 to search for the decay \meg\ by using one of the most intense continuous ÎĽ+\mu^+ beams in the world. This paper presents the MEG components: the positron spectrometer, including a thin target, a superconducting magnet, a set of drift chambers for measuring the muon decay vertex and the positron momentum, a timing counter for measuring the positron time, and a liquid xenon detector for measuring the photon energy, position and time. The trigger system, the read-out electronics and the data acquisition system are also presented in detail. The paper is completed with a description of the equipment and techniques developed for the calibration in time and energy and the simulation of the whole apparatus.Comment: 59 pages, 90 figure

    Sensor Integration for Smart Cities Using Multi-Hop Networks

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    Smart Cities are designed to be living systems and turn urban dwellers life more comfortable and interactive by keeping them aware of what surrounds them, while leaving a greener footprint. The Future Cities Project [1] aims to create infrastructures for research in smart cities including a vehicular network, the BusNet, and an environmental sensor platform, the Urban Sense. Vehicles within the BusNet are equipped with On Board Units (OBUs) that offer free Wi-Fi to passengers and devices near the street. The Urban Sense platform is composed by a set of Data Collection Units (DCUs) that include a set of sensors measuring environmental parameters such as air pollution, meteorology and noise. The Urban Sense platform is expanding and receptive to add new sensors to the platform. The parnership with companies like TNL were made and the need to monitor garbage street containers emerged as air pollution prevention. If refuse collection companies know prior to the refuse collection which route is the best to collect the maximum amount of garbage with the shortest path, they can reduce costs and pollution levels are lower, leaving behind a greener footprint. This dissertation work arises in the need to monitor the garbage street containers and integrate these sensors into an Urban Sense DCU. Due to the remote locations of the garbage street containers, a network extension to the vehicular network had to be created. This dissertation work also focus on the Multi-hop network designed to extend the vehicular network coverage area to the remote garbage street containers. In locations where garbage street containers have access to the vehicular network, Roadside Units (RSUs) or Access Points (APs), the Multi-hop network serves has a redundant path to send the data collected from DCUs to the Urban Sense cloud database. To plan this highly dynamic network, the Wi-Fi Planner Tool was developed. This tool allowed taking measurements on the field that led to an optimized location of the Multi-hop network nodes with the use of radio propagation models. This tool also allowed rendering a temperature-map style overlay for Google Earth [2] application. For the DCU for garbage street containers the parner company provided the access to a HUB (device that communicates with the sensor inside the garbage containers). The Future Cities use the Raspberry pi as a platform for the DCUs. To collect the data from the HUB a RS485 to RS232 converter was used at the physical level and the Modbus protocol at the application level. To determine the location and status of the vehicles whinin the vehicular network a TCP Server was developed. This application was developed for the OBUs providing the vehicle Global Positioning System (GPS) location as well as information of when the vehicle is stopped, moving, on idle or even its slope. To implement the Multi-hop network on the field some scripts were developed such as pingLED and “shark”. These scripts helped upon node deployment on the field as well as to perform all the tests on the network. Two setups were implemented on the field, an urban setup was implemented for a Multi-hop network coverage survey and a sub-urban setup was implemented to test the Multi-hop network routing protocols, Optimized Link State Routing Protocol (OLSR) and Babel

    Diseño de una plataforma de lectura, test y caracterización para ROICs de rayos X basada en el microprocesador LEON

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    Aquest project es situa dins del marc del CNM-IMB (CSIC). Consisteix en el disseny d'un sistema de biòpsia mamaria en temps real. Per realitzar aquest sistema s'ha dissenyat una plataforma de lectura, test y caracterització pel ROIC Medipix2 que es basa en el microprocessador LEON3 i és programat sobre una FPGA.Este proyecto se sitúa dentro de marco del CNM-IMB (CSIC). Consiste en el diseño de un sistema de biopsia mamaria en tiempo real. Para poder realizar este sistema se ha diseñado una plataforma de lectura, test y caracterización para el ROICs Medipix2 basada en el microprocesador LEON3 y programado sobre una FPGA.This project is within CNM-IMB (CSIC)'s scope. It consists of the design of a breast biopsy system in real time. To make this system has been designed a reader, test and characterization platform for ROIC Medipix2 based on LEON3 microprocessor and programmed on FPGA.Nota: Aquest document conté originàriament altre material i/o programari només consultable a la Biblioteca de Ciència i Tecnologia

    Autonomous Flight, Fault, and Energy Management of the Flying Fish Solar-Powered Seaplane.

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    The Flying Fish autonomous unmanned seaplane is designed and built for persistent ocean surveillance. Solar energy harvesting and always-on autonomous control and guidance are required to achieve unattended long-term operation. This thesis describes the Flying Fish avionics and software systems that enable the system to plan, self-initiate, and autonomously execute drift-flight cycles necessary to maintain a designated watch circle subject to environmentally influenced drift. We first present the avionics and flight software architecture developed for the unique challenges of an autonomous energy-harvesting seaplane requiring the system to be: waterproof, robust over a variety of sea states, and lightweight for flight. Seaplane kinematics and dynamics are developed based on conventional aircraft and watercraft and upon empirical flight test data. These models serve as the basis for development of flight control and guidance strategies which take the form of a cyclic multi-mode guidance protocol that smoothly transitions between nested gain-scheduled proportional-derivative feedback control laws tuned for the trim conditions of each flight mode. A fault-tolerant airspeed sensing system is developed in response to elevated failure rates arising from pitot probe water ingestion in the test environment. The fault-tolerance strategy utilizes sensor characteristics and signal energy to combine redundant sensor measurements in a weighted voting strategy, handling repeated failures, sensor recovery, non-homogenous sensors, and periods of complete sensing failure. Finally, a graph-based mission planner combines models of global solar energy, local ocean-currents, and wind with flight-verified/derived aircraft models to provide an energy-aware flight planning tool. An NP-hard asymmetric multi-visit traveling salesman planning problem is posed that integrates vehicle performance and environment models using energy as the primary cost metric. A novel A* search heuristic is presented to improve search efficiency relative to uniform cost search. A series of cases studies are conducted with surface and airborne goals for various times of day and for multi-day scenarios. Energy-optimal solutions are identified except in cases where energy harvesting produces multiple comparable-cost plans via negative-cost cycles. The always-on cyclic guidance/control system, airspeed sensor fault management algorithm, and the nested-TSP heuristic for A* are all critical innovation required to solve the posed research challenges.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91453/1/eubankrd_1.pd
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