HIRFL-CSR前端总线控制器的改进设计

论述了用于兰州重离子加速器冷却存储环(HIRFL-CSR)控制系统的前端总线系统控制器的改进。改进了控制器的嵌入式操作系统和应用程序,开发了控制器和数据库交换数据的应用程序。该控制器基于BGA封装的ARM920T(ARM9)处理器和嵌入式的LINUX操作系统,可以连接标准的VGA显示器、键盘、鼠标,采用了现场可编程的FPGA器件进行背板接口设计,并具有64mA高驱动能力的总线驱动器,以及拥有灵活的接口信号定义可编程能力,是HIRFL-CSR控制系统的关键部件。

HIRFL-CSR嵌入式数据库的设计实现

本文论述了用于兰州重离子加速器冷却储存环(HIRFL-CSR)控制系统的嵌入式数据库的设计和实现方法。控制系统采用三级数据库实现集中管理、分布式控制。前两级基于Windows平台,采用Oracle数据库通过ODBC进行互联,第三级根据控制系统的需要,采用基于嵌入式Linux平台的SQLite数据库引擎通过高速互联网与前两级交换数据。中控室预先将波形数据、事例表等分散存储到前端嵌入式数据库中,实验时,再由嵌入式数据库将数据传递给波形发生器DSP。在同步触发的控制下,DSP根据得到的波形数据产生所需的控制波形,进而控制电源、控制磁场,达到实验目的。

基于ARM920T的HIRFL-CSR前端总线控制器FBC-01

本文论述用于兰州重离子加速器冷却存储环(HIRFL-CSR)控制系统的前端总线系统控制器FBC-01的硬件设计。该控制器是基于0.8mmBGA封装的AT91RM9200(ARM9)处理器,运行嵌入式LINUX操作系统。控制器可以连接标准的VGA显示器、键盘、鼠标,具有通用的10M/100M以太网接口、USB接口、RS-232接口、485接口、CANBUS接口。可以带SD卡、CF卡存储器。该控制器采用现场可编程的FPGA器件设计背板接口,并采用具有64mA高驱动能力的总线驱动器,不仅符合VME规范的电气要求,而且具有灵活的接口信号定义可编程能力,是HIRFL-CSR控制系统的关键部件。

基于嵌入式Linux系统的数字调节器

数字调节器这种控制策略广泛应用于兰州重离子加速器冷却储存环（HIRFL-CSR）电源控制系统及其他工业控制场合，它采用高速微处理器芯片和现场可编程门阵列，对电源的各项性能参数进行精确运算，以控制电源工作总过程。本论文的重点，是数字调节器上基于ARM9处理器和嵌入式Linux操作系统的嵌入式相关技术。论文深入剖析了AT91RM9200处理器和嵌入式Linux的体系结构，给出了引导装入程序Bootloader和Linux内核的启动分析以及移植到硬件平台的整个过程。实现了常见的嵌入式文件系统的移植，以及操作系统外部设备的FPGA驱动。最后通过图形用户接口的应用实现了数字调节器的基本功能

ARM嵌入式系统在纳米操作系统中的应用研究

近年来，随着微纳米科技的迅速发展，机电产品有望向更微观化、高性能化发展，这将促进材料、制造、电子、生物医学、信息等领域新的科学技术出现，在新的科学技术层次上为可持续发展的理论提供物质和技术保障。微纳米科技最终目标是研究和发现微纳尺度物质所具有的新颖的物理、化学和生物学现象与特性。并以此为基础来设计、制作、组装成新的材料、组件或系统，实现与之相应的特定功能，促进新的科学技术发展与变革，这无疑具有十分重要的科学意义和经济价值。而实现这个目标的使能技术便是微纳米尺度下观测、操作和装配的科学方法与相关的技术和装备，因此开展微纳米操作研究具有特别重要的意义。微纳米操作是微纳米制造科学技术的重要内容之一，使用探针模式的机器人化微纳操作方法，实现在微纳米尺度物体的可控操作，对促进我国微纳米科学技术发展具有特别重要的意义。 目前已有的基于探针的纳米技术装置如SPM (Scanning Probe Microscope)是基于探针模式的纳米观测基本装置。在此基础上研究发展的基于探针的纳米操作已成为纳米科技研究的新领域，是目前世界上各国正在大力开发的前沿研究课题。但目前市场上的SPM等纳米观测设备缺乏驱动控制与信息交互功能和开放界面，限制了用户在此基础上开发纳米操作、装配等功能的能力，因而研究具有信息交互能力的、可进行在线操作控制与宏-微-纳观信息交互的纳米操作监控系统，进而发展成具有自动化/机器人化功能的纳米作业系统队纳米科学技术发展、纳米制造的实现无疑具有重要意义。本论文的科研内容是以面向纳米制造的机器人化系统为研究背景，在自主技术的基础上，开展应用ARM嵌入式系统构成纳米作业系统的实时控制器研究。实时多任务的操作控制系统是纳米作业系统的核心技术，可以实时进行基于探针的传感信息采集、状态反馈控制、形貌观测数据生成、作业运动轨迹生成、位置反馈控制等功能的数据处理与实现。本论文重点介绍以SAMSUNG公司的ARM9处理器芯片S3C2410为嵌入式控制器系统的核心，在移植嵌入式Linux作为操作系统的基础上，开发具有实时数据采集与控制指令、通信功能的人机交互界面。基于ARM的实时控制器的研究为探针模式的纳米观测与操作系统开发提供了关键技术，可以提供开放的AFM系统，促进操作型纳米系统的研究与实现，可以保证纳米观测与操作控制的实时性，可以为纳米作业控制方法提供方便的编程、开发功能。本论文主要研究了面向纳米作业的基于ARM嵌入式实时控制器硬件结构及软件系统的研究与开发过程。首先介绍嵌入式系统的基本概念和特点；其次介绍基于SPM模式的纳米操作系统性能与技术特点；第三，根据纳米作业系统的技术功能要求，详细介绍了具有实时多任务管理功能的硬件系统的设计，重点解决核心板和扩展板各部分功能模块的设计；第四，详细介绍了嵌入式Linux操作系统下的应用程序开发模式及开发过程；最后，详细介绍了嵌入式Linux操作系统下的应用程序开发，主要工作是完成SPM纳米操作系统中的ARM开发平台的功能接口模块的调试及Linux系统下多线程技术在本系统中的应用。本次毕业设计已完成ARM开发平台在整个SPM纳米操作系统中要实现的各个功能模块，结合SPM纳米操作系统的实时性问题，进行了ARM开发平台的系统软件架构分析和利用多线程技术的以太网通信实验，在一定程度上提高了纳米操作系统中的实时性和成像质量。

微型ROV控制装置及控制方法

介绍了一种基于嵌入式ARM9技术的微型ROV的控制装置及控制方法。该装置可以同时进行两通道串行通讯,实现微型ROV的视频信号、潜水深度、艏向角度、纵倾角度、横摇角度、电子舱温度等数据的采集和与上位机的通讯传输;该装置可以采集16路模拟量信号和12路数字量信号,输出4路模拟量信号和12路TTL电平信号,实现推进器、水下灯、水下摄像机、云台等ROV功能器件的驱动。该装置具有通讯能力强、集成度高、功耗低等特点,可以满足微型ROV所有的常用功能要求。

Compressão de imagem e vídeo com controlador ARM

Este trabalho foi desenvolvido num estágio na empresa ABS GmbH sucursal em Portugal, e teve como foco a compressão de imagem e vídeo com os padrões JPEG e H.264, respetivamente. Foi utilizada a plataforma LeopardBoard DM368, com um controlador ARM9. A análise do desempenho de compressão de ambos os padrões foi realizada através de programas em linguagem C, para execução no processador DM368. O programa para compressão de imagem recebe como parâmetros de entrada o nome e a resolução da imagem a comprimir, e comprime-a com 10 níveis de quantização diferentes. Os resultados mostram que é possível obter uma velocidade de compressão até 73 fps (frames per second) para a resolução 1280x720, e que imagens de boa qualidade podem ser obtidas com rácios de compressão até cerca de 22:1. No programa para compressão de vídeo, o codificador está configurado de acordo com as recomendações para as seguintes aplicações: videoconferência, videovigilância, armazenamento e broadcasting/streaming. As configurações em cada processo de codificação, o nome do ficheiro, o número de frames e a resolução do mesmo representam os parâmetros de entrada. Para a resolução 1280x720, foram obtidas velocidades de compressão até cerca de 68 fps, enquanto para a resolução 1920x1088 esse valor foi cerca de 30 fps. Foi ainda desenvolvida uma aplicação com capacidades para capturar imagens ou vídeos, aplicar processamento de imagem, compressão, armazenamento e transmissão para uma saída DVI (Digital Visual Interface). O processamento de imagem em software permite melhorar dinamicamente as imagens, e a taxa média de captura, compressão e armazenamento é cerca de 5 fps para a resolução 1280x720, adequando-se à captura de imagens individuais. Sem processamento em software, a taxa sobe para cerca de 23 fps para a resolução 1280x720, sendo cerca de 28 fps para a resolução 1280x1088, o que é favorável à captura de vídeo.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.