Real-Time Data Compression on an Embedded Controller

碩士 === 國立中興大學 === 電機工程學系 === 91 === For various working environments and requirements, it is necessary to acquire information from remote site such as voltage, temperature, and humidity. Putting a computer at the remote site is too expensive and occupies lots of spaces. So, the best choic...

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Bibliographic Details
Main Authors: LAI HSUEH LIANG, 賴學良
Other Authors: Jan-Ray Liao
Format: Others
Language:zh-TW
Published: 2003
Online Access:http://ndltd.ncl.edu.tw/handle/78156980896411558718
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Summary:碩士 === 國立中興大學 === 電機工程學系 === 91 === For various working environments and requirements, it is necessary to acquire information from remote site such as voltage, temperature, and humidity. Putting a computer at the remote site is too expensive and occupies lots of spaces. So, the best choice is to put an embedded controller at the site. Now, there are many embedded controllers with multiple functions, small space, cheap price, and different communication protocols to choose from. The microprocessor installed in the embedded controller can process the acquired data. The built-in operating system can start up quickly after the power was down. The application programs were written in C language, and can be downloaded to the embedded controller to solve various problems. This thesis simulated data acquired from a muti-meter. The data were transmitted to the user for processing. How to save the transmission time of the data under the constraints of the limited memory space is the main topic of the thesis. To save the resources of the network and time, we should collect the data for a period of time at the embedded controller before sending them out. To save more data or deduce the memory space, it is necessary to find a way to compress the data so that the storage capacity can be increased and the transmitting time can be reduced. The thesis attempts to investigate the current compression techniques to understand their the compression ratios, and analyze the execution time and the complexity of the algorithms. We choose the best algorithm which fits all the abore requirements to achieve the highest system efficiency. Comparing the compression ratio, execution time, and program size, we chose 〝arithmetic coding〞to as the compression algorithm for our embedded controller.