The capability to extract human activity context in a room environment can be used as meaningful feedback for various wireless indoor application systems. Being able to do so with easily installable resource-limited s...
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The capability to extract human activity context in a room environment can be used as meaningful feedback for various wireless indoor application systems. Being able to do so with easily installable resource-limited sensing components can even further increase the system's applicability for various purposes. This paper introduces our efforts to design a system consisting of heterogeneous low-cost, resource-limited, wireless sensing platforms for accurately extracting the human activity context from an indoor environment. Specifically, we introduce Resource Limited Sensor-based activity Context Extraction (ReLiSCE), a system consisting of microphone array, passive infra-red (PIR), and illumination sensors that effectively detect the activities that occur in an office (meeting room) environment. The signal processing schemes used in ReLiSCE are designed so that their size and complexity is suitable for the resource limitations that many embedded computing platforms introduce. Using empirical evaluations with a prototype system, we show that despite the simplicity of its data processing schemes, ReLiSCE successfully classifies human activity states in various meeting scenarios. Furthermore, we show that high accuracy is achieved by combining results from heterogeneous sensors. We foresee this paper as a sub-system that interconnects with various application systems for autonomously configuring people's everyday living environments in a more comfortable and energy-efficient manner.
Supporting standard text-based protocols in embedded systems is challenging because of the often limited computational resources that embedded systems provide. To overcome this issue, a promising approach is to build ...
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Supporting standard text-based protocols in embedded systems is challenging because of the often limited computational resources that embedded systems provide. To overcome this issue, a promising approach is to build parsers directly in the hardware. Unfortunately, developing such parsers is a daunting task for most developers as it is at the crossroads of several areas of expertise, such as low-level network programming or hardware design. In this letter, we propose Zebra, a generative approach that drastically eases the development of hardware parsers and their use in network applications. To validate our approach, we used Zebra to generate hardware parsers for widely used protocols, including HTTP, SMTP, SIP, and RTSP. Our experiments show that Zebra-based parsers are up to 11 times faster than software-based parsers.
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