• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.47-53
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• 2017

In this paper, we present open hardware platforms for Internet of Things (IoTs) emphasizing their strengths and weaknesses. We introduce six representative platforms, Raspberry PI, Arduino, Garileo, Edison, Beagle board and Artik. We define important performance issues for open hardware platforms for IoTs and analyze recent platforms according to the performance issues. We present recent research project using open hardware platforms introduced in this paper. We believe that this paper provide wise view and necessary information for open hardware platforms for Internet of Things (IoT).

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.595-602
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• 2016

With the evolution and development of many kinds of healthcare devices and techniques, u-health standards have emerged as a major issue. Yet, most legacy medical devices and systems are still being used without deployment of the standards. Therefore, it is required to support backward compatibility for u-health standard-compliant systems to communicate with legacy non-standard medical and healthcare devices. This paper proposes a new scheme to support backward compatibility of IEEE 11073 system by adding a codec module to IEEE 11073 agent. The codec converts data sent by non-standard health devices to IEEE 11073 MDER data. Plus, we implemented the proposed IEEE 11073 agent with an Intel Edison board which is one of popular open source H/W platforms. The IEEE 11073 manager of the proposed system can monitor and control legacy non-standard devices through the proposed agent system. In our experimental results, we examined the proposed system can support interoperability between u-health standard and non-standard devices and contribute to the growth and expansion of u-health services.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.247-258
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• 2017

Since 2016 when the regulations related to vehicle structure and device modification were drastically revised, the car tuning market has been growing rapidly. Particularly, many drivers are showing interest in changing the interior and exterior according to their preference, or improving the specifications of their cars by changing the engine and powertrain, among others. Also, as the initial engine settings such as horse power and torque of the vehicle are made for stable driving of the vehicle, it is possible to change the engine performance, via Engine Control Unit (ECU) mapping, to the driver's preference. However, traditionally, ECU mapping could be only performed by professional car engineers and the settings were also decided by them. Therefore, this study proposed a system that collects data related to the driver's driving habits for a certain period and sends them to a cloud server in order to analyze them and recommend ECU mapping values. The traditional mapping method only aimed to improve the car's performance and, therefore, if the changes were not compatible with the driver's driving habits, could cause problems such as incomplete combustion or low fuel efficiency. However, the proposed system allows drivers to set legally permitted ECU mapping based on analysis of their driving habits, and, therefore, different drivers can set it differently according to the vehicle specifications and driving habits. As a result, the system can optimize the car performance by improving output, fuel efficiency, etc. within the range that is legally permitted.