• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.835-849
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• 2013

Joint Collaborative Team on Video Coding (JCT-VC), which have founded ISO/IEC MPEG and ITU-T VCEG, has standardized High Efficiency Video Coding (HEVC). Standardization of HEVC has started with purpose of twice or more coding performance compared to H.264/AVC. However, flexible and hierarchical coding block and recursive coding structure are problems to overcome of HEVC standard. Many fast encoding algorithms for reducing computational complexity of HEVC encoder have been proposed. However, it is hard to implement a real-time HEVC encoder only with those fast encoding algorithms. In this paper, for implementation of software-based real-time HEVC encoder, data-level parallelism using SIMD instructions and CPU/GPU multi-threading methods are proposed. And we also proposed appropriate operations and functional modules to apply the proposed methods on HM 10.0 software. Evaluation of the proposed methods implemented on HM 10.0 software showed 20-30fps for $832{\times}480$ sequences and 5-10fps for $1920{\times}1080$ sequences, respectively.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.7
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• pp.39-49
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• 2012

Demand for high resolution video services leads to active studies on high speed video processing. Especially, widespread deployment of multi-core systems accelerates researches on high resolution video processing based on parallelization of multimedia software. Previously proposed parallelization approach could improve the decoding performance. However, some parallelization methods did not consider the entropy decoding and others considered only a partial decoding parallelization. Therefore, we consider parallel entropy decoding integrated with other parallel video decoding process on a multi-core system. We propose a novel parallel decoding method called Integrated Parallelization. We propose a method on how to optimize the parallelization of video decoding when we have a multi-core system with many cores. We parallelized the KTA 2.7 decoder with the proposed technique on an Intel i7 Quad-Core platform with Intel Hyper-Threading technology and multi-threads scheduling. We achieved up to 70% performance improvement using IP method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.329-331
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• 2018

In this paper, we propose a monitoring system that starts with the supply of raw materials for threading, is processed into a lathe machine, and checks for defects of the product are automatically performed by the robot with Smart Factory technology through assembly and disassembly. Completion check according to the production instruction quantity and production instruction is made by checking the production status according to whether or not the raw material is worn by the displacement sensor, and checking the pitch and the contour of the processed female and male to determine OK and NG. The robotic system acts as a relay for loading and unloading of raw materials, pallet transfer, and overall process, and it acts as an intermediary for organically driving. The location information of the threaded products is collected by using the non-contact wireless tag and the energy saving system Production efficiency and utilization rate were checked. The environmental sensor collects the air-conditioning environment data (temperature, humidity), measures the temperature and humidity accurately, and checks the quality of product processing. It monitors and monitors the driving hazard level environment (overheating, humidity) of the product. Controls for CNC and robot module PLC as a heterogeneous system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.2
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• pp.86-100
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• 2015

Cloud computing is a computing term that evolved in the late 2000s, based on utility and consumption of computer resources. Google say that "Cloud computing involves deploying groups of remote servers and software networks that allow different kinds of data sources be uploaded for real time processing to generate computing results without the need to store processed data on the cloud. Cloud computing relies on sharing of resources to achieve coherence and economies of scale, similar to a utility (like the electricity grid) over a network. At the foundation of cloud computing is the broader concept of converged infrastructure and shared services. Cloud computing, or in simpler shorthand just "the cloud", also focuses on maximizing the effectiveness of the shared resources." The cloud service is a smart and/or intelligent service to save private files in any device, anytime, anywhere. Dropbox, OAuth, PAClous are required that the accumulated user's data are archives with cloud service. Currently we suggest an implementation technique to process many tasks to the cloud server with a thread pooling. Thread pooling is one of efficient implementating technique for client and service environment. In this paper, to present the implementation technique we suggest three diagrams in the consideration of software engineering.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.133-137
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• 2018

Arduino is a small microcomputer that is used in a variety of industry fields and especially is widely used as an open source hardware IoT device. The multi-tasking method of Arduino is divided into super loop timing and RTOS thread method. The super loop timing method is simple and easy to understand. However, when one task is long, it affects the execution of the next task. In addition, RTOS threading has the advantage of being able to run without being influenced by other work time. However, Arduino, a small microcomputer, has a disadvantage in that, when the number of threads increases, the context switching time of the thread causes additional time not included in the super loop timing method have. In this paper, we use Arduino Uno R3 and FreeRTOS to analyze these different features, and the task for the experiment is to send 8000 digital signals to the built-in LED port. If two tasks of the same size are executed, the super loop method executes 3 ms faster than FreeRTOS multitasking. If multiple tasks are executed simultaneously, superloop type task is sequential execution and difference in execution time between first task and last task is large. FreeRTOS method can be executed concurrently, but execution time delay of about 30 ms occurs in context switching time.