• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.122-122
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• 2001

In this research, an Electronic White Board Control System is developed that stores and prints the data written on the white board. This control system is constructed using an embedded single board PC, whose function is the acquisition(from CCD image sensor system), processing and storing of the data on the screen. The properties of this system are as follows: 1. The system is able to move the screen on the right and left. 2. The system is able to output the data written on the screen to a connected printer. 3. The system is controlled by an external remote PC connected to the system by serial line as well as by the keys on the control panel. The data written on the screen can be sent to the external PC and then can be modified and stored...

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.6
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• pp.353-359
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• 2016

In this paper, we propose a high performance L1 cache structure for the high clock CPU. The proposed cache memory consists of three parts, i.e., a direct-mapped cache to support fast access time, a two-way set associative buffer to reduce miss ratio, and a way-select table. The most recently accessed data is stored in the direct-mapped cache. If a data has a high probability of a repeated reference, when the data is replaced from the direct-mapped cache, the data is stored into the two-way set associative buffer. For the high performance and fast access time, we propose an one way among two ways set associative buffer is selectively accessed based on the way-select table (WST). According to simulation results, access time can be reduced by about 7% and 40% comparing with a direct cache and Intel i7-6700 with two times more space respectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.2
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• pp.57-66
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• 2010

In this paper, we propose an enhanced voice recognition algorithm using adaptive MFCC(Mel Frequency Cepstral Coefficients) and neural network. Though it is very important to extract voice data from the raw data to enhance the voice recognition ratio, conventional algorithms are subject to deteriorating voice data when they eliminate noise within special frequency band. Differently from the conventional MFCC, the proposed algorithm imposed bigger weights to some specified frequency regions and unoverlapped filterbank to enhance the recognition ratio without deteriorating voice data. In simulation results, the proposed algorithm shows better performance comparing with MFCC since it is robust to variation of the environment.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.401-403
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• 2007

This paper present the Traffic information system that based on embedded WinCE board which has GPSand HSDPA. This system is able to overcome the limit of area using the Internet service other system can't provide. When the embedded board receives the data which has geometric and vehicle speed information, it transmits the data to server via HSDPN/the Internet. The server receives and processes the data for the path services. By an algorithm the data that road information is applied is provided to user. The users will be able to arrive there destination faster.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.412-428
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• 2014

Request oriented sensor networks have stricter requirements than conventional event-driven or periodic report models. Therefore, in this paper we propose a minimum energy data aggregation (MEDA), which meets the requirements for request oriented sensor networks by exploiting a low power real-time scheduler, on-demand time synchronization, variable response frame structure, and adaptive retransmission. In addition we introduce a test bed consisting of a number of MEDA prototypes, which support near real-time bidirectional sensor networks. The experimental results also demonstrate that the MEDA guarantees deterministic aggregation time, enables minimum energy operation, and provides a reliable data aggregation service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5669-5684
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• 2018

The new mobile edge network architecture has been required for an increasing amount of traffic, quality requirements, advanced driver assistance system for autonomous driving and new cloud computing demands on highway. This article proposes a hierarchical cloud computing architecture to enhance performance by using adaptive data load distribution for buses that play the role of edge computing server. A vehicular dynamic cloud is based on wireless architecture including Wireless Local Area Network and Long Term Evolution Advanced communication is used for data transmission between moving buses and cars. The main advantages of the proposed architecture include both a reduction of data loading for top layer cloud server and effective data distribution on traffic jam highway where moving vehicles require video on demand (VOD) services from server. Through the description of real environment based on NS-2 network simulation, we conducted experiments to validate the proposed new architecture. Moreover, we show the feasibility and effectiveness for the connected car media service on highway.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.113-117
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• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.14-19
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• 2007

As XML is used by standard format for information expression and information exchange in various field, Many Company began to use SVG by user interface or information expression tool of embedded system such as mobile phone based wireless internet. Because SVG has many additional information to keep structure of SVG document exception real data, there is a problem that transfer time of SVG file is so cost for quantity of transmitted data actually. To solve this problem, many researches using compression conception have been conducted for applying to an embedded system. This paper proposes SMETA that can use existing researches using compression concept at once. SMETA divides SVG file to each part that can allocate meaning, and gives semantic metadata to each part without alteration of SVG structure. SMETA can reduce site of transmitted SVG file actually by transmitting specification part of SVG file that metadata does not agree or has unlisted part in an embedded system between an embedded system and server, before transmit whole SVG file. By size of transmitting SVG file is decreasing, transfer time can be shortened accordingly.

• Journal of Platform Technology
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• v.8 no.4
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• pp.11-19
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• 2020

In an autonomous driving environment, dynamic recognition of objects is essential as the situation changes in real time. In addition, as the number of sensors and control modules built into an autonomous vehicle increases, the amount of data the central control unit has to process also rapidly increases. By minimizing the output data from the sensor, the load on the central control unit can be reduced. This study proposes a dynamic object recognition algorithm solely using the embedded processor on a LiDAR sensor. While there are open source algorithms to process the point cloud output from LiDAR sensors, most require a separate high-performance processor. Since the embedded processors installed in LiDAR sensors often have resource constraints, it is essential to optimize the algorithm for efficiency. In this study, an embedded processor based object recognition algorithm was developed for autonomous vehicles, and the correlation between the size of the point clouds and processing time was analyzed. The proposed object recognition algorithm evaluated that the processing time directly increased with the size of the point cloud, with the processor stalling at a specific point if the point cloud size is beyond the threshold

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.