• 전자공학회논문지 IE
• /
• v.47 no.3
• /
• pp.1-6
• /
• 2010

In this paper, it is designed a processor using embedded system so that moving picture can be expressed on LED electric sign board which has been expressed a simple message only like as a character or graphic. It has been fabricated a moving picture LED electric sign board which is composed to a video processor and LED display panel, in order to be able to express a digital moving picture of 24 bits that is transmitted from embedded system. It includes gamma adjustment, brightness, color contrast control, a schedule function, expression image conversion by the Internet and memory device. Also, an application program based Windows CE is designed so that a character, graphic, and moving picture can be expressed on a small LED electric sign board.

• 전자공학회논문지 IE
• /
• v.47 no.1
• /
• pp.48-55
• /
• 2010

The goal of this paper is to determine optimal hardware platform for specific applications. In order to develop an understanding of how select the optimal platform, we focus upon the real-time embedded vehicle system for processing forward image and sound. In this paper we propose to measure parameters such as instructions, execution cycle, required memory size for program and data by using ARMulator. We have measured three types of processor cores: ARM7, ARM9 and ARM10. The results of the study indicated that the proposed methods could measure the minimal requirements of hardware platform for specific applications. By defining lower limit of hardware specifications in embedded systems, we can minimize expenses with suitable system performance without implementing the system.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.10 no.5
• /
• pp.297-305
• /
• 2015

AFLS (Adaptive front lighting System) is being applied to improve safety in driving automotive at night. Safe embedded system for controlling head-lamp has to be tightly designed by considering safety requirement of hardware-dependent software, which is embedded in automotive ECU(Electronic Control Unit) hardware under severe environmental noise. In this paper, we propose an adaptive headlight controller with newly-designed symmetric angle sensor compensator, which is integrated with ECU-based adaptive front light system. The proposed system, on which additional backup hardware and emergency control algorithm are integrated, effectively detects abnormal situation and restore safe status of controlling the light-angle in AFLS operations by comparing result in symmetric angle sensor. The controlled angle value is traced into internal memory in runtime and will be continuously compared with the pre-defined lookup table (LUT) with symmetric angle value, which is used in normal operation. The watch-dog concept, which is based on using angle sensor and control-value tracer, enables quick response to restore safe light-controlling state by performing the backup sequence in emergency situation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.20 no.6
• /
• pp.104-110
• /
• 2006

In this paper, we proposed the micro web-server implementation on Strong ARM processor with embedded Linux. The parallel port connecting parallel I/O is controlled via HTTP protocol and web browser program HTTP protocol with Linux, the micro web server program and port control program are installed on-board memory using CGI to be accessed by web browser. The processor parallel input port is monitored and parallel output port is controlled from remote hosts via HTTP protocol. The result of the proposed embedded micro-web server can be used in remote automation systems, distributed control via internet using web browser.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.1
• /
• pp.136-140
• /
• 2015

Linux environment that is commonly used at embedded systems supports various file systems as Ext2, FAT, NTFS, etc. The file system that is equiped on the embedded system is mostly implemented on mini hard disk or flash memory. The types of the file system of the system make an effect on the performance of a application programs. The factors of file system performance on a same media are block read, block write and block free time. On these factors, block read and block free time are not so different according to the type of file systems. This paper evaluates the performance benchmark of file systems supported by linux about block allocation and write performance. The results obtained from various experiments shows the characteristics of each file system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.355-357
• /
• 2014

Linux environment that is commonly used at embedded systems, supports various file systems as Ext2, FAT, NTFS, ets. The file system that is equiped on the embedded system is mostly implemented on mini hard disk or flash memory. The types of the file system of the system make an effect on the performance of a application programs. The factors of file system performance on a same media are block allocation and block free time. On these factors, block free time is not so different according to the type of file systems. This thesis performs the performance benchmark of a Ext2, FAT and NTFS file systems about block allocation performance. As the result, it is discussed that what file system is better at which case.

• Journal of Information Processing Systems
• /
• v.16 no.6
• /
• pp.1359-1371
• /
• 2020

In transmitting and receiving such a large amount of data, reliable data communication is crucial for normal operation of a device and to prevent abnormal operations caused by errors. Therefore, in this paper, it is assumed that an error correction code (ECC) that can detect and correct errors by itself is used in an environment where massive data is sequentially received. Because an embedded system has limited resources, such as a low-performance processor or a small memory, it requires efficient operation of applications. In this paper, we propose using an accelerated ECC-decoding technique with a graphics processing unit (GPU) built into the embedded system when receiving a large amount of data. In the matrix-vector multiplication that forms the Hamming code used as a function of the ECC operation, the matrix is expressed in compressed sparse row (CSR) format, and a sparse matrix-vector product is used. The multiplication operation is performed in the kernel of the GPU, and we also accelerate the Hamming code computation so that the ECC operation can be performed in parallel. The proposed technique is implemented with CUDA on a GPU-embedded target board, NVIDIA Jetson TX2, and compared with execution time of the CPU.

• Journal of Korea Multimedia Society
• /
• v.25 no.1
• /
• pp.39-51
• /
• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.4
• /
• pp.203-210
• /
• 2024

The automotive industry is transitioning from traditional internal combustion engines to systems powered by motors, batteries, and various electronic control units. Central to this shift is the micro-controller unit, which processes data from various sensors for real-time environmental awareness and control. This paper explores using non-contact magnetic sensors for sensing vehicle inclination as part of a digital twin implementation. Unlike optical or contact sensors, non-contact magnetic sensors offer robust performance in challenging environments, providing consistent and reliable data under varying conditions. To optimize real-time data processing, we propose a double buffer structure to enhance digital signal processing performance in embedded systems. Experiments using a custom sensor-integrated board demonstrate that the double buffer structure with direct memory access-enabled serial peripheral interface significantly reduces data processing time and improves noise reduction filtering. Our results show that the proposed system can greatly enhance the reliability and accuracy of sensor data, crucial for real-time vehicle control systems. In particular, by using the double buffer structure proposed in this paper, it was possible to secure 8.27 times more data compared to raw data, despite performing additional filtering. The techniques outlined have potential applications in various fields, offering enhanced monitoring and optimization capabilities, thus paving the way for more advanced and efficient vehicle control technologies.

• Journal of IKEEE
• /
• v.28 no.3
• /
• pp.397-404
• /
• 2024

This paper presents the design and implementation results of a Raspberry-Pi-based embedded system with an FPGA accelerator that can detect and classify objects using an FMCW radar sensor for preventing door collision accidents in vehicles. The proposed system performs a radar sensor signal processing and a deep learning processing that classifies objects into bicycles, automobiles, and pedestrians. Since the CNN algorithm requires substantial computation and memory, it is not suitable for embedded systems. To address this, we implemented a lightweight deep learning model, BNN, optimized for embedded systems on an FPGA, and verified the results achieving a classification accuracy of 90.33% and an execution time of 20ms.