• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1027-1028
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• 2006

In this paper, design of current and speed controller for DC motor drive system using Embedded Target for TI C2000DSP in Matlab/Simulink is introduced. Current and speed controller is designed and implemented using program simply and easily, and speed control response of DC motor can be advanced. Current and speed control of DC motor is carried in eZdsp F2812 control board using Embeded Target for TI C2000DSP in Matlab/Simulink. Speed feedback is processed through A/D converter using tacho generator as speed sensor, and current feedback is processed through A/D converter using hall sensor as current sensor. Controller is designed to PI current controller and PI speed controller. Current and speed response is verified through simulations and experiments.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.27-34
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• 2017

The signal transmitted from radar is not reflected from a single point when the signal reflected by complex target. Resultantly, the amplitude and phase of the received signal can be changed because the target has lots of scatterers. The changes of the amplitude and the phase mean Glint and RCS, respectively. Although the Glint and RCS that caused by the same scatters are uncorrelated, however, they are not independent completely. Therefore, this paper proposes a method for generating the Glint and RCS by using same random number generator. And the time correlations of the Glint and RCS are respectively implemented in frequency domain by using each power spectral density of them.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.17-24
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• 2009

According to increasing requirments in embedded systems, embedded software has been more complicated then before, a software developer is required to test her/his software to make a efficient embedded system software in both time and space. This paper suggests a testing tool with which a software developer can easily test the embedded system software in cross-development environments. The suggested tool is designed based on host/target architecture, to provide an intuitive test environment in which a test case can be executed in a target board. The tool uses an XML-based test script to generate an appropriate test case. It includes a tree-based test script editor with which a developer can easily make a test case. Therefore, with the suggested tool, a develop can put down a burden on an software testing and get more productivity in software development related on embedded system.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.474-479
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• 2014

LEON3 is a 32-bit synthesizable processor based on the SPARC V8. It can be connected to AMBA 2.0 bus and has a 7- stage pipeline, IEEE-754 FPU and 256[KB] cache. It can be easily implemented using FPGA and used for a SoC design. DSU which comes with LEON3 can be used to control and monitor the operation of LEON3. And DSU makes it easy to set a debugging environment for the development of both hardware and software for an embedded systems based on LEON3. This paper presents the summary of the debugging tool for LEON3 based embedded systems. The debugging tool can initialize the target hardware, find out how the target hardware is configured, load application code to a specified memory space and run that application code. To provide users a debugging environment, it can set breakpoints and control the operation of LEON3 correspondingly. And function call trace is one of key functions of the debugging tool.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.569-572
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• 2008

I proposed wireless ordering system using Qt embedded. This system will make the good use of real life applications and make fit for practical use. I embodied data input, transfer, gathering and back-transfer on wireless network using TCP/IP protocol on QSocket provided by Qt. Server environment is Linux 9.0 and gcc-2.95.3 on PC, and three target-boards consist PXA255 processor with wireless-LAN card which communicate through wireless access pointer. Software tools on target-board are gcc, tmake, qt-X11, qt-embedded and etc. I designed touch-screen interface for user convenience.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.635-638
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• 1998

This paper proposes a simple method to measure system's performance in target tracking problems. Essentially employing the Cramer-Rao lower bound (CRLB) on trakcing accuracy, an algorithm of predicting system's performance under various scenarios is developed. The input data is a collection of measurements over time fromsensors embedded in gaussian noise. The target of interest may not maneuver over the processing time interval while the own ship observing platform may maneuver in an arbitrary fashion. Th eproposed approach is demonstrated and discussed through simulation results.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.451-458
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• 2002

In this article, we present, design, and implement a software and hardware for an embedded RAID system. The merits and drawbacks of our system are presented by performance evaluation. The proposed hardware system consists of three fibre channel controllers for the interface with fibre channel disks and hosts. Embedded Linux in which a RAID software is implemented is ported to the hardware. A SCSI target mode device driver and a target mode SCSI module are designed for that our RAID system is considered as a block device to a host computer. Linux Multi-device is used as RAID functions of this system. A data cache module is implemented for high performance and the interconnection between Linux Multi-device and the target mode SCSI module. The RAID 5 module of Multi-device is modified for improvement of read performance. The benchmark shows that the new RAID 5 module is superior to the original one in overall performance.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.2
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• pp.31-42
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• 2014

Debugger systems are necessary to apply dynamic program analysis when evaluating security properties of embedded system software. It may be possible to make the use of software-based debugger and/or DBI framework if target devices support general purpose operating systems, however, constraints on applicability as well as environmental transparency might be incurred thereby hindering overall analyzability. Analysis with JTAG (IEEE 1149.1) debugging devices can overcome these difficulties in that no change would be involved in terms of internal software environment. In that sense, JTAG API can facilitate to practically perform dynamic program analysis for evaluating security properties of target device software. In this paper, we introduce an implementation of JTAG API to enable analysis of ARM core based embedded systems. The API function set includes the categories of debugger and target device controls: debugging environment and operation. To verify API applicability, we also provide example analysis tool implementations: our JTAG API could be used to build kernel function fuzzing and live memory forensics modules.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.375-382
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• 2022

This paper proposes a multi-core cooperative computing structure considering the heterogeneous features of automotive embedded on-chip software. The automotive embedded software has the heterogeneous execution flow properties for various hardware drives. Software developed with a homogeneous execution flow without considering these properties will incur inefficient overhead due to core latency and load. The proposed method was evaluated on an target board on which a automotive MCU (micro-controller unit) with built-in multi-cores was mounted. We demonstrate an overhead reduction when software including common embedded system tasks, such as ADC sampling, DSP operations, and communication interfaces, are implemented in a heterogeneous execution flow. When we used the proposed method, embedded software was able to take advantage of idle states that occur between heterogeneous tasks to make efficient use of the resources on the board. As a result of the experiments, the power consumption of the board decreased by 42.11% compared to the baseline. Furthermore, the time required to process the same amount of sampling data was reduced by 27.09%. Experimental results validate the efficiency of the proposed multi-core cooperative heterogeneous embedded software execution technique.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.89-100
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• 2023

Recently, on-device artificial intelligence (AI) solutions using mobile devices and embedded edge devices have emerged in various fields, such as computer vision, to address network traffic burdens, low-energy operations, and security problems. Although vision transformer deep learning models have outperformed conventional convolutional neural network (CNN) models in computer vision, they require more computations and parameters than CNN models. Thus, they are not directly applicable to embedded edge devices with limited hardware resources. Many researchers have proposed various model compression methods or lightweight architectures for vision transformers; however, there are only a few studies evaluating the effects of model compression techniques of vision transformers on performance. Regarding this problem, this paper presents a performance evaluation of vision transformers on embedded platforms. We investigated the behaviors of three vision transformers: DeiT, LeViT, and MobileViT. Each model performance was evaluated by accuracy and inference time on edge devices using the ImageNet dataset. We assessed the effects of the quantization method applied to the models on latency enhancement and accuracy degradation by profiling the proportion of response time occupied by major operations. In addition, we evaluated the performance of each model on GPU and EdgeTPU-based edge devices. In our experimental results, LeViT showed the best performance in CPU-based edge devices, and DeiT-small showed the highest performance improvement in GPU-based edge devices. In addition, only MobileViT models showed performance improvement on EdgeTPU. Summarizing the analysis results through profiling, the degree of performance improvement of each vision transformer model was highly dependent on the proportion of parts that could be optimized in the target edge device. In summary, to apply vision transformers to on-device AI solutions, either proper operation composition and optimizations specific to target edge devices must be considered.