• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.10
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• pp.75-82
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• 2008

In this paper, we propose an efficient symbol detection algorithm for multiple-input multiple-output spatial multiplexing (MIMO-SM) systems and present its design and implementation results. By enhancing the performance of the first detected symbol which causes error propagation, the proposed algorithm achieves a considerable performance gain as compared to the conventional sorted QR decomposition (SQRD) based detection and the ordered successive detection (OSD) algorithms. The bit error rate (BER) performance of the proposed detection algorithm is evaluated by the simulation. In case of 16QAM MIMO-SM system with 4 transmit and 4 receive ($4{\times}4$) antennas, at $BER=10^{-3}$ the proposed algorithm obtains the gai improvement of about 2.5-13.5 dB over the conventional algorithms. The proposed detection algorithm was designed in a hardware description language (HDL) and synthesized to gate-level circuits using 0.18um 1.8V CMOS standard cell library. The results show that the proposed algorithm can be implemented without increasing the hardware costs significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11C
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• pp.940-948
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• 2008

This paper presents a low-power, low-complexity design and implementation results of a high speed multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless LAN (WLAN) baseband processor. The proposed processor is composed of the physical layer convergence procedure (PLCP) processor and physical medium dependent (PMD) processor, which have been optimized to have low-power and reduced-complexity architecture. It was designed in a hardware description language (HDL) and synthesized to gate-level circuits using 0.18um CMOS standard cell library. As a result, the proposed TX-PLCP processor reduced the power consumption by as much as 81% over the bit-level operation architecture. Also, the proposed MIMO symbol detector reduced the hardware complexity by 18% over the conventional SQRD-based architecture with division circuits and square root operations.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Publications of The Korean Astronomical Society
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• v.21 no.2
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• pp.67-74
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• 2006

We have developed a control electronics system for an infrared detector array of KASINICS (KASI Near Infrared Camera System), which is a new ground-based instrument of the Korea Astronomy and Space science Institute (KASI). Equipped with a $512{\times}512$ InSb array (ALADDIN III Quadrant, manufactured by Raytheon) sensitive from 1 to $5{\mu}m$, KASINICS will be used at J, H, Ks, and L-bands. The controller consists of DSP(Digital Signal Processor), Bias, Clock, and Video boards which are installed on a single VME-bus backplane. TMS320C6713DSP, FPGA(Field Programmable Gate Array), and 384-MB SDRAM(Synchronous Dynamic Random Access Memory) are included in the DSP board. DSP board manages entire electronics system, generates digital clock patterns and communicates with a PC using USB 2.0 interface. The clock patterns are downloaded from a PC and stored on the FPGA. UART is used for the communication with peripherals. Video board has 4 channel ADC which converts video signal into 16-bit digital numbers. Two video boards are installed on the controller for ALADDIN array. The Bias board provides 16 dc bias voltages and the Clock board has 15 clock channels. We have also coded a DSP firmware and a test version of control software in C-language. The controller is flexible enough to operate a wide range of IR array and CCD. Operational tests of the controller have been successfully finished using a test ROIC (Read-Out Integrated Circuit).

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.2
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• pp.87-92
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• 2015

Purpose When the patients takes myocardial perfusion SPECT using $^{201}Tl$, the operator gives the patients an injection of $^{201}Tl$. But the uniformity correction map in SPECT uses $^{99m}Tc$ uniformity correction map. Thus, we want to compare the image quality when it uses $^{99m}Tc$ uniformity correction map and when it uses $^{201}Tl$ uniformity correction map. Materials and Methods Phantom study is performed. We take the data by Asan medical center daily QC condition with flood phantom including $^{201}Tl$ 21.3 kBq/mL. After postprocessing with this data, we analyze CFOV integral uniformity(I.U) and differential uniformity(D.U). And we take the data with Jaszczak ECT Phantom by American college of radiology accreditation program instruction including $^{201}Tl$ 33.4 kBq/mL. After post processing with this data, we analyze spatial Resolution, Integral Uniformity(I.U), coefficient of variation(C.V) and Contrast with Interactive data language program. Results In the flood phantom test, when it uses $^{99m}Tc$ uniformity correction map, Flood I.U is 3.6% and D.U is 3.0%. When it uses $^{201}Tl$ uniformity correction map, Flood I.U is 3.8% and D.U is 2.1%. The flood I.U is worsen about 5%, but the D.U is improved about 30% inversely. In the Jaszczak ECT phantom test, when it uses $^{99m}Tc$ uniformity correction map, SPECT I.U, C.V and contrast is 13.99%, 4.89% and 0.69. When it uses $^{201}Tl$ uniformity correction map, SPECT I.U, C.V and contrast is 11.37%, 4.79% and 0.78. All of data are improved about 18%, 2%, 13% The spatial resolution was no significant changes. Conclusion In the flood phantom test, Flood I.U is worsen but Flood D.U is improved. Therefore, it's uncertain that an image quality is improved with flood phantom test. On the other hand, SPECT I.U, C.V, Contrast are improved about 18%, 2%, 13% in the Jaszczak ECT phantom test. This study has limitations that we can't take all variables into account and study with two phantoms. We need think about things that it has a good effect when doctors decipher the nuclear medicine image and it's possible to improve the image quality using the uniformity correction map of other radionuclides other than $^{99m}Tc$, $^{201}Tl$ when we make other nuclear medicine examinations.

• Progress in Medical Physics
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• v.26 no.3
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• pp.160-167
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• 2015

Radiation exposure from medical diagnostic imaging procedures to patients is one of the most significant interests in diagnostic x-ray system. A miniature x-ray intraoral tube was developed for the first time in the world which can be inserted into the mouth for imaging. Dose evaluation should be carried out in order to utilize such an imaging device for clinical use. In this study, dose evaluation of the new x-ray unit was performed by 1) using a custom made in vivo Pig phantom, 2) determining exposure condition for the clinical use, and 3) measuring patient dose of the new system. On the basis of DRLs (Diagnostic Reference Level) recommended by KDFA (Korea Food & Drug Administration), the ESD (Entrance Skin Dose) and DAP (Dose Area Product) measurements for the new x-ray imaging device were designed and measured. The maximum voltage and current of the x-ray tubes used in this study were 55 kVp, and 300 mA. The active area of the detector was $72{\times}72mm$ with pixel size of $48{\mu}m$. To obtain the operating condition of the new system, pig jaw phantom images showing major tooth-associated tissues, such as clown, pulp cavity were acquired at 1 frame/sec. Changing the beam currents 20 to $80{\mu}A$, x-ray images of 50 frames were obtained for one beam current with optimum x-ray exposure setting. Pig jaw phantom images were acquired from two commercial x-ray imaging units and compared to the new x-ray device: CS 2100, Carestream Dental LLC and EXARO, HIOSSEN, Inc. Their exposure conditions were 60 kV, 7 mA, and 60 kV, 2 mA, respectively. Comparing the new x-ray device and conventional x-ray imaging units, images of the new x-ray device around teeth and their neighboring tissues turn out to be better in spite of its small x-ray field size. ESD of the new x-ray device was measured 1.369 mGy on the beam condition for the best image quality, 0.051 mAs, which is much less than DRLs recommended by IAEA (International Atomic Energy Agency) and KDFA, both. Its dose distribution in the x-ray field size was observed to be uniform with standard deviation of 5~10 %. DAP of the new x-ray device was $82.4mGy*cm^2$ less than DRL established by KDFA even though its x-ray field size was small. This study shows that the new x-ray imaging device offers better in image quality and lower radiation dose compared to the conventional intraoral units. In additions, methods and know-how for studies in x-ray features could be accumulated from this work.