• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.3
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• pp.263-281
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• 2014

Transcription factors are essential for the regulation of gene expression in plant. They are binding to either enhancer or promoter region of DNA adjacent to the gene and are related to basal transcription regulation, differential enhancement of transcription, development, response to intercellular signals or environment, and cell cycle control. The mechanism in controlling gene expression of transcription can be understood through the assessment of the complete sequence for the maize genome. It is possible that the maize genome encodes 4,000 or more transcription factors because it has undergone whole duplication in the past. Previously, several transcription factors of maize have been characterized. In this review article, the transcription factors were selected using Pfam database, including many family members in comparison with other family and listed as follows: ABI3/VP1, AP2/EREBP, ARF, ARID, AS2, AUX/IAA, BES1, bHLH, bZIP, C2C2-CO-like, C2C2-Dof, C2C2-GATA, C2C2-YABBY, C2H2, E2F/DP, FHA, GARP-ARR-B, GeBP, GRAS, HMG, HSF, MADS, MYB, MYB-related, NAC, PHD, and WRKY family. For analyzing motifs, each amino acid sequence has been aligned with ClustalW and the conserved sequence was shown by sequence logo. This review article will contribute to further study of molecular biological analysis and breeding using the transcription factor of maize as a strategy for selecting target gene.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.119-127
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• 2004

In this paper, an automatic external biphasic defibrillator that removes ventricular fibrillation efficiently with a low discharging energy has been developed. The system is composed of software including a fibrillation detection algorithm and a system control algorithm, and hardware including a high voltage charging/discharging part and a signal processing part. The stability of the developed system has been confirmed through continuous charging/discharging test of 160 times and the detection capability of the real-time fibrillation detection algorithm has been estimated by applying a total of 30 various fibrillation signals. In order to verify the clinical efficiency and safety, the system has been applied to five pigs before and after fibrillation inductions. Also, we have investigated the system efficiency in removing fibrillation by applying two different discharging waveforms, which have the same energy but different voltage levels.

• Proceedings of the Microbiological Society of Korea Conference
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• 2008.05a
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• pp.42-44
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• 2008

Bacterial biofilms are analogous to multi-cellular organisms or to clonal communities of higher organisms. In this respect, it can be demonstrated that biofilms display the type of genetic variation associated with macroorganisms. The formation of genetic variants from biofilms is the result of internally produced and regulated signals and the appearance of these variants coincides with dispersal from the biofilm. Moreover, the generation of such variation, has similar outcomes for the bacterial community, where diversification of phenotypic traits ensures that the bacterial community optimizes its chances of success when dispersing or surviving when challenged with environmental stress. These observations increase the complexity with which we view bacteria and also suggest that microbial systems can serve as models for the testing of eukaryotic ecological theories.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.251-260
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• 1998

The Holter monitoring system is a widely used noninvasive diagnostic tool for ambulatory patient who may be at risk from latent life-threatening cardiac abnormalities. In this paper, we design a high performance intelligent holter monitoring system which is characterized by the small-sized and the low-power consumption. The system hardware consists of one-chip microcontroller(68HC11E9), ECG preprocessing circuit, and flash memory card. ECG preprocessing circuit is made of ECG preamplifier with gain of 250, 500 and 1000, the bandpass filter with bandwidth of 0.05-100Hz, the auto-balancing circuit and the saturation-calibrating circuit to eliminate baseline wandering, ECG signal sampled at 240 samples/sec is converted to the digital signal. We use a linear recursive filter and preprocessing algorithm to detect the ECG parameters which are QRS complex, and Q-R-T points, ST-level, HR, QT interval. The long-term acquired ECG signals and diagnostic parameters are compressed by the MFan(Modified Fan) and the delta modulation method. To easily interface with the PC based analyzer program which is operated in DOS and Windows, the compressed data, that are compatible to FFS(flash file system) format, are stored at the flash memory card with SBF(symmetric block format).

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.451-459
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• 1999

During the electromyographic evaluation for the diagnosis of neuropathy, presence for site of lesion could be predicted by a detection of denervation potentials such as fibrillation potentials or positive sharp waves in a group of muscles. Unfortunately, since denervation potentials are distinguished by examiner's experience, it is possible to make mistake identifying endplate spikes as a denervation potential. The aim of the study was to extract parameters to make an algorithm for quantitative distinction between denervation potentials and endplate spikes. It will help to minimize the examiner's bias and to localize the site of lesion thus increase the reliability on EMG diagnosis. There types of signals, endplate spike, fibrillation potential and positive sharp wave were obtained from the EDB(extensor digitorum brevis) muscle using then neuropathic patients. Eight parameters such as duration, area, slope, peak-to-peak amplitude, positive peak negative peak amplitude, ratio of positive to negative peak amplitude, and number of phase were extracted and compared. As a results, peak-to-peak amplitude, positive peak amplitude, ratio of positive to negative peak amplitude showed statistically significant differences between endplate spikes and denervation potentials. It was concluded that those parameters could be used to establish an algorithm which will improve the accuracies in automated quantitative EMG diagnosis.

• Journal of Biomedical Engineering Research
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• v.34 no.1
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• pp.8-13
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• 2013

Persons with sensorineural hearing impairment have troubles in hearing at noisy environments because of their deteriorated hearing levels and low-spectral resolution of the auditory system and therefore, they use hearing aids to compensate weakened hearing abilities. Various algorithms for hearing loss compensation and environmental noise reduction have been implemented in the hearing aid; however, the performance of these algorithms vary in accordance with external sound situations and therefore, it is important to tune the operation of the hearing aid appropriately in accordance with a wide variety of sound situations. In this study, a sound classification algorithm that can be applied to the hearing aid was suggested. The proposed algorithm can classify the different types of speech situations into four categories: 1) speech-only, 2) noise-only, 3) speech-in-noise, and 4) music-only. The proposed classification algorithm consists of two sub-parts: a feature extractor and a speech situation classifier. The former extracts seven characteristic features - short time energy and zero crossing rate in the time domain; spectral centroid, spectral flux and spectral roll-off in the frequency domain; mel frequency cepstral coefficients and power values of mel bands - from the recent input signals of two microphones, and the latter classifies the current speech situation. The experimental results showed that the proposed algorithm could classify the kinds of speech situations with an accuracy of over 94.4%. Based on these results, we believe that the proposed algorithm can be applied to the hearing aid to improve speech intelligibility in noisy environments.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.479-487
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• 2004

Biopotential signals have been used as command in systems using electrical stimulation of motor nerves to restore movement after an injury to the central nervous system (CNS). In order to use the voluntary EMG (electromyography) among the biopotentials as a control signal for the electrical stimulation of the same muscle for CNS injury patients, it is necessary to remove M-wave of having high magnitude from raw data. We designed an optimal filter for removing the M-wave and preserving the voluntary EMG and showed that the optimal filter is eigen filter. We also proved that the previous method using the prediction error filter(PEF) is a suboptimal filtering in the sense of preserving the voluntary EMG. On basis of the data obtained from a model for M-wave and voluntary EMG and from actual CNS injury patients, with false-positive rate analysis, the proposed adaptive filter showed a very promising performance in comparison with previous method.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.431-438
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• 2004

Medical ultrasonic imaging is a useful imaging facility known to be most safe and easy. It enables physicians to observe the inside structures of the bodies, blood flow, and motions of internal organs. Some physical properties of biologic tissues can also be estimated from backscattered sounds. However, the ultrasonic pulses interrogating the living organisms leave their footprints in the returning signals during imaging. Some significant details are buried in the footprints and their overlaps from adjacent particles. These distortions also decrease the quality of the images. Many research efforts have been made to enhance the image quality and to recover the acoustic information in various ways. In this study, a new interrogation method based on the wavelet and subband filter bank is proposed. It adopts the subband wavelet filters satisfying the perfect-reconstruction (PR) conditions as the interrogating pulses to restore the details useful in tissue characterization and to enhance the image quality. The proposed method was applied to two types of simulations of ultrasonic imaging. The results showed its ability to restore the detailsin the simulated interrogation of biologic tissues, and verified the improved image quality in the simulated imaging of general ultrasonic phantom compared with the conventional method.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.471-478
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• 2004

Electromyogram (EMC) signal generated by voluntary contraction of muscles is often used in a rehabilitation devices such as an upper limb prosthesis because of its distinct output characteristics compared to other bio-signals. This paper proposes an EMG-based human-computer interface (HCI) for the control of the above-elbow prosthesis or the wheelchair. To control such rehabilitation devices, user generates four commands by combining voluntary contraction of two different muscles such as levator scapulae muscles and flexor-extensor carpi ulnaris muscles. The muscle contraction is detected by comparing the mean absolute value of the EMG signal with a preset threshold value. However. since the time difference in muscle firing can occur when the patient tries simultaneous co-contraction of two muscles, it is difficult to determine whether the patient's intention is co-contraction. Hence, the use of the comparison method using a single threshold value is not feasible for recognizing such co-contraction motion. Here, we propose a novel method using double threshold values composed of a primary threshold and an auxiliary threshold. Using the double threshold method, the co-contraction state is easily detected, and diverse interface commands can be used for the EMG-based HCI. The experimental results with real-time EMG processing showed that the double threshold method is feasible for the EMG-based HCI to control the myoelectric prosthetic hand and the powered wheelchair.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.701-709
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• 2019

Recently, heart rate signal, which is one of biological signals, have been used in various fields related to healthcare. Conventionally, most of the proposed heart rate signal detection methods are contact type methods, but there is a problem of discomfort that the subject have to contact with the device. In order to solve the problem, detection study by non-contact method has been progressed recently. The detected heart rate signal can be used for finger vein liveness detection and various application using heart rate. In this paper, we propose a method to obtain heart rate signal by using finger vein imaging system. The proposed method detected the signal from the changes of the brightness value in the time domain of the infrared finger vein images and converted it into the frequency domain using the image processing algorithm. After the conversion, we removed the noise not related to the heart rate signal through band-pass filtering. In order to evaluate the accuracy of the signal, we analyzed the correlation with the signal obtained simultaneously with the finger vein acquisition device and contact type PPG sensor approved by KFDA. As a result, it was possible to confirm that the heart rate signal detected in non-contact method through the finger vein image coincides with the waveform of actual heart rate signal.