• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.275-281
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• 1999

Conventional PW Doppler systems suffer from the ambiguity of measured blood velocities due to the spectrum aliasing when the corresponding Doppler frequencies are greater than the Nyquist frequency. Base-line shift is a customary method for dealiasing the Doppler spectrums. I lowever, Doppler audio signals still remain unchanged even when the base-line shift method is applied. This paper de scribes an method for dealiasing both the Doppler spectra and audio signals by using sampling rate expansion, frequency shifting, and filtering poerations. For undirectional flows, the method can increase the maximum detectable Doppler frequency from the Nyquist limit of one-half of the Pulse Repetition Frequency(PRF) to the PRF. Experiments with real data have been performed to verify the proposed method.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.05
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• pp.134-135
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• 1993

The purpose of this research is development of EGG system for quantitative assessment of laryngeal function using speech and electroglotto-graphic data. The designed EGG system is 4-electrodes system which excitation current source is supplied from 1st to 4th electrode. The output signal.: from 2nd and 3rd electrodes, which are motivated by frequency of excitation current source, are air-pressure waveforms from vocal folds. After demodulation process, we obtain pitch signals of the modulated waveforms by excitation current source through differentiator which cuts off frequency below 0.1Hz. Software processing methods were used as conventional pitch extraction methods, but the proposed system is designed to analog hardware in order to eliminate interferences from low formant frequency of speech. We will construct the discriminating database between pathological subjects and control groups on each case. Using the proposed 3 channel EGG system and LMS algorithm, it will be detected that the distinctive characteristics of laryngeal function of voiced region and other regions by EGG signals and LPC spectra.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.160-163
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• 1995

An optimal EEG and artifact classifier is proposed using neural network operating characteristics. The neural network operating characteristics are two dimensional parametric representations of the right and false identification probabilities of the network classifier. Since the EEG and EP signals acquired from multi -channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG), the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. Using the neural-network based classification, human expert's efforts and time can be substantially reduced. From experiments, the neural-network based classification performs as good as human experts: variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.179-183
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• 1996

Most of the conventional electrocardiogaphies fail to detect signals other than P-QRS-T due to the limited SNR and bandwidth. High-resolution electrocardiography (HRECG) provides better SNR and wider bandwidth for the detection of micro-potentials with higher frequency components such as ventricual late potentials(LP). In this paper, we developed a HRECG using uncorrected XYZ lead. The overall gain of the amplifier is 4000 and the bandwidth is $0.5{\sim}300Hz$ without using 60Hz notch filter. Three 16-bit AH converters sample X, Y, and Z signals simultaneously with a sampling frequency of 2000Hz. Sampled data are transmitted to PC via a DMA-controlled serial communication channel using RS-485 and HDLC protocol. The noise level of the developed HRECG is less than $5{\mu}V_{rms,\;RTI}$. In order to further reduce the noise level, signal averaging technique is implemented utilizing template matching method. The SNR of the developed HRECG is high enough for the detection of LP.

• Korean Journal of Medicinal Crop Science
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• v.18 no.3
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• pp.186-190
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• 2010

The chromosome number was identified and fluorescence in situ hybridization(FISH) mapping of 5S and 45S rDNAs were conducted for C. minima and C. lanceolata in the genus Codonopsis from Jeju island. In this study, we have confirmed that the somatic metaphase chromosome number determined as 2n=2x=16 was the same as the findings from the previous studies. While the conventional staining method makes it rather difficult to distinguish satellite chromosomes due to high degree of variability, FISH analysis produced the exact number and location of 5S and 45S rDNAs. Both species in the genus Codonopsis have a pair of 5S rDNA and their gene loci were observed on chromosome 3. Although two pairs of 45S rDNAs (one on chromosome 1 and the other on chromosome 8) were identified in both species, the 45S rDNA signals on chromosome 8 in C. minima were significantly weaker than those on chromosome 1. In addition, the 45S rDNA signals on chromosome 1 in C. lanceolata showed that the chromosome is non-homologus. In this study, we have determined cytogenetic characteristics of C. minima and C. lanceolata according to their gene replication patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2732-2753
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• 2019

With the application of wireless sensor networks in the fields of ecological observation, defense military, architecture and urban management etc., the security problem is becoming more and more serious. Characteristics and constraint conditions of wireless sensor networks such as computing power, storage space and battery have brought huge challenges to protection research. Inspired by the danger theory in biological immune system, this paper proposes an intrusion detection model for wireless sensor networks. The model abstracts expressions of antigens and antibodies in wireless sensor networks, defines meanings and functions of danger signals and danger areas, and expounds the process of intrusion detection based on the danger theory. The model realizes the distributed deployment, and there is no need to arrange an instance at each sensor node. In addition, sensor nodes trigger danger signals according to their own environmental information, and do not need to communicate with other nodes, which saves resources. When danger is perceived, the model acquires the global knowledge through node cooperation, and can perform more accurate real-time intrusion detection. In this paper, the performance of the model is analyzed including complexity and efficiency, and experimental results show that the model has good detection performance and reduces energy consumption.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.7-12
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• 2020

Various parameters are used to measure anesthetic depth during surgery using brain waves, and in actual clinical use, the linear analysis SEF is widely used. However, with recent studies showing that biological signals including EEG, contain nonlinear properties interest in nonlinear analysis of brain signals is increasing and parameters based on these are being developed. In this study, we are going to develop a parameter that can measure EEG using the nonlinear analysis method and extract noise that can be mixed with external electronic equipment and EEG instrumentation by comparing it with the data from the bispectrum analysis of static waves.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.417-423
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• 2017

This paper intend to implement monitoring systems for individual customized diagnostics to maintain ongoing disease management to promote human health. Analyze the threshold of a measured biological signal using a number of measuring sensors. Performance assessment revealed that the SVM algorithm for bio-signal analysis showed an average error rate of 2 %. The accuracy of the classification is 97.2%, and reduced the maximum of 19.2% of the storage space when you split the window into 5,000 pieces. Out of the total 5,000 bio-signals, 84 results showed that results from the system were differently the results of the expert's diagnosis and showed about 98 % accuracy. However, the results of the monitoring system did not occur when the results of the monitoring system were lower than that of experts. And About 98% accuracy was shown.

• Journal of Biomedical Engineering Research
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• v.32 no.1
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• pp.45-54
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• 2011

Cuff electrodes have a benefit for chronic electroneurogram(ENG) recording while minimizing nerve damage. However, the ENG signals are usually contaminated by electromyogram(EMG) activity from the surrounding muscle, the thermal noise generated within the source resistance, and the electric noise generated primarily at the first stage of the amplifier. This paper proposes a new cuff electrode to reduce the interference of EMG signals. An additional middle electrode was placed at the center of cuff electrode. As a result, the proposed cuff electrode achieved a higher signal-to-interference ratio compared to the conventional tripolar cuff. The cuff electrode was then assembled together with closure, headstage, and hermetic case including electronic circuits. This paper also presents a lownoise amplifier system to improve signal-to-noise ratio. The circuit was designed based on the noise analysis to minimize the electronic noise. The result shows that the total noise of the amplifier was below $1{\mu}V_{rms}$ for a cuff impedance of $1\;k{\Omega}$ and the common-mode rejection ratio was 115 dB at 1 kHz. In the current study, the performance of nerve cuff electrode system was evaluated by monitoring afferent nerve signals under mechanical stimuli in a rat animal model.