• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.311-321
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• 2003

In an emergency telemedicine system such as the High-quality Multimedia based Real-time Emergency Telemedicine(HMRET) service, it is very important to examine the status of the patient continuously using the multimedia data including the biological signals(ECG, BP, Respiration, $SpO_2)$ of the patient. In order to transmit these data real time through the communication means which have the limited transmission capacity, it is also necessary to compress the biological data besides other multimedia data. For this purpose, we investigate and compare the ECG compression techniques in the time domain and in the wavelet transform domain, and present an effective lossless compression method of the biological signals using PEG Huffman table for an emergency telemedicine system. And, for the HMRET service, we developed the lossless compression and reconstruction program or the biological signals in MSVC++ 6.0 using DPCM method and JPEG Huffman table, and tested in an internet environment.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1159-1165
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• 2011

Telemetry techniques of rats have been used for assessing safety pharmacology of drugs and chemicals. Biological signals including blood pressure and heart rate measured under anesthesia were significantly different from those obtained under normal conditions. The stress of restraint in awake animals can also affect the accuracy of physiological evaluation. This paper details the surgery required to allow key cardiovascular parameters to be determined. The telemetric measurement of cardiovascular parameters such as blood pressure, heart rate, electrocardiograph(ECG) established. We carried out the continuous monitoring of cardiovascular parameters using the telemetry system in F344 rats. During the measurement, no significant changes were observed in the heart rate and blood pressure. ECG signals and body temperature were also constant during the measurement of biological signals. With the results of this study, we conclude that this telemetry system can be applied usefully for the assesment of biological parameters in the rats.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.845-848
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• 2003

There are many researches on using human neuro-biological signals for various problems such as controlling a mechanical object and/or interfacing human with the computer. It is one of very interesting topics that human can use various instruments without learning specific knowledge if the instruments can be controlled as human intends. In this paper, we proposed an intelligent motion planner for a redundant manipulator, which is controlled by humans neuro-biological signals, especially, EOG (Electrooculogram). We found the optimal motion planner for the redundant manipulator that can move to the desired point. We used neural networks to find the inverse kinematics solution of the manipulator. We also showed the performance of the proposed motion planner with several simulations.

• Journal of Biomedical Engineering Research
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• v.4 no.1
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• pp.29-36
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• 1983

In this paper, center frequency down slift of ultrasonic echo signals which for the measurements of frequency dependent attenuation in the biological tissue are estimated. Center frequency down shift of echo-signals are estimated after signal spectrum analysis of whole echo-signals. In case of signal spectrums are simple, estimation of down shift frequency is very simple and in case of complicate spectrum, estimation of down shift frequency is depend on spectral shape. In case of unable to estimate, frequency dependence of medium is nonlinear(n) 1), in which upper shift of spectrums are presented. In case of unable to estimate, spectrum analysis are performed at local position. At consquence, we know that spectral dispersions are caused complicately by biological tissue layer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.472-474
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• 2018

Recent rapid creation of data has resulted in the development of big data technologies. In particular, with the development of wearable devices that measure biological signals, a variety of biological signals are growing exponentially. Thus, storage technologies are required to identify and systematically store characteristics of exponential increase in biological signals. In this paper, we will study the storage design that stores the biometrics by identifying the characteristics of the biometrics and the techniques to collect the biometrics.

• The Journal of Information Technology
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• v.9 no.1
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• pp.33-39
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• 2006

This study is for the contents of development to the hardware system and software driving algorithm to implement the frequency band of about 7KHz los pass digital filter which has the cut-off frequency of 392Hz by interfacing of a microprocessor with its peripheral analog-to-digital converter chip and digital-to-analog converter chip. The simplicity of digital filter design without difficulty and the implementation of programmed digital filter can be realized by providing the interfacing method to implement the law pass digital filter for the biological signals and the realization method of computer algorithm by a microprocessor.

• Journal of Biomedical Engineering Research
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• v.40 no.1
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• pp.1-6
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• 2019

Human mesenchymal stem cells (hMSC) are multipotent stromal cells that have great potential to differentiate into a variety of cell types such as osteocytes, chondrocytes, and myocytes. Although there have been many studies on their clinical availability, little is known about how intracellular signals can be modulated by topographic features of the extracellular matrix (ECM). In this study, we investigated whether and how microwavy-patterned extracellular matrix (ECM) could affect the signaling activity of focal adhesion kinase (FAK), a key cellular adhesion protein. The fluorescence resonance energy transfer (FRET)-based FAK biosensor-transfected cells are incubated on microwavy-patterned surfaces and then platelet derived growth factor (PDGF) are treated to trigger FAK signals, followed by monitoring through live-cell FRET imaging in real time. As a result, we report that PDGF-induced FAK was highly activated in cells cultured on microwavy-patterned surface with L or M type, while inhibited by H type-patterned surface. In further studies, PDGF-induced FAK signals are regulated by functional support of actin filaments, microtubules, myosin-related proteins, suggesting that PDGF-induced FAK signals in hMSC upon microwavy surfaces are dependent on cytoskeleton (CSK)-actomyosin networks. Thus, our findings not only provide new insight on molecular mechanisms on how FAK signals can be regulated by distinct topographical cues of the ECM, but also may offer advantages in potential applications for regenerative medicine and tissue engineering.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.70-73
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• 1996

Many researchers had considered biological systems as linear systems. In many cases of biological systems, the phenomena that show the regular and periodic dynamics are considered the normal state. However, some clinical experiments reported, in some cases, the periodic signals represented the abnormal state. We assume that signals from human body system are generated from deterministic, intrinsic mechanisms and can be represented a simple equation that show nonlinear dynamics dependent on control parameters. The objective of our study is to model a nonlinear dynamics correctly from the nonlinear time series using the genetic programming method; to find a simple equation of nonlinear dynamics using collected time series and its nonlinear characteristics. We applied genetic programming to model RR interval of ECG that shows chaotic phenomena. We used 4 statistic measures and 2 fractal measures to estimate fitness of each chromosome, and could obtain good solutions of which chaotic features are similar.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1637-1642
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• 2015

Next-generation applications using technology IT fused to biological signals from the emotional state to extract a lot of research has been, and the sensitivity of the human sensory functions influences the physiological condition known to be the fact that. In this paper, Propose an Emotional-lighting control algorithm using bio-signals. LED lighting for Emotion light is environmentally friendly and has a high efficiency and long life. In particular, LED lights are different colors represent the possible single light sphere advantages. And, Human sensitivity for determining a more accurate biological signals using EEG was collected using EEG equipment sensitivity was determined to analyze the EEG.