• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.183-186
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• 1993

We have developed a bioimpedance measurement system for impedance cardiography and pneumography. The system injects 50kHz, $200mA_{p-p}$ curreng into the thorax and measures the voltage changes using body surface electrodes. We used the four-electrode method for tile measurement of cardiac singnals and two-electrode method for respiratory signals. We developed a Microsoft Windows program for the acquisition, display, storage, and processing of impedance signals.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.268-272
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• 1996

The removal of impulsive noise terms which occur in interventricular pressure (IVP) signals of a Korean-type total artificial heart is essential for estimation of atrial pressure change. We compared various order statistic filters and conclude that median filter with sidelength L = 1 is the most appropriate filter for IVP signals in the perspectives of operation cost, detail preserving (peak value), and waveform.

• Journal of Biomedical Engineering Research
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• v.14 no.2
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• pp.137-145
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• 1993

Time domain analysis as well as frequency domain analysis of signal conditioning filters is very useful for practical applications. Time domain analysis of digital filters for noise cancelling in ECG signals is presented. Several band pass and band reject filters are designed for the analysis. Computer simulations are performed to compare the distortions of the Butterworth type filters and linear phase optimal FIR filters which are widely used for ECG signal processing. Band reject filters are applied to power line interference cancelling in ECG signals.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.21-40
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• 2019

This paper presents a comprehensive survey of various artificial intelligence (AI) techniques implemented in cognitive radio engine to improve cognition capability in cognitive radio networks (CRNs). AI enables systems to solve problems by emulating human biological processes such as learning, reasoning, decision making, self-adaptation, self-organization, and self-stability. The use of AI techniques is studied in applications related to the major tasks of cognitive radio including spectrum sensing, spectrum sharing, spectrum mobility, and decision making regarding dynamic spectrum access, resource allocation, parameter adaptation, and optimization problem. The aim is to provide a single source as a survey paper to help researchers better understand the various implementations of AI approaches to different cognitive radio designs, as well as to refer interested readers to the recent AI research works done in CRNs.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.60-62
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• 1994

In this paper, we present a signal analysis workstation in which the user can scrutinize and quantify biological signals, observe the effects of various signal processing algorithms on them, and eventually get some interpretation of clinical use. Within the system, the user can also access all the information in the central data base, such as patient personal information, biological signal information, and insert his interpretation results obtained into the data base after his careful observation. The software system is designed in an object-oriented paradigm, and written in C++ as a window-based application program.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2727-2729
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• 2002

In an emergency telemedicine system such as 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 the HMRET service, we developed the lossless biological signal compression program in MSVC++ 6.0 using DPCM method and JPEG Huffman table, and tested in an internet environment.

• Sleep Medicine and Psychophysiology
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• v.5 no.1
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• pp.34-44
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• 1998

Living organisms are influenced by many external rhythms and they have adapted their physiology to periodically changing conditions. These adaptive strategies are controlled by endogenous innate programs of behavior and physiology which are determined by external signals ("Zeitgeber"). There are many biological rhythms, each with its own characteristic functional adaptation. Among them, the presence of endogenous time control of feeding and drinking becomes obvious. There are increasing evidences that the control of food intake, food selection, and drinking are regulated by the endogenous rhythms including a circadian rhythm. However, there have been many restrictions in understanding the endogenous control of food intake itself and its mechanism. To broaden our know ledges of the endogenous time control of feeding and drinking, the author reviwed the characteristics of the endogenous timing for food intake, the influence of circadian pacemakers and food-entrainable oscillators, the interaction between the circadian control and the external and internal conditions in the control of food intake, the conseqences of feeding, the circadian control of food selection, and the biological cycles in energy balance.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.49-59
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• 2019

Biosensors, which are analysis devices used to convert biological reactions into electric signals, are made up of a receptor component and a signal transduction part. Graphene quantum dots (GQDs) and carbon quantum dots (CQDs) are new types of carbon nanoparticles that have drawn a significant amount of attention in nanoparticle research. The unique features exhibited by GQDs and CQDs are their excellent fluorescence, biocompatibility, and low cytotoxicity. As a result of these features, carbon nanomaterials have been extensively studied in bioengineering, including biosensing and bioimaging. It is extremely important to find biomaterials that participate in biological processes. Biomaterials have been studied in the development of fluorescence-based detection methods. This review provides an overview of recent advances and new trends in the area of biosensors based on GQDs and CQDs as biosensor platforms for the detection of biomaterials using fluorescence. The sensing methods are classified based on the types of biomaterials, including nucleic acids, vitamins, amino acids, and glucose.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1935-1941
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• 2021

In order to develop an algorithm using deep learning, which has been recently applied to various fields, it is necessary to have rich, high-quality learning data. In this paper, we propose an acquisition system for biological signals that simultaneously collects bio-signal data such as optical videos, thermal videos, and voices, which are mainly used in developing deep learning algorithms and useful in derivation of information, and transmit them to the server. To increase the portability of the collector, it was made based on Raspberry Pi, and the collected data is transmitted to the server through the wireless Internet. To enable simultaneous data collection from multiple collectors, an ID for login was assigned to each subject, and this was reflected in the database to facilitate data management. By presenting an example of biological data collection for fatigue measurement, we prove the application of the proposed acquisition system.

• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.269-274
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• 2010

The purpose of this study was to develop the FSR sensor suit that controls walking assist device for paraplegic patients. The FSR sensor suit was to detect user's intent and patterns for walking by measuring pressure on the palm and the sole of user's foot. It consisted of four modules: sensing pressure from palm, changing modes and detecting pressure on the palm/at the wrist, sensing pressure from the soles of user's foot, and host module that transmit FSR data obtained from sensing modules to PC. Sensing modules were connected to sensing pads which detect analog signals obtained from the palm or the sole of foot. These collect signals from the target regions, convert analog signals into digital signals, and transmit the final signals to host module via zigbee modules. Finally, host modules transmit the signals to host PC via zigbee modules. The study findings showed that forces measured at the palm when using a stick reflected user's intent to walk and forces at the sole of the user's foot revealed signals detecting walking state.