• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.251-254
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• 2018

Research about IoT system that utilizes variation of wireless received signal according to the changing of the surrounding environment are actively being studied. In this paper, firstly we proved that the received signal strength changes according to the ambient temperature variation. Then, we proposed the fire detection scheme by using received signal strength variation when the signal exchange between fixed transmitter and receiver periodically. The proposed scheme consists of the received signal strength change detection unit and the internal receiver temperature detection unit which prevents misunderstanding the received signal strength variation by the changing of wireless channel environment as outbreak of fire. The proposed scheme has the advantage of being able to support the existing receiver through software upgrade without additional device.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.465-470
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• 2013

Dempster-Shafer Evidence Theory is available for multi-sensor data fusion. Basic Probability Assignment is essential for multi-sensor data fusion using Dempster-Shafer Theory. In this paper, we proposed a novel method of BPA calculation with signal assessment. We took notice of the signal that reported from the sensor mote at the time slot. We assessed the variation rate of the reported signal from the terminal. The trend of variation implies significant component of the context. We calculated the variation rate of signal for reveal the relation of the variation and the context. We could reach context inference with BPA that calculated with the variation rate of signal.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1210-1216
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• 2014

This paper deals with an application of Teager Energy Operator (TEO) and Energy Separation Algorithm(ESA) to detect and determine various voltage waveform distortions like harmonics, inter-harmonics and frequency variation. Because the TEO and DESA algorithm was initially proposed for speech or communication analysis, its applications are limited to some types of waveform in the power quality analysis area. For example, an undistorted voltage signal is similar with a pure sinusoid. A voltage fluctuation is very similar with an amplitude-modulated signal, from the viewpoint of signal theory. And a continuous frequency variation is similar with a frequency-modulated signal, which is also known as a chirp signal. This paper is written to show that the TEO and DESA algorithm can be used for detecting occurrences of the representative waveform distortions and determining their instantaneous information of amplitude and frequency.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.215-220
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• 1995

In this paper, we study myoelectric signal variation through kyungrak needle stimulation. The signal are detected from two kyunrak route of arm; before and after stimulation of a needle, and before and after needle rotation. The detected signals are analyzed at frequency domain to search a characteristic parameters. At the rotation method, spectrum denity of the signals varies large but spectrum is not detected before and after rotation. We can not see any relation bet ween spectrum variation and rotational direction. As the results, when the same stimulation method is used at two different kyungrak route respectively, it is found that the variation of the myoelectric signal is not same.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.29-36
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• 2009

The analysis of Radar signal, telecommunication, bioengineering, seismic, and acoustic signal is consist of the Non-stationary signal which has non-linear phase variation. Non-stationary signal means that the physical properties of signal depend on time variation and the instantaneous frequency represents physical property of these type of signal. Thus estimation of the instantaneous frequency of non-stationary signal is important subject in signal processing. In this work, the instantaneous frequency analysis method utilizing continuous wavelets transform is represented and compared with Hilbert Transform method.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.2
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• pp.30-39
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• 1999

In this paper, we have proposed a novel method which can compose a reflected signal of the underwater target. The synthesis of the reflected signal in the target, the synthesized signal being similar to the characteristics of the reflected signal in the real target, is used the highlight model at the specific points of the target. We suggest the synthesis method of the reflected signal of the target using the pulsewidth variation and each other doppler effect at the highlight point, and compare the composed signal by the proposed method with that by conventional one. Simulation results show that the composed signal using the proposed method and the reflected signal of the real target is similar to the spectral characteristics.

• International journal of advanced smart convergence
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• v.3 no.1
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• pp.20-24
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• 2014

To compare body signal, was designed the F-B function system on the body movement for the comfortable state. To detect subject of the normal state, was decided on the base of physical signal in the body movement. There are to detect the condition of Vision, Vestibular, Somatosensory and CNS. Vision condition was verified a variation of greater average (Vi-${\Phi}_{AVG-AVG}$) was presented slightly greater at $17.424{\pm}9.65$ unit. Vestibular condition was identified a variation of slightly greater average (Ve-${\Phi}_{AVG-AVG}$) was presented at $9.068{\pm}1.478$ unit. Somatosensory condition was checked a variation of smaller average (So-${\Phi}_{AVG-AVG}$) was presented slightly smaller at $2.79{\pm}0.419$ unit. CNS condition was confirmed a variation of diminutive smaller average (C-${\Phi}_{AVG-AVG}$) was presented slightly larger at $0.557{\pm}0.153$ unit. As the model depends on the F-B function system of body movement, average values of these perturbation were computed F-B function comparison data. These systems will be to infer a data algorithm and a data signal processing system for the evaluation of the stability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.813-821
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• 2013

In this paper, a vital signal sensor based on impedance variation of resonator is presented. Proposed vital signal sensor can detect the vital signal, such as respiration and heart-beat signal. System is composed of resonator, oscillator, surface acoustic wave (SAW) filter, and power detector. The cyclical movement of a dielectric such as a human body, causes the impedance variation of resonator within the near-field range. So oscillator's oscillation frequency variation is effected on resonator's resonant frequency. SAW filter's skirt characteristic of frequency response can be transformed a small amount of frequency deviation to a large variation. Aim to enhance the existing sensor detection range, proposed sensor operates in 870 MHz ISM band, and detect respiration and heart-beat signal at distance of 120 mm.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.7
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• pp.11-19
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• 1998

A signal line potential variation due to the delta-I noise was physically investigated in CMOS integrated circuits. An equivalent circuit for the noise analysis was presented. The signal line was modeled as segmented RC-lumped circuits with the ground noise. Then the equivalent circuit was mathematically analyzed. Therebvy a new signal line potential variation model due to the switching mosie was developed. Th emodel was verified with 0.35.mu.m CMOS deivce model parameters. The model has an excellent agreement with HSPICE simulation. Thus the proposed model can be dirctly employed in the industry to design the high-performance integrted circuit design as well as integrated circuit package design.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.287-293
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• 2004

This paper proposes an efficient motion compensation algorithm for video sequences with brightness variations. In the proposed algorithm, the brightness variation parameters are estimated and local motions are compensated. To detect the frame with large brightness variations, we employ the frame classification based on the cross entropy between histograms of two successive frames, which can reduce the computational redundancy. Simulation results show that the proposed method yields a higher peak signal to noise ratio (PSNR) than that of the conventional methods, with a low computational load, when the video scene contains large brightness changes.