• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2051-2052
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• 2011

The structural defects of a heart often reflects the sounds that the heart produces. This paper describes heart sound analysis method using Wavelet transform and average Shannon energy. This can extract the features of heart sounds in various disease identify the heart sounds. Experimental results show that the presented method has potential application in detecting various heart diseases.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.75-81
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• 2017

Polymer collapse transition에 대한 연구가 많이 진행되어왔다. 허나 각각의 microstate에 대한 entropy나 free energy에 대한 계산을 하지는 못하였다. 최근 local nonequilibrium thermodynamics와 관련한 논문이 발표되었는데 이는 비평형 상태에서의 각각의 microstate에 대한 확률 분포를 결정하는 물리량을 발견 및 특성을 규명하여 이 중 특별한 상태가 지니는 "information" 이라는 양이 내부에너지와 엔트로피와의 상관관계가 있음을 보였다. 또한, 이러한 information theory를 이용한 Shannon entropy를 사용하여 entropy를 정의하고 free energy와 같은 물리량을 계산하였다. 따라서 이를 이용하여 information theory를 이용한 Shannon entropy와 이로 정의된 free energy를 이용하여 polymer collapse중 entropy 및 free energy를 계산하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.51-57
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• 2021

In this paper, the relevance between Einstein's special theory of relativity (1905) and Bernoulli's fluid mechanics (1738), which motivates Shannon's theorem (1948), was derived from the AB=A/A=I dimension, and the Shannon's theorem channel code was simulated. When Bernoulli's fluid mechanics ΔP=pgh was applied to the Hallasan volcano Magma eruption, the dimensions and heights matched the measured values. The relationship between Einstein's special theory of relativity, Shannon's information theory, and the stack effect theory of fluid mechanics was analyzed, and the relationship between volcanic eruptions was mathematically proven. Einstein's and Bernoulli's conservation of energy and conservation of mass were the same in terms of bandwidth and power efficiency in Shannon's theorem.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.176-181
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• 2002

The continuous Gabor wavelet transform (GWT) has been utilized as a useful time-frequency analysis tool to identify the rapidly-varying characteristics of some wave signals. In the application of GWT, it is important to select the Gabor wavelet with the optimal shape factor by which the time-frequency distribution of a signal can be accurately estimated. To find the signal-dependent optimal Gabor wavelet shape factor, the notion of the Shannon entropy which mesures the extent of signal energy concentration in the time-frequency plane is employed. To verify the validity of the present entropy-based scheme, we have applied it to the time-frequency analysis of a set of elastic bending wave signals generated by an impact in a solid cylinder.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.324.1-324
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• 2002

The continuous Gabor wavelet transform (GWT) has been utilized as a useful time-frequency analysis tool to identify the rapidly-varying characteristics of some wave signals. In the application of GWT, it is important to select the Gabor wavelet with the optimal shape factor by which the time-frequency distribution of a signal can be accurately estimated. To find the signal-dependent optimal Gator wavelet shape factor, the notion of the Shannon entropy which measures the extent of signal energy concentration in the time-frequency plane is employed. (omitted)

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.884-894
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• 2011

We proposed a new algorithm for detection of first(S1) and second heart sound(S2). Many researches for detecting primary components and those algorithms have good performance at normal heart sound, but the performance is degraded at abnormal heart sound which is contain murmurs generated by heart disease. Therefore we proposed the S1, S2 detection algorithm using three-order Shannon energy difference. Using S1, S2's character which has large energy difference than murmurs, it is reduced noise and detected S1, S2. According to simulation results, not only normal heart sound but also abnormal heart sound, the proposed algorithm has better performance than former study at abnormal heart sound.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2E
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• pp.72-78
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• 2009

Heart sound segmentation into its components, S1, systole, S2 and diastole is the first step of analysis and the most important part in the automatic diagnosis of heart sounds. Conventionally, the Shannon energy envelope peak detection method has been popularly used due to its superior performance in locating S1 and S2. Recently, the HMM has been shown to be quite suitable in modeling the heart sound signal and its use in segmenting the heart sound signal has been suggested with some success. In this paper, we compared the two methods for heart sound segmentation using a common database. Experimental tests carried out on the 4 different types of heart sound signals showed that the segmentation accuracy relative to the manual segmentation was 97.4% in the HMM based method which was larger than 91.5% in the peak detection method.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1500-1507
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• 2021

Identification and analysis of the first and second heart sounds(S1, S2) is the easiest way for cardiovascular disease prevention and early diagnosis. However, accurate identification is difficult because the heart sound includes organ movement, blood vortex, user experience, and noise influenced by subjective judgment. Therefore, an algorithm to automatically identify the S1 and S2 heart sounds based on blood vessel transit time(VTT) is presented in this paper. According to the experimental results of comparing the algorithm developed for S1 and S2 heart sound analysis with the conventional Shannon energy algorithm in 10 volunteers, it has been proven that the proposed algorithm can automatically identify S1 and S2 heart sounds with higher accuracy than existing algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.83-93
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• 2013

By comparing Wiener's MMSE on stochastic signal transmission with Shannon's mutual information first proved by C.E. Shannon in terms of information theory, connections between two approaches were investigated. What Wiener wanted to see in signal transmission in noisy channel is to try to capture fundamental limits for signal quality in signal estimation. On the other hands, Shannon was interested in finding fundamental limits of signal quantity that maximize the uncertainty in mutual information using the entropy concept in noisy channel. First concern of this paper is to show that in deriving limits of Shannon's point to point fundamental channel capacity, Shannon's mutual information obtained by exploiting MMSE combiner and Wiener filter's MMSE are interelated by integro-differential equantion. Then, At the meeting point of Wiener's MMSE and Shannon's mutual information the upper bound of spectral efficiency and the lower bound of energy efficiency were computed. Choosing a proper lattice-type code of a mod-${\Lambda}$AWGN channel model and MMSE estimation of ${\alpha}$ confirmed to lead to the fundamental Shannon capacity limits.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1078-1085
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• 2011

In this paper, we analyze the relationship between energy per bit and the data rate with the variation of the system bandwidth. A existing power model is mathematical model to express power consumption of each device. In this paper, we have to investigate the system level energy model for the RF front-end of a wireless transceiver. Also, the effects of the signal bandwidth, PAR, date rate, modulation level, transmission distance, specific attenuation of frequency band, and the signal center frequency on the RF front-end energy consumption and system capacity are considered. Eventually, we analyze the relationship between energy per bit and the data rate with the variation of the system bandwidth so that we simulate the minimum energy per bit in the several Gbps data rate using Shannon capacity theory.