• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.91-98
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• 2022

In the next generation wireless communication system, the beamforming technique based on a massive antenna is one of core technologies for transmitting and receiving huge amounts of data, efficiently and accurately. For highly performed and highly reliable beamforming, it is required to accurately estimate the Angle of Arrival (AOA) for the desired signal incident to an antenna. Employing the massive antenna with a large number of elements, although the accuracy of the AOA estimation is enhanced, its computational complexity is dramatically increased so much that real-time communication is difficult. In order to improve this problem, AOA estimation algorithms based on the massive antenna with the low computational complexity have been actively studied. In this paper, we compute and analyze the computational complexity of the cascade AOA estimation algorithm based on the Flexible Massive Concentric Circular Array (FMCCA). In addition, its computational complexity is compared to conventional AOA estimation techniques such as the Multiple Signal Classification (MUSIC) algorithm with the high resolution and the Only Beamspace MUSIC (OBM) algorithm.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.293-301
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• 2022

Direction of arrival (DOA) estimation of space signals is a basic problem in array signal processing. DOA estimation based on the multiple signal classification (MUSIC) algorithm can theoretically overcome the Rayleigh limit and achieve super resolution. However, owing to its inadequate real-time performance and accuracy in practical engineering applications, its applications are limited. To address this problem, in this study, a DOA estimation algorithm with high parallelism and precision based on an analysis of the characteristics of complex matrix eigenvalue decomposition and the coordinate rotation digital computer (CORDIC) algorithm is proposed. For parallel and single precision, floating-point numbers are used to construct an orthogonal identity matrix. Thus, the efficiency and accuracy of the algorithm are guaranteed. Furthermore, the accuracy and computation of the fixed-point algorithm, double-precision floating-point algorithm, and proposed algorithm are compared. Without increasing complexity, the proposed algorithm can achieve remarkably higher accuracy and efficiency than the fixed-point algorithm and double-precision floating-point calculations, respectively.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.277-287
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• 2024

In satellite systems, efficient communication and observation require identifying of specific signal arrival points using onboard antenna systems. When utilizing massive array antennas to estimate the angle of arrival (AOA) of signals, traditional high-performance AOA estimation algorithms such as Multiple Signal Classification (MUSIC) encounter extremely high complexity due to the numerous individual antenna elements. Although, in order to improve this computational complexity problem, the cascade AOA estimation algorithm with CAPON and beamspace-MUSIC was recently proposed, the comparison of the computational complexity of the proposed algorithm across different massive array antenna configurations has not yet been conducted. In this paper, we provide the analyzed results of the computational complexity of the proposed cascade algorithm based on various massive array antennas, and determine an optimal antenna configuration for the efficient AOA estimation in satellite systems.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.97-104
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• 2007

This paper presents the problem of the feature extraction techniques that has been used a content-based analysis, classification and retrieval in audio data and proposes a course of the preprocessing for a new contents-based retrieval methods. Because the feature vector according to sampling value changes, the existing audio data analysis is problem that same music is appraised by other music. Therefore, we propose waveform information extraction method of PCM data for retrieval audio data of various format to contents-based. If this method is used. we can find that audio datas that get into sampling in various format are same data. And it may be applied in contents-based music retrieval system. To verity the performance of the method, an experiment was done feature extraction using STFT and waveform information extraction using PCM data. As a result, we could know that the method to propose is effective more.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.166-171
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• 2022

In this paper, we propose an algorithm to improve DoA(direction of arrival) estimation performance of the subspace-based method by generating high robustness correlation matrix of the signals incident on the uniformly linear array antenna. The existing subspace-based DoA estimation method estimates the DoA by obtaining a correlation matrix and dividing it into a signal subspace and a noise subspace. However, the component of the correlation matrix obtained from the low SNR and small number of snapshots inaccurately estimates the signal subspace due to the noise component of the antenna, thereby degrading the DoA estimation performance. Therefore a robust correlation matrix is generated by arranging virtual signal vectors obtained from the existing correlation matrix in a sliding manner. As a result of simulation using MUSIC and ESPRIT, which are representative subspace-based methods,, the computational complexity increased by less than 2.5% compared to the existing correlation matrix, but both MUSIC and ESPRIT based on RMSE 1° showed superior DoA estimation performance with an SNR of 3dB or more.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.748-756
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• 2022

Thanks to the competition of AlphaGo and Sedol Lee, machine learning has received world-wide attention and huge investments. The performance improvement of computing devices greatly contributed to big data processing and the development of neural networks. Artificial intelligence not only imitates human beings in many fields, but also seems to be better than human capabilities. Although humans' creation is still considered to be better and higher, several artificial intelligences continue to challenge human creativity. The quality of some creative outcomes by AI is as good as the real ones produced by human beings. Sometimes they are not distinguishable, because the neural network has the competence to learn the common features contained in big data and copy them. In order to confirm whether artificial intelligence can express the inherent characteristics of different arts, this paper proposes a new neural network model called Humming. It is an experimental model that combines vgg16, which extracts image features, and DeepJ's architecture, which excels in creating various genres of music. A dataset produced by our experiment shows meaningful and valid results. Different results, however, are produced when the amount of data is increased. The neural network produced a similar pattern of music even though it was a different classification of images, which was not what we were aiming for. However, these new attempts may have explicit significance as a starting point for feature transfer that will be further studied.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.439-448
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• 2001

This paper considers the acoustical characteristics of the closing click sounds of the mechanical valves employed in an implantable biventricular assist device (BYAD) and their re1evance to the Physical states of the valved. Bj rk Shiley Convexo Concave tilting disk valve was chosen for the study and acoustic measurement was made for the BYAD operated in a mock circulatory system as well as implanted in an animal (sheep). In the BYAD operated in the mock circulatory system. three different states of the valve were examined, ie. normal. mechanically damaged. pseudo-thrombus attached. Microphone measurement for the BVAD implanted in the animal was carried out for five days at a regular time interval from one day after implantation. Characteristic spectrum of the sound from the valve was estimated using Multiple Signal Classification (MUSIC) in which the optimal order was determined according to Bayesian Information Criterion (BIC) . It was observed that the mechanical damage of the valve resulted in changes of the structure of the acoustic spectrum. In contrast. the thrombus formed on the valve did not change much the basic structure of the spectrum but brought about altering the spectral Peak frequencies and energies. Maximum spectral Peak (MSP) with the greatest energy was seen at 2 kHz for the normal valve and it was shifted to 3 kHz for the calve attaching the Pseudo-thrombus. Unlike the normal valve, strong spectral Peak appeared around 7 kHz in the sound from the valve mechanically damaged. In the case of the BYAD implanted in the animal. as the thrombus grew, acoustic energy was reduced relatively more in the low frequency components (〈 2 kHz) and the frequencies of the 1st, 2nd and 3rd MSP were increased little. The thrombus formation would result in reduction in both the variability of the 1st, 2nd and 3rd MSP and the value of the BIC optimal order.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.419-421
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• 2006

The importance of automatic discrimination image data has evolved as a research topic over recent years. We have used forward neural network as a classifier using sound data features within image data, our initial tests have shown encouraging results that indicate the viability of our approach.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.23-24
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• 2016

본 논문에서는 온라인 오디오 장르 분류의 성능을 비교 분석한다. 온라인 동작을 위해 1초 단위의 오디오 신호를 입력하여 music, speech, effect 중 하나의 장르로 판단한다. 학습 방법은 GMM과 심층 신경망을 사용하며, 특성은 MFCC와 스펙트로그램을 포함하는 네 가지 종류의 벡터를 사용한다. 각 성능을 비교 분석하여 장르 분류에 적합한 학습 방법과 특성 벡터를 확인한다.