• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1678-1685
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• 2015

We propose a linear precoding scheme combining the merits of beamforming and orthogonal space-time block coding based on the imperfect CSI without knowing the CSI reliability. We first investigate the impact of the CSI reliability on precoding performance by assuming various values of the CSI reliability. Then, we propose a method of predicting the CSI reliability based on the received SNR for the design of an efficient precoder. We show the efficiency of the proposed scheme by simulation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.8
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• pp.797-804
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• 2011

Network coding has recently emerged as an effective solution for multicast and broadcast communications in wireless ad hoc networks. In this paper, we propose a throughput performance model for IEEE 802.11 wireless networks with network coding. Specifically, we consider IEEE 802.11 DCF protocol and linear topology in which traffic sources are located at both ends and intermediate nodes act as relays performing network coding. The proposed analytic model has the form of nonlinear equations in terms of throughput of each node. The solution of the nonliear equations thus correspond to the end-to-end throughput. Extensive simulation experiments have been performed to validate accuracy of the proposed model. Numerical results show that the results of the proposed analytic model agree fairly well with the corresponding simulation results.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.26-34
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• 2000

In this paper, we propose a 4kbps speech coder that combines the harmonic vector excitation coding with time-separated transition coding. The harmonic vector excitation coding uses the harmonic excitation coding in the voiced frame and uses the vector excitation coding with the structure of analysis-by-synthesis in the unvoiced frame, respectively. But two mode coding method is not effective for transition frame mixed in voiced and unvoiced signal and a new method beyond using unvoiced/voiced mode coding is needed. Thus, we designed a time-separated transition coding method for transition frame in which a voiced/unvoiced decision algorithm separates unvoiced and voiced duration in a frame, and harmonic-harmonic excitation coding and vector-harmonic excitation coding method is selectively used depending on the previous frame U/V decision. In the decoder, the voiced excitation signals are generated efficiently through the inverse FFT of harmonic magnitudes and the unvoiced excitation signals are made by the inverse vector quantization. The reconstructed speech signal are synthesized by the Overlap/Add method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1957-1964
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• 2010

In Video coding, the first frame is encoded in intra mode which generates a larger number of bits. In addition, the first frame is used for the inter mode encoding of the following frames. Thus the intial QP for the first frame of GOP affects the first frame as well as the following frames. Traditionally, the initial QP of a GOP is determined by the initial QP of the previous GOP and the average QP of the inter mode frames. In case of JM, the initial QP of a GOP is adjusted as the initial QP being less than the average QP of inter mode frames by two. However, this method is not suitable for the low bit rate video coding. In this paper, the linear relationship between the optimal QP and the ratio of the PSNR of the first frame and the average PSNR of the inter mode frames is first investigated and the linear model is proposed based on the results of the investigation. The proposed model calculate the optimal initial QP using the encoding results of the previous GOP. It is shown by experimental results that the new algorithm can predict the optimal initial QP more accurately and generate the PSNR performance better than that of the existing JM algorithm.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.21-30
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• 2014

The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.420-427
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• 2004

This paper presents real time implementation of 4kbps EHSX (Enhanced Harmonic Stochastic Excitation) speech coder that combines the harmonic vector excitation coding with time-separated transition coding. The harmonic vector excitation coding uses the harmonic excitation coding for voiced frames and used the vector excitation coding with the structure of analysis-by-synthesis for unvoiced frames, respectively. For transition frames mixed with voiced and unvoiced signal, we use the time-separated transition coding. In this paper. we present the optimization methods of implementation speech coder on the EMS320C6701/sup (R)/ DSP. To reduce the complex for real-time implementation. we perform the optimization method in algorithm by replacing the complex sinusoidal synthesis method with IFFT. and we apply fully pipelines hand assembly coding after converting it from floating source to fixed source. To generate a more efficient code. we also make use or the available EMS320C6701/sup (R)/ resources such as Fastest67x library and memory organization.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.957-963
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• 2007

In this paper, Nonlinear Autoregressive (NAR) method based on Least Square-Support Vector Regression (LS-SVR) is introduced and tested for nonlinear sustained vowel modeling. In the database of total 43 sustained vowel of Benign Vocal Fold Lesions having aperiodic waveform, this nonlinear synthesizer near perfectly reproduced chaotic sustained vowels, and also conserved the naturalness of sound such as jitter, compared to Linear Predictive Coding does not keep these naturalness. However, the results of some phonation are quite different from the original sounds. These results are assumed that single-band model can not afford to control and decompose the high frequency components. Therefore multi-band model with wavelet filterbank is adopted for substituting single band model. As a results, multi-band model results in improved stability. Finally, nonlinear sustained vowel modeling using NAR based on LS-SVR can successfully reconstruct synthesized sounds nearly similar to original voiced sounds.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.346-353
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• 2013

Industrial processes need to be monitored in real-time based on the input-output data observed during their operation. Abnormalities in an induction motor should be detected early in order to avoid costly breakdowns. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results shows that the proposed approach yields higher classification accuracies than the state-of-the-art approach for both noiseless and noisy environments for identifying the induction motor faults.

• Journal of Bio-Environment Control
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• v.31 no.2
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• pp.98-103
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• 2022

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars ('Arihyang', 'Jukhyang', 'Keumsil', 'Maehyang', and 'Seollhyang'). The coefficient of determination (R²) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R² value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars 'Jukhyang', 'Seolhyang', and 'Maehyang' could be non-destructively predicted using the model developed in this study, as they had R² values over 0.96. The cultivars 'Arihyang' and 'Geumsil' had slightly low R² values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.23-35
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• 2013

This paper proposes an effective technique that is used to automatically extract feature vectors from vibration signals for fault classification systems. Conventional mel-frequency cepstral coefficients (MFCCs) are sensitive to noise of vibration signals, degrading classification accuracy. To solve this problem, this paper proposes spectral envelope cepstral coefficients (SECC) analysis, where a 4-step filter bank based on spectral envelopes of vibration signals is used: (1) a linear predictive coding (LPC) algorithm is used to specify spectral envelopes of all faulty vibration signals, (2) all envelopes are averaged to get general spectral shape, (3) a gradient descent method is used to find extremes of the average envelope and its frequencies, (4) a non-overlapped filter is used to have centers calculated from distances between valley frequencies of the envelope. This 4-step filter bank is then used in cepstral coefficients computation to extract feature vectors. Finally, a multi-layer support vector machine (MLSVM) with various sigma values uses these special parameters to identify faulty types of induction motors. Experimental results indicate that the proposed extraction method outperforms other feature extraction algorithms, yielding more than about 99.65% of classification accuracy.