• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5A
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• pp.333-340
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• 2003

In this paper, we propose a new turbo-coded orthogonal frequency division multiplexing (OFDM) transmission scheme that can improve greatly the performance by making all the turbo-coded symbols have the same reliability for OFDM transmission over a frequency selective fading channel. The same reliability, that is, the same fading can be accomplished through multiplexing of turbo-coded symbols using distinct orthogonal codes and spreading over the whole effective subcarriers (hereafter, called as the orthogonal code multiplexing (OCM)). As for the orthogonal code selection, we choose the set of the discrete Fourier transform (DFT) basis sequences, since the code set holds the orthogonality irrespective of the length and also has the equal energy property. We perform computer simulations using the Log-maximum-a-posteriori (Log-MAP) algorithm for iterative decoding in order to assess the performance of the proposed transmission scheme.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.455-464
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• 2009

Lee(2009) proposed the boundary-adaptive despeckling method using a Bayesian model which is based on the lognormal distribution for image intensity and a Markov random field(MRF) for image texture. This method employs the Point-Jacobian iteration to obtain a maximum a posteriori(MAP) estimate of despeckled imagery. The boundary-adaptive algorithm is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The boundary-adaptive scheme was comprehensively evaluated using simulation data and the effectiveness of boundary adaption was proved in Lee(2009). This study, as an extension of Lee(2009), has suggested a modified iteration algorithm of MAP estimation to enhance computational efficiency and to combine classification. The experiment of simulation data shows that the boundary-adaption results in yielding clear boundary as well as reducing error in classification. The boundary-adaptive scheme has also been applied to high resolution Terra-SAR data acquired from the west coast of Youngjong-do, and the results imply that it can improve analytical accuracy in SAR application.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.315-323
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• 2011

This paper proposes two methods of estimating prior distribution to improve the performance of rapid speaker adaptation based on maximum a posteriori linear regression (MAPLR). In general, prior distribution of the transformation matrix used in MAPLR adaptation is estimated from all of the training speakers who are employed to construct the speaker-independent model, and it is applied identically to all new speakers. In this paper, we propose a method in which prior distribution is estimated from a group of reference speakers, selected using adaptation data, so that the acoustic characteristics of the selected reference speakers may be similar to that of the new speaker. Additionally, in MAPLR adaptation with block-diagonal transformation matrix, we propose a method in which the mean matrix and covariance matrix of prior distribution are estimated from two groups of transformation matrices obtained from the same training speakers, respectively. To evaluate the performance of the proposed methods, we examine word accuracy according to the number of adaptation words in the isolated word recognition task. Experimental results show that, for very limited adaptation data, statistically significant performance improvement is obtained in comparison with the conventional MAPLR adaptation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.80-85
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• 2011

Alzheimer's disease and Parkinson's disease are the most common neurodegenerative disorders. In this paper, we proposed a feature extraction method for classification of AD and PD based on micro-Raman spectra from platelet. The first step of the preprocessing is a simple smoothing followed by background elimination to the original spectra to make it easy to measure the intensity of the peaks. The last step of the preprocessing was peak alignment with the reference peak. After the inspection of the preprocessed spectra, we found that proportion of two peak intensity at 743 and $757cm^{-1}$ and peak intensity at 1248 and $1448cm^{-1}$ are the most discriminative features. Then we apply mapstd method for normalization. The method returned data with means to 0 and deviation to 1. With these three features, the classification result involving 263 spectra showed about 95.8% true classification in case of MAP(maximum a posteriori probability).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.385-391
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• 2007

In this paper, we propose and observe the Adaptive Modulation system with optimal Turbo Coded V-BLAST (Vertical-Bell-lab Layered Space-Time) technique that is applied the extrinsic information from MAP (Maximum A Posteriori) Decoder with Iterative Decoding to use as a priori probability in two decoding procedures of V-BLAST: ordering and slicing. Also, comparing with the Adaptive Modulation system using conventional Turbo Coded V-BLAST technique that is simply combined V-BLAST with Turbo Coding scheme, we observe how much throughput performance has been improved. As a result of simulation, in the Adaptive Modulation systems with several Turbo Coded V-BLAST techniques, the optimal Turbo Coded V-BLAST technique has higher throughput gain than the conventional Turbo Coded V-BLAST technique. Especially, the results show that the proposed scheme achieves the gain of 1.5 dB SNR compared to the conventional system at 2.5 Mbps throughput.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.588-592
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• 2009

In this paper, we propose a novel method to improve the performance of the global soft decision which is based on the second-order conditional maximum a posteriori (CMAP). Conventional global soft decision scheme has an disadvantage in that the speech absence probability adjusted by a fixed-parameter was sensitive to the various noise environments. In proposed approach using the second-order CMAP, speech absence probability value is more flexible which exploit not only the current observation but also the speech activity decisions in the previous two frames. Experimental results show that the proposed improved global soft decision method based on second-order conditional MAP yields better results compared to the conventional global soft decision technique with the performance criteria of the ITU-T P. 862 perceptual evaluation of speech quality (PESQ).

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.3-10
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• 1999

This paper describes the development and evaluation of a Korean address input system employing automatic speech recognition technique as user interface for input Korean address. Address consists of cities, provinces and counties. The system works on a window 95 environment of personal computer with built-in soundcard. In the speech recognition part, the Continuous density Hidden Markov Model(CHMM) for making phoneme like units(PLUs) and One Pass Dynamic Programming(OPDP) algorithm is used for recognition. For address recognition, Finite State Automata(FSA) suitable for Korean address structure is constructed. To achieve an acceptable performance against the variation of speakers, microphones, and environmental noises, Maximum a posteriori(MAP) estimation is implemented in adaptation. And to improve the recognition speed, fast search method using variable pruning threshold is newly proposed. In the evaluation tests conducted for the 100 connected words uttered by 3 males the system showed above average 96.0% of recognition accuracy for connected words after adaption and recognition speed within 2 seconds, showing the effectiveness of the system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.117-122
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• 2007

For multiple antenna systems, we consider the hybrid iterative detection of the maximum a posteriori probability(MAP) detection and the linear detection such as the minimum-mean-square-error(MMSE) filtering with soft cancelation. We devise methods to obtain both the lower complexity of the linear detection and the superior performance of the MAP detection. Using the a prior probability of the coded bit which is extrinsic of the outer decoder, we compute the threshold of grouping and determine the detection scheme symbol by symbol. Through the simulation results, it is shown that the proposed receiver obtains the superior performance to the MMSE detector and the lower complexity than the MAP detector.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.257-265
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• 2000

Turbo Code decoder is an iterate decoding technology, which extracts extrinsic information from the bit to be decoded by calculating both forward and backward metrics in each decoding step, and uses the information to the next decoding step. Viterbi decoder, which is for a convolutional code, runs continuous mode, while Turbo Code decoder runs by block unit. There are algorithms used in a decoder : which are MAP(maximum a posteriori) algorithm requiring very complicated calculation and SOVA(soft output Viterbi algorithm) using Viterbi algorithm suggested by Hagenauer, and it is known that the decoding performance of MAP is better. The result of this make experimentation shows that the performance of SOVA, which has half complex algorithm compare to MAP, is almost same as the performance of MAP when the SOVA decoding performance is supplemented with Robust equalization techniques.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.105-112
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• 2006

In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half Hanning window and dimension reduction with principal components analysis (PCA). The application of the half Hanning window deemphasizes the peak near $1650cm^{-1}$ and improves classification performance by lowering the false negative ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori probability (MAP), k-nearest neighbor (KNN), probabilistic neural network (PNN), multilayer perceptron(MLP), support vector machine (SVM) and minimum squared error (MSE) classification. Classification results with KNN involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.