• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.29-33
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• 1992

We examine the compression performance of two irreversible (lossy) compression techniques, ADCT-VQ (Adaptive Discrete Cosine Trandform - Vector Quantization) and JPEG (Joint Photographic Experts group) which are basis of medical image information systems. Under the same compression ratio, MSE(Mean Square Error) is 0.578 lower in JPEG than in ADCT-VQ while SNR(Signal to Noise Ratio) is 1.236 dB higher in JPEG than in ADCT-VQ.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.627-638
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• 1999

In this paper, the system with an artificial intelligent that is able itself to adjust the perimeter condition of the plant is presented. The proposed intelligent system is composed of two learning vector quantization(LVQ) networks, which are used mostly in the field of the pattern recognition and signal processing. From the external condition of the plant, the first LVQ network recognizes the pattern of the sensed signal and the second LVQ network judges synthetically user's characteristics and performs learning. The controller controls the plant using the reference value, which is the output value of the synthetic judgement part. In order to verify the usefulness of the proposed method, we simulated the two LVQs are implemented for the artificial intelligent illuminator as well as being carried out computer simulations. We implemented the proposed artificial intelligent illuminator and perform the experiment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.685-694
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• 1993

A progressive image transmission (PIT) scheme based on the classified transform vector quantization (CVQ) technique using the lapped orthogonal transform (LOT) and human visual system (HVS) weighting is proposed in this paper. Conventional block transform coding of images using DCT produces in general undesirable block-artifacts at low bit rates. In this paper, image blocks are transformed using the LOT and classified into four classes based on their structural properties and further divided adaptively into subvectors depending on the LOT coefficient statistics with HVS weighting to improve the reconstructed image quality by adaptive bit allocation. The subvectors are vector quantized and transmitted progressively. Coding tests using computer simulations show that the LOT/CVQ based PIT of images is a effective coding scheme. The results are also compared with those obtained using PIT/DCTVQ. The LOT/CVQ based PIT reduces the block-artifacts significantly.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.473-481
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• 2005

In this paper, we propose an image segmentation method preserving object's boundaries by using the number of quantized colors and merging regions using adaptive threshold values. First of all, the proposed method quantizes an original image by a vector quantization and the number of quantized colors is determined differently using PSNR each image. We obtain initial regions from the quantized image, merge initial regions in CIE Lab color space and RGB color space step by step and segment the image into semantic regions. In each merging step, we use color distance between adjacent regions as similarity-measure. Threshold values for region-merging are determined adaptively according to the global mean of the color difference between the original image and its split-regions and the mean of those variations. Also, if the segmented image of RGB color space doesn't split into semantic objects, we merge the image again in the CIE Lab color space as post-processing. Whether the post-processing is done is determined by using the color distance between initial regions of the image and the segmented image of RGB color space. Experiment results show that the proposed method splits an original image into main objects and boundaries of the segmented image are preserved. Also, the proposed method provides better results for objective measure than the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3A
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• pp.111-118
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• 2003

The transmit adaptive array requires the forward link channel information for evaluating the optimum transmit weight vector in which a feedback channel provides transmitter with the forward link channel information. The larger transmit adaptive array is, the higher required rate of feedback channel is. Therefore we consider the system that the N-transmit antenna system is expanded to the 2N-transmit antenna system, while the feedback channel is maintained as that of N-transmit antenna system. The increase of the number of antennas can produce the additional diversity gain, however the insufficient feedback bits assigned to each antenna aggravates the quantization error. In this paper, we propose the transmit antenna selection in order to improve the performance of transmit adaptive array having an insufficient feedback channel information. The effective method to transmit the weight vector is also introduced. System performances are investigated for the case of N=4 corresponding to the antenna selection diversity schemes on the flat fading channel and the multipath fading channel. The simulation results show that the proposed scheme can improve the system performance by 1 dB when the N is expanded to the 2N, while the feedback channel is restricted to that of N-transmit antenna system.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.672-676
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• 2010

This paper presents a learning rule that weights more on data near decision boundary. This learning rule generates better decision boundary by reducing the effect of outliers on the decision boundary. The proposed learning rule is integrated into IAFC neural network. IAFC neural network is stable to maintain previous learning results and is plastic to learn new data. The performance of the proposed fuzzy neural network is compared with performances of LVQ neural network and backpropagation neural network. The results show that the performance of the proposed fuzzy neural network is better than those of LVQ neural network and backpropagation neural network.

• The Journal of the Acoustical Society of Korea
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• v.6 no.3
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• pp.60-67
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• 1987

In this paper, a phoneme classification method for Korean speech recognition has been proposed and its performance has been studied. The phoneme classification has been done based on the phonemic features extracted by the prewindowed recursive least-square (PRLS) algorithm that is a kind of adaptive filter algorithms. Applying the PRLS algorithm to input speech signal, precise detection of phoneme boundaries has been made, Reference patterns of Korean phonemes have been generated by the ordinery vector quantization (VQ) of feature vectors obtained manualy from prototype regions of each phoneme. In order to obtain the performance of the proposed phoneme classification method, the method has been tested using spoken names of seven Korean cities which have eleven different consonants and eight different vowels. In the speaker-dependent phoneme classification, the accuracy is about $85\%$ considering simple phonemic rules of Korean language, while the accuracy of the speaker-independent case is far less than that of the speaker-dependent case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7B
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• pp.1378-1383
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• 1999

In this paper, an effective variable-block-based image compression technique using wavelet transform is proposed. Since the statistical property of each wavelet subband is different, we apply the adaptive quantization to each wavelet subband. In the proposed algorithm, each subband is divided into non-overlapping variable-sized blocks based on directional properties. In addition, we remove wavelet coefficients which are below a certain threshold value for coding efficiency. To compress the transformed data, the proposed algorithm quantizes the wavelet coefficients using scalar quantizer in LL subband and vector quantizers for other subbands to increase compression ratio. The proposed algorithm shows improvements in compression ratio as well as PSNR compared with the existing block-based compression algorithms. In addition, it does not cause any blocking artifacts in very low bit rates even though it is also a block-based method. The proposed algorithm also has advantage in computational complexity over the existing wavelet-based compression algorithms since it is a block-based algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.149-154
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• 2004

In this research, we selected the speech recognition to implement the electric wheelchair system as a method to control it by only using the speech and used DTW (Dynamic Time Warping), which is speaker-dependent and has a relatively high recognition rate among the speech recognitions. However, it has to have small memory and fast process speed performance under consideration of real-time. Thus, we introduced VQ (Vector Quantization) which is widely used as a compression algorithm of speaker-independent recognition, to secure fast recognition and small memory. However, we found that the recognition rate decreased after using VQ. To improve the recognition rate, we applied ART2 (Adaptive Reason Theory 2) algorithm as a post-process algorithm to obtain about 5% recognition rate improvement. To utilize ART2, we have to apply an error range. In case that the subtraction of the first distance from the second distance for each distance obtained to apply DTW is 20 or more, the error range is applied. Likewise, ART2 was applied and we could obtain fast process and high recognition rate. Moreover, since this system is a moving object, the system should be implemented as an embedded one. Thus, we selected TMS320C32 chip, which can process significantly many calculations relatively fast, to implement the embedded system. Considering that the memory is speech, we used 128kbyte-RAM and 64kbyte ROM to save large amount of data. In case of speech input, we used 16-bit stereo audio codec, securing relatively accurate data through high resolution capacity.