• Journal of the Korea Computer Graphics Society
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• v.15 no.3
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• pp.11-18
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• 2009

In this paper, we introduce a novel method to extend the JPEG standard, which is the most widely used for lossy image compression, in order to improve compression ratio. To employ two of the most successful methodologies for the data compression: differential coding and quantization simultaneously, we propose a line-based approach. For each line in a block, we apply one-dimensional discrete cosine transformation to the increments instead of the pixel values. Those values are quantized and entropy-coded similarly to the JPEG standard. To further increase compression ratio, the proposed method is plugged into the JPEG standard to form a new compression method, in which the proposed method are applied to only selected JPEG blocks. In our experiment, we found that the proposed method outperform the JPEG standard when the qualities of the coded images are set to be high. We believe the proposed method can be simply plugged into the standard to improve its compression ratio for higher quality images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5855-5877
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• 2017

In view of personal health and disease management based on cost effective healthcare services, there is a growing need for real-time monitoring services. The electrocardiogram (ECG) signal is one of the most important of health information and real-time monitoring of the ECG can provide an efficient way to cope with emergency situations, as well as assist in everyday health care. In this system, it is essential to continuously collect and transmit large amount of ECG data within a given time and provide maximum user convenience at the same time. When considering limited wireless capacity and unstable channel conditions, appropriate signal processing and transmission techniques such as compression are required. However, ISO/IEEE 11073 standards for interoperability between personal health devices cannot properly support compressed data transmission. Therefore, in the present study, the problems for handling compressed data are specified and new extended agent and manager are proposed to address the problems while maintaining compatibility with existing devices. Extended devices have two PM-stores enabling compression and a novel transmission scheme. A variety of compression techniques can be applied; in this paper, discrete cosine transformation (DCT) is used. And the priority of information after DCT compression enables new transmission techniques for performance improvement. The performance of the compressed signal and the original uncompressed signal transmitted over the noisy channel are compared in terms of percent root mean square difference (PRD) using our simulation results. Our transmission scheme shows a better performance and complies with 11073 standards.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.117-123
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• 2018

In this study, we suggest a fast image reconstruction scheme using Poisson equation from image gradient domain. In this approach, using the Poisson equation, a guided vector field is created by employing source and target images within a selected region at the first step. Next, the guided vector is used in generating the result image. We analyze the problem of reconstructing a two-dimensional function that approximates a set of desired gradients and a data term. The joined data and gradients are able to work like modifying the image gradients while staying close to the original image. Starting with this formulation, we have a screened Poisson equation known in physics. This equation leads to an efficient solution to the problem in FFT domain. It represents the spatial filters that solve the two-dimensional screened Poisson model and shows gradient scaling to be a well-defined sharpen filter that generalizes Laplace sharpening. We demonstrate the results using a discrete cosine transformation based this Poisson model.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.30-36
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• 2003

According to an evolution of image and video compression technologies, most digital images are in the compressed form. Resizing of these compressed images have various applications such as transmission of resized image according to varying bandwidth, content adaptation for display purpose and etc. Discrete Cosine Transform (DCT) is the most popular transformation for image compression. Recently, several researches have been performed to obtain the reconstructed image of original size in the DCT domain after downsampling and upsampling in the DCT domain. Main focus of these researches is to improve quality of the reconstructed image after downsampling and upsampling in the DCT domain In this paper, we present an modified IDCT method to downsize DCT-encoded image. Furthermore, we propose an efficient scheme for image downsampling and upsampling in the DCT domain With these modified IDCT method. The proposed scheme Provides higher PSNR values than the existing schemes In terms of the reconstructed image after halving and doubling in the DCT domain.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.162-173
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• 2013

The purpose of this paper is to classify VOC gases by emulating the characteristics found in biological olfaction. For this purpose, we propose new signal processing method based a polymeric chemical sensor array consisting of 4096 sensors which is created by NEUROCHEM project. To remove unstable sensors generated in the manufacturing process of very large scaled chemical sensor array, we used discrete wavelet transformation and cosine similarity. And, to remove the supernumerary redundancy, we proposed the method of selecting candidates of representative sensor representing sensors with similar features by Fuzzy c-means algorithm. In addition, we proposed an improved algorithm for selecting representative sensors among candidates of representative sensors to better enhance classification ability. However, Classification for very large scaled sensor array has a great deal of time in process of learning because many sensors are used for learning though a redundancy is removed. Throughout experimental trials for classification, we confirmed the proposed method have an outstanding classification ability, at transient state as well as steady state.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.