• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1961-1966
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• 2016

Compressive sensing has proved that a signal can be restored from less samples than the Nyquist rate. Reducing the required data rate is essential for a variety of fields including compression, transmission, and storage. It has been made lots of attempt to apply the compressive sensing theory into data intensive fields, such as image processing which needs to cover 4K and 8K pictures. In this paper, an image acquisition algorithm based on compressive sensing is proposed. It combines DCT, which can compact the energy of a image into a few coefficients, and the Noiselet transform, which is incoherent with DCT. The DCT coefficients represent the coarse structure of the images while the Noiselet information holds the fine details. Performance experiments with several images show that the proposed image acquisition algorithm not only outperforms the previous results, but also improves the reconstruction quality faster as the number of measurements increases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.9
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• pp.993-1000
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• 1992

Recently, many researchers have found that the speaker recognition rate is high when they perform the speaker recognition using statistical processing method of orthogonal parameter, which are derived from the analysis of speech signal and contain much of the speaker's identity. This method, however, has problems caused by vocalization speed or time varying feature of speed. Thus, to solve these problems, this paper proposes two methods of speaker recognition which combine DTW algorithm with the method using orthogonal parameters extracted from $Karthumem-Lo\'{e}ve$ Transform method which applies orthogonal parameters as feature vector to ETW algorithm and the other is the method which applies orthogonal parameters to the optimal path. In addition, we compare speaker recognition rate obtained from the proposed two method with that from the conventional method of statistical process of orthogonal parameters. Orthogonal parameters used in this paper are derived from both linear prediction coefficients and partial correlation coefficients of speech signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10C
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• pp.1484-1489
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• 2004

Given a video frame in terms of its 8${\times}$8 block-DCT coefncients, we wish to obtain a downsized or upsized version of this Dame also in terms of 8${\times}$8 block DCT coefficients. The DCT being a linear unitary transform is distributive over matrix multiplication. This fact has been used for downsampling video frames in the DCT domains in Dugad's, Mukherjee's, and Park's methods. The downsampling and upsampling schemes combined together preserve all the low-frequency DCT coefficients of the original image. This implies tremendous savings for coding the difference between the original frame (unsampled image) and its prediction (the upsampled image).This is desirable for many applications based on scalable encoding of video. In this paper, we extend the earlier works to various DCT sizes, when we downsample and then upsample of an image by a factor of two. Through experiment, we could improve the PSM values whenever we increase the DCT block size. However, because the complexity will be also increase, we can say there is a tradeoff. The experiment result would provide important data for developing fast algorithms of compressed-domain image/video resizing.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.431-445
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• 2003

In this paper, we experimentally investigated the precision of 3D positioning using 4S-Van images in photograrmmetric perspective. The 3D calibration target was built over building facade outside and was captured separately by two CCD cameras installed in 4S-Van. After then, we determined the interior orientation parameter for each CCD camera through self-calibration technique. With the interior orientation parameter computed, the bundle adjustment was performed to obtain the exterior orientation parameters simultaneously for two CCD cameras using calibration target image and object coordinates. The reverse lens distortion coefficients were computed and acquired by least squares method so as to introduce lens distortion into epipolar line. It was shown that the reverse lens distortion coefficients could transform image coordinates into lens distorted image coordinates within about 0.5 pixel. The proposed semi-automatic matching scheme incorporated with lens distorted epipolar line was implemented with scene images captured by 4S-Van in moving. The experimental results showed that the precision of 3D positioning from 4S-Van images in photograrmmetric perspective is within 2cm in the range of 20m from the camera.

• Polymer(Korea)
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• v.34 no.4
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• pp.369-375
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• 2010

We used melt polymerization method to prepare a series of aromatic liquid crystals (LCs) based on aromatic ester and amide units with the reactive methyl-maleimide end group, and then the resulting thermally cross-linked LCs to produce LC thermoset films by means of solution casting and the followed heat treatment. The synthesized LCs and LCTs were characterized by Fourier transform infrared (FTIR) spectroscopy, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), thermomechanical analysis (TMA), X-ray diffractometry (XRD), and polarizing optical microscopy (POM) with a hot stage. All of the LCs prepared by melt polymerization method formed smectic mesophases. The thermal properties of the LC and LCT films were strongly affected by the mesogen units in the main chain structures. The thermal expansion coefficients of samples were in the range of 27.72~50.95 ppm/$^{\circ}C$.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.11-17
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• 2023

This paper proposes a lightweight deep learning network for detecting an image splicing forgery. The research on image forgery detection using CNN, a deep learning network, and research on detecting and localizing forgery in pixel units are in progress. Among them, CAT-Net, which learns the discrete cosine transform coefficients of images together with images, was released in 2022. The DCT coefficients presented by CAT-Net are combined with the JPEG artifact learning module and the backbone model as pre-learning, and the weights are fixed. The dataset used for pre-training is not included in the public dataset, and the backbone model has a relatively large number of network parameters, which causes overfitting in a small dataset, hindering generalization performance. In this paper, this learning module is designed to learn the characterization depending on the DCT domain in real-time during network training without pre-training. The DCT RRU-Net proposed in this paper is a network that combines RRU-Net which detects forgery by learning only images and JPEG artifact learning module. It is confirmed that the network parameters are less than those of CAT-Net, the detection performance of forgery is better than that of RRU-Net, and the generalization performance for various datasets improves through the network architecture and training method of DCT RRU-Net.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.5
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• pp.513-518
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• 2008

The research of global-scale mass redistribution and it changed by Earth gravity filed variation observations, including Earth's oblateness $J_2$(also called low degree spherical harmonic coefficient $C_{20}$), is in continuous progress. Recently, the comparative analysis of geodetic observation SLR can be made by the development of GRACE and other time-variable gravity measurements. In this study, $C_{20}$ time series changes in the value of comparative analysis was got by GRACE monthly Gravity filed model (CSR RL04) for the period April 2002 to May 2008. And comparative analysis the harmonic coefficients of $C_{20}$ was obtained from SLR observations. Signal analysis for two time-series data was made by wavelet transform, CWT(continuous wavelet transform), XWT(cross wavelet transform) and WTC(wavelet coherence) methods. The results indicate that GRACE and SLR values for $C_{20}$ had both decreasing trend, as well as SLR data represent the annual frequencies, and GRACE was semiannual variations. In addition, the results of GRACE and SLR had a strong correlation with the XWT and WTC in an annual cycle.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.205-214
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• 2015

In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.683-694
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• 2002

In this paper, a digital image compression codec using 2D DWT(Discrete Wavelet Transform) is designed using the FPGA technology for real time operation The implemented image compression codec using wavelet decomposition consists of a wavelet kernel part for wavelet filtering process, a quantizer/huffman coder for quantization and huffman encoding of wavelet coefficients, a memory controller for interface with external memories, a input interface to process image pixels from A/D converter, a output interface for reconstructing huffman codes, which has irregular bit size, into 32-bit data having regular size data, a memory-kernel buffer to arrage data for real time process, a PCI interface part, and some modules for setting timing between each modules. Since the memory mapping method which converts read process of column-direction into read process of the row-direction is used, the read process in the vertical-direction wavelet decomposition is very efficiently processed. Global operation of wavelet codec is synchronized with the field signal of A/D converter. The global hardware process pipeline operation as the unit of field and each field and each field operation is classified as decomposition levels of wavelet transform. The implemented hardware used FPGA hardware resource of 11119(45%) LAB and 28352(9％) ESB in FPGA device of APEX20KC EP20k600CB652-7 and mapped into one FPGA without additional external logic. Also it can process 33 frames(66 fields) per second, so real-time image compression is possible.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1407-1419
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• 2020

Long time-series gridded data is crucial for the analyses of Earth environmental changes. Climate reanalysis and satellite images are now used as global-scale periodical and quantitative information for the atmosphere and land surface. This paper examines the feasibility of DCT-PLS (penalized least square regression based on discrete cosine transform) for the spatial gap filling of gridded data through the experiments for multiple variables. Because gap-free data is required for an objective comparison of original with gap-filled data, we used LDAPS (Local Data Assimilation and Prediction System) daily data and MODIS (Moderate Resolution Imaging Spectroradiometer) monthly products. In the experiments for relative humidity, wind speed, LST (land surface temperature), and NDVI (normalized difference vegetation index), we made sure that randomly generated gaps were retrieved very similar to the original data. The correlation coefficients were over 0.95 for the four variables. Because the DCT-PLS method does not require ancillary data and can refer to both spatial and temporal information with a fast computation, it can be applied to operative systems for satellite data processing.