• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.2
• /
• pp.97-103
• /
• 2010

In this paper, we propose a new digital watermarking technique. The main idea of the proposed algorithm relies on the assumption that any real number can be expressed as a summation of the exponential form with base of 2 and if only consider the first few summations some numbers can be expressed in the same form. Therefore, we can be sure that some amount of changes does not affect the first few summations. The algorithm decomposes a host image in wavelet domain and intensity of the significant wavelet coefficient is expressed in exponential form with base of 2. Multiple barcode watermarks are then embedded by modifying the parity of the exponent. The proposed scheme is semi-blind and also offers either objective or subjective deteew su as well. From extracted watermarks, more accurate watermark is obtained by merging technique as a final watermark. As a simulation result, the proposed algorithm could resist most cases of salt and pepper noise, Gaussian noise and JPEG compression.

• Journal of Korea Water Resources Association
• /
• v.53 no.6
• /
• pp.395-408
• /
• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• The Journal of the Acoustical Society of Korea
• /
• v.23 no.1
• /
• pp.8-16
• /
• 2004

An inversion method is presented for the determination of the compressional wave speed, compressional wave attenuation, thickness of the sediment layer and density as a function of depth for a horizontally stratified ocean bottom. An experiment for estimating those properties was conducted in the shallow water of South Sea in Korea. In the experiment, a light bulb implosion and the propagating sound were measured using a VLA (vertical line array). As a method for estimating the geoacoustic properties, a coherent broadband matched field processing combined with Genetic Algorithm was employed. When a time-dependent signal is very short, the Fourier transform results are not accurate, since the frequency components are not locatable in time and the windowed Fourier transform is limited by the length of the window. However, it is possible to do this using the wavelet transform a transform that yields a time-frequency representation of a signal. In this study, this transform is used to identify and extract the acoustic components from multipath time series. The inversion is formulated as an optimization problem which maximizes the cost function defined as a normalized correlation between the measured and modeled signals in the wavelet transform coefficient vector. The experiments and procedures for deploying the light bulbs and the coherent broadband inversion method are described, and the estimated geoacoustic profile in the vicinity of the VLA site is presented.

• Journal of the Korean Geographical Society
• /
• v.51 no.4
• /
• pp.509-523
• /
• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• Journal of Korea Multimedia Society
• /
• v.11 no.11
• /
• pp.1501-1514
• /
• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.4A
• /
• pp.336-344
• /
• 2005

Discrete Wavelet Transform(DWT) is the oncoming generation of compression technique that has been selected for MPEG4 and JEPG2000, because it has no blocking effects and efficiently determines frequency property of temporary time. In this paper, we propose an efficient bit-serial architecture for the low-power and low-complexity DWT filter, employing two-channel QMF(Qudracture Mirror Filter) PR(Perfect Reconstruction) lattice filter. The filter consists of four lattices(filter length=8) and we determine the quantization bit for the coefficients by the fixed-length PSNR(peak-signal-to-noise ratio) analysis and propose the architecture of the bit-serial multiplier with the fixed coefficient. The CSD encoding for the coefficients is adopted to minimize the number of non-zero bits, thus reduces the hardware complexity. The proposed folded 1D DWT architecture processes the other resolution levels during idle periods by decimations and its efficient scheduling is proposed. The proposed architecture requires only flip-flops and full-adders. The proposed architecture has been designed and verified by VerilogHDL and synthesized by Synopsys Design Compiler with a Hynix 0.35$\mu$m STD cell library. The maximum operating frequency is 200MHz and the throughput is 175Mbps with 16 clock latencies.

• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.1
• /
• pp.19-25
• /
• 2010

In this paper, digital watermarking method using quad-tree algorithm is proposed. The proposed algorithm searches the significant coefficient of the watermark by using quad-tree algorithm and inserts the watermark by the Cox's algorithm. The simulation for performance analysis of the proposed algorithm is implemented about the effect of quad-tree algorithm in wavelet domain and that of embedding watermark in each subband coefficient (HH, LH, HL) and each DWT level, and that of embedding in the lowest frequency band (LL). As a simulation result, digital watermarking using quad-tree algorithm improves the watermarking performance in comparison with inserting watermark in the significant coefficients of fixed frequency band. The watermarking performance of simultaneously embedding in HH, LH, and HL band of DWT(6 level) is better than that of different cases. In addition, insertion the watermark to the LL band about 30~60% of all watermarks improves the correlation value while PSNR performance decreases 1~3dB.

• Geophysics and Geophysical Exploration
• /
• v.26 no.4
• /
• pp.199-210
• /
• 2023

The physical properties of rocks in reservoirs change after CO₂ injection, we modeled a reservoir with a transition zone within which the physical properties change linearly. The function of the Wolf reflection coefficient consists of the velocity ratio of the upper and lower layers, the frequency, and the thickness of the transition zone. This function can be used to estimate the thickness of a reservoir or seafloor transition zone. In this study, we propose a method for predicting the thickness of the transition zone using deep learning. To apply deep learning, we modeled the thickness-dependent Wolf reflection coefficient on an artificial transition zone formation model consisting of sandstone reservoir and shale cap rock and generated time-frequency spectral images using the continuous wavelet transform. Although thickness estimation performed by comparing spectral images according to different thicknesses and a spectral image from a trace of the seismic stack did not always provide accurate thicknesses, it can be applied to field data by obtaining training data in various environments and thus improving its accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.1
• /
• pp.192-210
• /
• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• Journal of the Korean Society of Radiology
• /
• v.3 no.1
• /
• pp.29-39
• /
• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.