• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.169-172
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.871-876
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• 2010

Currently the development of powerline communication technology has become possible due to the high-speed communications. But the communication lines used for power line communication, not wires carrying power wiring is because when sending high-frequency wireless communication system unintentionally be influenced. To compensate for these shortcomings by using notch filters to reduce interference has been studied. Wavelet-based OFDM on the other hand by the method has been used to reduce interference. Wavelet-based OFDM has been used in the existing powerline OFDM scheme using FFT instead of the general structure of the CMFB filters to generate a signal. By doing so, subtly signals per frequency band, cut it, is to realize how efficient highways. It brought a deep filter characteristics, a flexible notch filter can be achieved without an external circuit has an advantage. In this paper, Using Wavelet OFDM powerline communication system is designed and presented the results of simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1155-1163
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• 2010

An algorithm of the conventional wavelet shift keying is carried out that the scaling function and wavelet are encoded to 1(mark) and 0(space) for the input binary data, respectively. Two bit modulation technique which uses four carrier frequencies is existed. Four carrier frequencies are defined as scaling function, inversed scaling function, wavelet, and inversed wavelet, which are encoded to 10, 11, 00 and 01, respectively. In this paper, we defined 4-ary SWSK (4-ary scaling wavelet shift keying) which is two bit modulation, and it is derived to the probability of bit error and symbol error of the defined system from QPSK. In order to analyze to the performance of 4-ary SWSK, we are obtained in terms of the probability of bit error and symbol error for QPSK (quadrature phase shift keying), MFSK(M-ary frequency shift keying) and proposed method. As a results of simulation, we confirmed that the proposed method was superior to the performance in terms of the probability of bit error and symbol error.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.777-783
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• 2008

Nowadays wavelet transforms are widely used for the analyses of PIV velocity vector fields. This is bemuse the wavelet provides not only spatial information of the velocity vectors but also of time and frequency domains. In this study, a discrete wavelet trC1f1$form has been applied to real PIV images of bubbly flows. The vector fields obtained by a self-made cross-correlation PIV algorithm were used for the discrete wavelet transform The performances of the discrete wavelet transform is investigated by changing the level of power of discretization. The decomposed images by the wavelet multiresolution showed conspicuous characteristics of the bubbly flows according to the level changes. The high spatial bubble concentrated area could be evaluated by the constructed discrete wavelet transform algorithm, at which high leveled wavelets could play a dominant roles to reveal the flow characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.581-586
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• 2006

Incidents, which is unexpected unusual events such as traffic accidents, have increased on the most roads in Korea. The obstruction of a fluent traffic flow occurred by incidents causes the traffic congestion and decreases the capacity. The Wavelet technique was applied to detect the road section and the happening time of incidents on urban freeways in this study, and this technique has been widely used in many engineering fields such as an electrical engineering, etc. The availability and validity of the Wavelet technique to the detection of incidents was examined by the occupancy rate, the important element of traffic flows, which is extracted from the data of detectors installed on Seoul Urban freeways. Then, this result is compared to the California Algorithm and the Low-Pass Filtering Algorithm among basic present detection algorithms, which are based on the occupancy rate. As a result, the false alarm rate of this method was similar as that of the California algorithm and the Low-Pass Filtering algorithm, but the detection rate is higher.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.113-118
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• 2024

In this paper, we propose an optimal wavelet in a system for canceling background noise of acoustic signals. This system performed Discrete Wavelet Transform(DWT) instead of the existing Short Time Fourier Transform(STFT) and then improved noise cancellation performance through a deep learning process. DWT functions as a multi-resolution band-pass filter and obtains transformation parameters by time-shifting the parent wavelet at each level and using several wavelets whose sizes are scaled. Here, the noise cancellation performance of several wavelets was tested to select the most suitable mother wavelet for analyzing the speech. In this study, to verify the performance of the noise cancellation system for various wavelets, a simulation program using Tensorflow and Keras libraries was created and simulation experiments were performed for the four most commonly used wavelets. As a result of the experiment, the case of using Haar or Daubechies wavelets showed the best noise cancellation performance, and the mean square error(MSE) was significantly improved compared to the case of using other wavelets.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.761-769
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• 2017

Accurately extracting the axle distribution information of a passing vehicle from bridge dynamic responses experiences a key and challenging step in non-pavement bridge weigh-in-motion (BWIM). In this article, the wavelet transformation is adopted and the wavelet coefficient curve is used as a substitute for dynamic response. The driving frequency is introduced and expanded to multi-axle vehicle, and the wavelet coefficient curve on specific scale corresponding to the driving frequency is confirmed to contain obvious axle information. On this basis, an automatic method for axle distribution information identification is proposed. The specific wavelet scale can be obtained through iterative computing, and the false peaks due to bridge vibration can be eliminated through cross-correlation analysis of the wavelet coefficients of two measure points. The integrand function that corresponds to the maximum value of the cross-correlation function is used to identify the peaks caused by axles. A numerical application of the proposed axle information identification method is carried out. Numerical results demonstrate that this method acquires precise axle information from the responses of an axle-insensitive structure (e.g., girder) and decreases the requirement of sensitivity structure of BWIM. Finally, an experimental study on a full-scale simply supported bridge is also conducted to verify the effectiveness of this method.

• Composites Research
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• v.13 no.1
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• pp.61-68
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• 2000

A new approach is presented for the analysis of transient waves propagating in anisotropic composite laminates. The wavelet transform (WT) using the Gabor wavelet is applied to the time-frequency analysis of dispersive flexural waves. It is shown that the peaks of the magnitude of WT in time-frequency domain is related to the arrival times of group velocity. Experiments are performed using a lead break as the simulated fracture source on the surface of quasi-isotropic and unidirectional laminates. For predictions of the dispersion of the flexural mode, Mindlin plate theory is shown to give good agreement with the experimental results. Based on the frequency-dependent arrival times and angular dependence of group velocities of flexural waves, the problem of source location in anisotropic laminates is considered and the results are given.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.449-453
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• 2001

In this paper, we propose an algorithm to design the adaptive structure of wavelet neural network with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign a wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology that a network designer can construct wavelet neural network according to one's intention. We apply the proposed constructing algorithm of the adaptive structure of wavelet neural network to approximation problems of 1-D and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.2
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• pp.81-88
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• 2004

For scale and rotation invariant pattern recognition of images with noise, an optical wavelet CHF-fSDF filter is proposed. Wavelet CHF-fSDF filter is synthesized by each single CHF extracted from scale-changed and wavelet transformed images for a referene image as training images. The proposed optical wavelet CHF-fSDF filter is the type of the matched filter so that it can use the structure of 4f optical correlation system. The results of computer simulation show that the proposed filter has the rotation and scale-invariant correlation output and it is useful in the noisy input.