• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.51-54
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• 2003

The z-transform method is a basic mathematical tool in analyzing and designing digital signal processing systems for discrete input and output signals. There are may cases where the output signal is in the form of a discrete convolution sum of an input function and a designed digital processing algorithm function. It is well known that the z-transform of the convolution sum becomes the product of the two z-transforms of the input function and the digital processing function, whose proofs require the causality of the digital signal processing function in the almost all the available references. However, not all of the convolution sum functions are based on the causality. Many digital signal processing systems such as image processing system may depend not on the time information but on the spatial information, which has nothing to do with causality requirement. Thus, the application of the causality-based z-transform theorem on the convolution sum cannot be used without difficulty in this case. This paper proves the z-transform theorem on the discrete convolution sum without causality requirement, and make it possible for the theorem to be used in analysis and desing for any cases.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.70-76
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• 2013

In this paper, we propose the bandpass form of discrete prolate spheroidal sequences(DPSS) which have the maximal energy concentration in a given passband and as such are very appropriate to obtain a projection of signals. The basic properties of the bandpass DPSS are also presented. Assuming a signal satisfies the finite time support and the essential band-limitedness conditions with a known center frequency, signal representation and interpolation techniques for band-limited signals using the bandpass DPSS are introduced where the reconstructed signal has minimal out-of-band energy. Simulation results are given to present the usefulness of the bandpass DPSS for efficient representation of band-limited signal.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.267-269
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• 2005

This paper describes a consideration on the sampling in linearly time-varying (LTV) systems in view of the convenience in digital signal processing. The relation between a continuous-time and a discrete-time system models is investigated for a simple linear time-invariant system. Based on the results of the investigation, we first consider discrete-time models for LTV systems, Then the simplicity of the models in terms of microprocessor-based digital signal processing is compared.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.517-524
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• 1998

This paper discusses the development of a reactive power controller using digital signal processing. Digital Signal Processing is the technique of using digital devices to Process continuous signals or data, often in real-time. And DSP algorithms are associated with a discrete time interval between input samples. When one designs a digital filter, one can use a Laplace transform to determine the continuous time frequency response. The corresponding discrete time transform is called Z transform and depends upon discrete samples of the input spaced equally in time. The objectives of this paper are to minimize real power losses and improve the power factor of a given system. Also, the implementation of a direct-form non recursive filter on the TMS320C31 has been described. The application of this microprocessor-based controller using DSP on test system reveals its numerous advantages. Performance and features of the controller for the reactive power control are analyzed.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.237-247
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• 2014

This paper describes a 2D discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor, Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The TQWT can also be used as an easily-invertible discrete approximation of the continuous wavelet transform. The transform is based on a real valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e. g. 3-4 times overcomplete) being sufficient for the analysis/synthesis functions to be well localized. Therefore, This method services good performance in image processing fields.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.1-8
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• 2014

Digital watermarking techniques are attracting attention as a proper solution to protect copyright for multimedia data. This paper proposes a new audio watermarking method based on Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) for copyright protection. In our proposed watermarking method, the original audio is transformed into DCT domain and divided into two parts. Synchronization code is applied on the signal in first part and 2 levels DWT domain is applied on the signal in second part. The absolute value of DWT coefficient is divided into arbitrary number of segments and calculates the energy of each segment and middle peak. Watermarks are then embedded into each middle peak. Watermarks are extracted by performing the inverse operation of watermark embedding process. Experimental results show that the hidden watermark data is robust to re-sampling, low-pass filtering, re-quantization, MP3 compression, cropping, echo addition, delay, and pitch shifting, amplitude change. Performance analysis of the proposed scheme shows low error probability rates.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.54-62
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• 2000

Adaptive filtering method is one of signal processing area which is frequently used in the case of statistical characteristic change in time-varing situation. The performance of adaptive filter is usually evaluated with complexity of its structure, convergence speed and misadjustment. The structure of adaptive filter must be simple and its speed of adaptation must be fast for real-time implementation. In this paper, we propose chirp discrete cosine transform (CDCT), which has the characteristics of CZT (chrip z-transform) and DCT (discrete cosine transform), and then CDCTLMS (chirp discrete cosine transform LMS) using the above mentioned algorithm for the improvement of its speed of adaptation. Using loaming curve, we prove that the proposed method is superior to the conventional US (normalized LMS) algorithm and DCTLMS (discrete cosine transform LMS) algorithm. Also, we show the real application for the ultrasonic signal processing.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.97.1-97
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• 2002

$\textbullet$ Abstract $\textbullet$ Introduction $\textbullet$ Wire Rope Detecting System $\textbullet$ Signal Processing $\textbullet$ Experiment $\textbullet$ Conclusion $\textbullet$ Reference