• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.603-605
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• 1997

A new algorithm using 1-dimensional wavelet transform for autofocusing of optical instrument has been developed. Previous studies based on the conventional frequency analysis have shown that as the lens-object distance approaches the optimum value, the high frequency energy in the corresponding image shows a consistent increase. However, as conventional frequency analysis techniques hide spatial distribution of each band energy, shape information in the original signal cannot be easily utilized. In this paper, a newly devised wavelet based focus measuring scheme is presented. Unlike other frequency domain analysis techniques that simply produce "frequency-only" spectra, wavelet analysis provides a "time-frequency" localized view of a given signal. As a result, both frequency band filtering and spatial distribution filtering can easily be realized. Depending on the proposed focus quality measuring algorithm, a fast and reliable automatic focus adjustment of optical devices could be implemented.

• ETRI Journal
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• v.33 no.6
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• pp.961-964
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• 2011

The cognitive ultra-wideband (UWB) network detects interfering narrowband systems and adapts its configuration accordingly. An inherently adaptive and flexible candidate for cognitive UWB transmission is the wavelet packet multicarrier modulation (WPMCM). In this letter, we use an enhanced forward consecutive mean excision thresholding algorithm to tackle the noise uncertainty in the wavelet-based sensing of WPMCM systems, and mathematical analysis is performed for primary user channel fading. As a benchmark, we compare the proposed system with a conventional fast Fourier transformation-based system, and performance investigation proves significant improvements when primary and secondary links are subjected to multipath fading and noise.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.116-124
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• 2009

This paper proposes a new Artificial-Intelligent(AI) Power Quality(PQ) diagnosis algorithm using Discrete Wavelet Transform(DWT), Fast Fourier Transform(FFT), Root-Mean-Square(RMS) value. The developed algorithm is able to detect and classify the PQ problems such as the transient, the voltage sag, the voltage swell, the voltage interruption and the total harmonics distortion. The 15.36[kHz] sampling frequency is used to measure the voltages in a power system. The measured signals are used for DWT, FFT, RMS calculation. For AI diagnosis of the PQ problems, a simple multi-layered Artificial Neural Network(ANN) with the back-propagation algorithm is adopted, programmed in C++ and tested in PSIM simulation studies. Finally, the algorithm, which is installed in MP PQ+256 with TI DSP320C6713, is proved to diagnose the PQ problems efficiently.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.174-184
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• 2013

For the successful R&D and deployment of LED-based green energy lighting systems, the real-time power quality measurement of both various non-linear power signals including pulse waveform, spike waveform, etc and the undesired-signals including harmonics, sag, swell, etc is required. In this paper, we propose a low-cost power quality measurement (PQM) method for low- (60Hz-several KHz) to high-frequency (several tens KHz) power signals, which are generated by green-energy lighting systems, and implement a PQM testbed using TI TMS320F28335 MCU. The proposed algorithm is programmed using C in the CCS (Code Composer Studio) 3.3 environment and is verified using test signals generated by an arbitrary signal generator, NF-WF1974. In the implemented testbed, we can measure various non-linear current signals that LED SMPS generates, analyze harmonics by fast Fourier transform, and test sag, swell, and interruption using wavelet transform.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.80-86
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• 2002

This paper presents a fast hardware architecture to implement a 2-D DWT(Discrete Wavelet Transform) computed by lifting scheme framework. The conventional 2-D DWT hardware architecture has problem in internal memory, hardware resource, and latency. The proposed architecture was based on the 4-way partitioned data set. This architecture is configured with a pipelining parallel architecture for 4-way partitioning method. Due to the use of this architecture, total latency was improved by 50%, and memory size was reduced by using lifting scheme.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.45-55
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• 1997

In this paper, we propose a fast algorithm with adaptive thresholding for the wavelet transform (WT) based blocking artifact reduction. In the fast algorithm, all processings that are equivalent to the processing in WT domain of the first and second scale are performed in spatial domain. In the adaptive thresholding, the threshold values used to classify the block boundary are selected adaptively according to each input image by using the statistical properties of the WT of the coded signal at block boundary and at block center, which can be obtained in spatial domain. Experimental results showed that the proposed fast algorithm is about 10 times faster than the WT-based algorithm. It also was found that the postprocessing with proposed adaptive thresholding yields some PSNR improvement and better subjective quality over that with nonadaptive thresholding which has best performance at high compression ratios of a certain .image, even at low compression ratios.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.814-821
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• 2002

The SPIHT(set partitioning in hierarchical tree) is efficient and well-known in the zerotree coding algorithm. However SPIHT's high memory requirement is a major difficulty for hardware implementation. In this paper we propose low-memory and fast zerotree algorithm. We present following three methods for reduced memory and fst coding speed. First, wavelet transform by lifting has a low memory requirement and reduced complexity than traditional filter bank implementation. The second method is to divide the wavelet coefficients into a block. Finally, we use NLS algorithm proposed by Wheeler and Pearlman in our codec. Performance of NLS is nearly same as SPIHT and reveals low and fixed memory and fast coding speed.

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.621-632
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• 2004

Recent measurement studies of real teletraffic data in modern telecommunication networks have shown that self-similar (or fractal) processes may provide better models of teletraffic in modern telecommunication networks than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A new generator of pseu-do-random self-similar sequences, based on the fractional Gaussian nois and a wavelet transform, is proposed and analysed in this paper. Specifically, this generator uses Daubechies wavelets. The motivation behind this selection of wavelets is that Daubechies wavelets lead to more accurate results by better matching the self-similar structure of long range dependent processes, than other types of wavelets. The statistical accuracy and time required to produce sequences of a given (long) length are experimentally studied. This generator shows a high level of accuracy of the output data (in the sense of the Hurst parameter) and is fast. Its theoretical algorithmic complexity is 0(n).