• ETRI Journal
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• v.42 no.4
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• pp.596-607
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• 2020

Scaling is an important operation because of the iterative nature of arithmetic processes in digital signal processors (DSPs). In residue number system (RNS)-based DSPs, scaling represents a performance bottleneck based on the complexity of intermodulo operations. To design an efficient RNS scaler for special moduli sets, a body of literature has been dedicated to the study of the well-known moduli sets {2ⁿ - 1, 2ⁿ, 2ⁿ + 1} and {2ⁿ, 2ⁿ - 1, 2ⁿ⁺¹ - 1}, and their extension in vertical or horizontal forms. In this study, we propose an efficient programmable RNS scaler for the arithmetic-friendly moduli set {2^n+p, 2ⁿ - 1, 2ⁿ⁺¹ - 1}. The proposed algorithm yields high speed and energy-efficient realization of an RNS programmable scaler based on the effective exploitation of the mixed-radix representation, parallelism, and a hardware sharing technique. Experimental results obtained for a 130 nm CMOS ASIC technology demonstrate the superiority of the proposed programmable scaler compared to the only available and highly effective hybrid programmable scaler for an identical moduli set. The proposed scaler provides 43.28% less power consumption, 33.27% faster execution, and 28.55% more area saving on average compared to the hybrid programmable scaler.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.4
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• pp.305-312
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• 2014

In this study, in order to quantitatively confirm walking rehabilitation degree, we analyzed EMG pattern simulated abnormal gait and normal gait by applying a curve fitting. We calculated the suitable high-order function for EMG signal, and classified them into 5 groups by using cluster analysis. Depending on the distance from normal pattern group, we listed the pattern group and then the distribution of each variables were confirmed. The amplitude-decreased pattern was the most similar to the normal pattern, but the reversed pattern showed the lowest similarity. Due to the smaller overlapping range, the distribution of the groups were possible to classify using the value of variable. The standard deviation of each term coefficient was compared to indicate the quantitative rehabilitation extent, and the higher value was confirmed as the pattern is close to the normal pattern. Consequently, the representation of quantitative rehabilitation extent is expected to contribute to the more effective rehabilitation method study.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.65-71
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• 2000

Finite wordlength effect of the recursive implementation of SDFT(sliding-DFT) is analytically derived in this paper. Representation errors of the twiddle coefficients and the data registers are the two major causes of the spectral errors in the recursive implementation. The noise-to-signal ratio is analytically derived in terms of the coefficients wordlength, the data registers wordlength, and the DFT's block-length used in the computation Error dynamic equation is obtained from the recursive DFT and the probabilistic models for the coefficients error and the round-off error are introduced for the NSR derivation, The result of the NSR derivation is verified with the simulation data.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.225-232
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• 2009

Context-aware technologies for ubiquitous computing are necessary to study the representation of gathered context-information appropriately, the understanding of user's intention using context-information, and the offer of pertinent services for users. In this paper, we propose the WS-CAM(Web Services based Context-Aware Middleware) framework for context-aware computing. WS-CAM provides ample power of expression and inference mechanisms to various context-information using an ontology-based context model. We also consider that WS-CAM is the middleware-independent structure to adopt web services with characteristic of loosely coupling as a matter of communication of context-information. In this paper, we describe a scenario for lecture services based on the ubiquitous computing e e e e e e to verify the utilization of WS-CAM We also show an example of middleware-independent system expansion to display the merits of web-based services. WS-CAM for lecture services represented context-information itodomaits as OWL-based ontology model effectively, and we confirmed the information is inferred to high level context-information by user-defined rules. We also confirmed the context-information is transferred to application services middleware-independently using various web methods provided by web services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4119-4124
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• 2011

In a MPC coding using excitation source of voiced and unvoiced, it would be a distortion of speech waveform in case of increasing or decreasing of speech signal amplitude in a frame. This is caused by normalization of synthesis speech signal in the process of restoration the multi-pulses of representation section. To solve this problem, this paper present a method of amplitude compensation(AC-MPC) in a multi-pulses each pitch interval in order to reduce distortion of speech waveform. I was confirmed that the method can be synthesized close to the original speech waveform. And I evaluate the MPC and AC-MPC using amplitude compensation method. As a result, SNRseg of AC-MPC was improved 0.7dB for female voice and 0.7dB for male voice respectively. Compared to the MPC, SNRseg of AC-MPC has been improved that I was able to control the distortion of the speech waveform finally. And so, I expect to be able to this method for cellular phone and smart phone using excitation source of low bit rate.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.2
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• pp.190-199
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• 2014

In the last years, fault problem in power electronics has been more and more investigated both from theoretical and practical point of view. The fault problem can cause equipment failure, data and economical losses. And the analyze system require to ensure fault problem and also rectify failures. The current errors on these faults are applied for identified type of faults. This paper presents technique to detection and identification faults in three-phase voltage source inverter (VSI) by using time-frequency distribution (TFD). TFD capable represent time frequency representation (TFR) in temporal and spectral information. Based on TFR, signal parameters are calculated such as instantaneous average current, instantaneous root mean square current, instantaneous fundamental root mean square current and, instantaneous total current waveform distortion. From on results, the detection of VSI faults could be determined based on characteristic of parameter estimation. And also concluded that the fault detection is capable of identifying the type of inverter fault and can reduce cost maintenance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2851-2865
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• 2014

To improve the rate-distortion performance of distributed video compressive sensing (DVCS), the adaptive sparse basis and nonlocal similarity of video are proposed to jointly reconstruct the video signal in this paper. Due to the lack of motion information between frames and the appearance of some noises in the reference frames, the sparse dictionary, which is constructed using the examples directly extracted from the reference frames, has already not better obtained the sparse representation of the interpolated block. This paper proposes a method to construct the sparse dictionary. Firstly, the example-based data matrix is constructed by using the motion information between frames, and then the principle components analysis (PCA) is used to compute some significant principle components of data matrix. Finally, the sparse dictionary is constructed by these significant principle components. The merit of the proposed sparse dictionary is that it can not only adaptively change in terms of the spatial-temporal characteristics, but also has ability to suppress noises. Besides, considering that the sparse priors cannot preserve the edges and textures of video frames well, the nonlocal similarity regularization term has also been introduced into reconstruction model. Experimental results show that the proposed algorithm can improve the objective and subjective quality of video frame, and achieve the better rate-distortion performance of DVCS system at the cost of a certain computational complexity.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4E
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• pp.112-119
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• 2004

This paper presents a method for the automatic recognition of pitch accents with no prior knowledge about the phonetic content of the signal (no knowledge of word or phoneme boundaries or of phoneme labels). The recognition algorithm used in this paper is a time-delay recurrent neural network (TDRNN). A TDRNN is a neural network classier with two different representations of dynamic context: delayed input nodes allow the representation of an explicit trajectory F0(t), while recurrent nodes provide long-term context information that can be used to normalize the input F0 trajectory. Performance of the TDRNN is compared to the performance of a MLP (multi-layer perceptron) and an HMM (Hidden Markov Model) on the same task. The TDRNN shows the correct recognition of $91.9{\%}\;of\;pitch\;events\;and\;91.0{\%}$ of pitch non-events, for an average accuracy of $91.5{\%}$ over both pitch events and non-events. The MLP with contextual input exhibits $85.8{\%},\;85.5{\%},\;and\;85.6{\%}$ recognition accuracy respectively, while the HMM shows the correct recognition of $36.8{\%}\;of\;pitch\;events\;and\;87.3{\%}$ of pitch non-events, for an average accuracy of $62.2{\%}$ over both pitch events and non-events. These results suggest that the TDRNN architecture is useful for the automatic recognition of pitch accents.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.82-89
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• 2004

This paper describes how to reduce the effect of an occupation threshold by that the transform of mixture components of HMM parameters is controlled in hierarchical tree structure to prevent from over-adaptation. To reduce correlations between data elements and to remove elements with less variance, we employ PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible, and decline the effect of over-adaptation. When we set lower occupation threshold and increase the number of transformation function, ordinary WLLR adaptation algorithm represents lower recognition rate than SI models, whereas the proposed MLLR adaptation algorithm represents the improvement of over 2% for the word recognition rate as compared to performance of SI models.

• The Journal of Korean Association of Computer Education
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• v.10 no.1
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• pp.97-105
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• 2007

In this paper, visualization of convolution operation is presented, which is implemented by scalable vector graphics (SVG). Convolution operation is one of the basic essential concepts in the area of signal and image processing. However, it is difficult for students to intuitively understand the operation of convolution since it is mainly based on mathematical representation. We present the visualization of convolution operation and its applications which are implemented by SVG. The effects of the proposed approach have been analyzed by interviews. It has been seen that the proposed visualization of convolution operation could be effectively applied to learn the convolution operation and its applications.