• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.11
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• pp.14-23
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• 2002

Two implementation methods for SOVA (Soft Output Viterbi Algorithm)of Turbo decoder are applied and verfied. The first method is the combination of a trace back (TB) logic for the survivor state and a double trace back logic for the weight value in two-step SOVA. This architecure of two-setp SOVA decoder allows important savings in area and high-speed processing compared with that of one-step SOVA decoding using register exchange (RE) or trace-back (TB) method. Second method is adjusting the reliability value with a scaling factor between 0.25 and 0.33 in order to compensate for the distortion for a rate 1/3 and 8-state SOVA decoder with a 256-bit frame size. The proposed schemes contributed to higher SNR performance by 2dB at the BER 10E-4 than that of SOVA decoder without a scaling factor. In order to verify the suggested schemes, the SOVA decoder is testd using Xillinx XCV 1000E FPGA, which runs at 33.6MHz of the maximum speed with 845 latencies and it features 175K gates in the case of 256-bit frame size.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.115-125
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• 2016

The Bollinger Band indicating the current price position in the recent price action range is obtained by adding/substracting the simple standard deviation (SSD) to/from the simple moving average (SMA). In this paper, we first compare the characteristics of the SMA and the exponential moving average (EMA) in the operator's point of view. A basic equation is obtained between the interval length N of the SMA operator and the weighting factor ${\rho}$ of the EMA operator, that makes the centers of the 1st order momentums of each operator impulse respoinse identical. For equivalent N and ${\rho}$, frequency response examples are obtained and compared by using the discrete time Fourier transform. Based on observation that the SMA operator reacts more excessively than the EMA operator, we propose a novel exponential standard deviation (ESD) band centered at the EMA and derive an auto recursive formula for the proposed ESD band. Practical examples for the ESD band show that it has a smoother bound on the price action range than the Bollinger Band. Comparisons are also made for the gap corrected chart to show the advantageous feature of the ESD band even in the case of gap occurrence. Trading techniques developed for the Bollinger Band can be straight forwardly applied to those for the ESD band.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.