• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.8
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• pp.816-822
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• 2003

In this paper, we propose an efficient iteration control method with low complexity for Turbo TCM(Turbo Trellis Coded Modulation) decoding which will be used fur power-limited environment. As the decoding approaches the performance limit of a given turbo code, any further iteration results in very little improvement. Therefore, it is important to devise an efficient criterion to stop the iteration process and prevent unnecessary computations and decoding delay. This paper presents an efficient algorithm for turbo TCM decoding that can greatly reduce the delay and iteration number. The proposed method use adaptive iteration number according to the criterion using the extrinsic information variance parameter in turbo TCM decoding process. The simulation results show that the proposed technique effectively can reduce the decoding delay and computation with very little performance degradation.

• Journal of Digital Contents Society
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• v.5 no.3
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• pp.199-204
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• 2004

A problem of many fractal image compression algorithms providing good quality at low bit rate is that the decoding time rests on an iterative procedure whose complexity is imag-dependent. This paper proposes an iterative-free fractal image decoding algorithm to reduce the decoding time. In the proposed method, under the encoder previously with the same codebook image as an initial image to be used at the decoder, the fractal coefficients are obtained through calculating the similarity between the codebook image and an input image to be encoded. As the decoding time could be remarkably reduced. For verifying the validity and universality of proposed method, We evaluated and analyzed the performance of decoding time and image quality for a number of still images and a moving picture with different distributed characteristics.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.12
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• pp.79-90
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• 2000

In this paper, a high-performance floating point unit, which is suitable for high-performance superscalar microprocessors and supports IEEE 754 standard, is designed. Floating-point arithmetic unit (AU) supports all denormalized number processing through hardware, while eliminating the additional delay time due to the denormalized number processing by proposing the proposed gradual underflow prediction (GUP) scheme. Contrary to the existing fixed-radix implementations, floating-point divide/square root unit adopts a new architecture which determines variable length quotient bits per cycle. The new architecture is superior to the SRT implementations in terms of performance and design complexity. Moreover, sophisticated exception prediction scheme enables precise exception to be implemented with ease on various superscalar microprocessors, and removes the stall cycles in division. Designed floating-point AU and divide/square root unit are integrated with and instruction decoder, register file, memory model and multiplier to form a floating-point unit, and its function and performance is verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.548-554
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• 2003

Interoperability is ole the most important factors for a successful integration of the speech network. To accomplish communication between endpoints employing different speech coders, decoder and encoder of each endpoint coder should be placed in tandem. However, tandem coder often produces problems such as poor speech quality, high computational load, and additional transmission delay. In this paper, we propose an efficient transcoding algorithm that can provide interoperability to the networks employing ITU-T G.723.1［1］and TIA IS-127 EVRC［2］speech coders. The proposed transcoding algorithm is composed of four parts: LSP conversion, open-loop pitch conversion, fast adaptive codebook search, and fast fixed codebook search. Subjective and objective quality evaluation confirmed that the speech quality produced by the proposed transcoding algorithm was equivalent to, or better than the tandem coding, while it had shorter processing delay and less computational complexity, which is certified implementing on TMS320C62x.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.70-77
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• 2017

Bandwidth extension is a technique to improve speech quality, intelligibility and naturalness, extending from the 300 ~ 3,400 Hz narrowband speech to the 50 ~ 7,000 Hz wideband speech. In this paper, an Artificial Bandwidth Extension (ABE) module embedded in the Opus audio decoder is designed using the information of narrowband speech to reduce the computational complexity of LPC (Linear Prediction Coding) and LSF (Line Spectral Frequencies) analysis and the algorithm delay of the ABE module. We proposed a spectral envelope extension method using DBN (Deep Belief Network), one of deep learning techniques, and the proposed scheme produces better extended spectrum than the traditional codebook mapping method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.64-73
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• 2008

In this paper, we propose a novel objective video quality assessment method from encoded H.264/AVC.. Conventional algorithms have been proposed to assess video/image quality with image frames reconstructed in a decoder side. On the other hand, the proposed assessment is conducted with the syntax elements which are embedded in a bitstream. The proposed BS-based algorithm makes use of the statistics of boundary strength(BS) which are employed in the H.264/AVC. The proposed algorithm has lower computational complexity than conventional methods, EPSNR and Blockiness, resulting that it can accomplish assessment of the video quality in real time. Furthermore, the accuracy of the proposed video quality assessment is about 32% and 65% better than several conventional algorithms.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1
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• pp.56-61
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• 2001

This paper presents the real-time implementation of an AMR (Adaptive Multi Rate) vocoder which is included in the asynchronous International Mobile Telecommunication (IMT)-2000 mobile ASIC. The implemented AMR vocoder is a multi-rate coder with 8 modes operating at bit rates from 12.2kbps down to 4.75kbps. Not only the encoder and the decoder as basic functions of the vocoder are implemented, but VAD (Voice Activity Detection), SCR (Source Controlled Rate) operation and frame structuring blocks for the system interface are also implemented in this vocoder. The DSP for AMR vocoder implementation is a 16bit fixed-point DSP which is based on the TeakLite core and consists of memory block, serial interface block, register files for the parallel interface with CPU, and interrupt control logic. Through the implementation, we reduce the maximum operating complexity to 24MIPS by efficiently managing the memory structure. The AMR vocoder is verified throughout all the test vectors provided by 3GPP, and stable operation in the real-time testing board is also proved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.157-162
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• 2016

The performance of turbo-like codes highly depends on their frame size and thus, the bit error rate performance of turbo-like codes can be improved by increasing the frame size. Unfortunately, increasing the frame size of channel codes induces some drawbacks such as the increase of not only encoding and decoding complexity but also transmission and decoding latencies. On the other hand, a faster than Nyquist (FTN) transmission causes intentional inter-symbol interference (ISI) and thus, induces some correlation among the transmission symbols. In this paper, we propose an FTN transmission with multiple channel codes. By exploiting the correlation among the modulated symbols, multiple code frames can be regarded as a code frame with a lager frame size. Due to the inherent parallel encoding scheme of proposed scheme, parallel decoding can be easily implemented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1095-1102
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• 2016

In this paper, we propose a decoding scheme that can apply to a three dimensional turbo product code(TPC) with a single parity check code(SPC). In general, SPC is used an axis with shortest code length in order to maximize a code rate of the TPC. However, SPC does not have any error correcting capability, therefore, the error correcting capability of the three-dimensional TPC results in little improvement in comparison with the two-dimensional TPC. We propose two schemes to improve performance of three dimensional TPC decoder. One is $min^*$-sum algorithm that has advantages for low complexity implementation compared to Chase-Pyndiah algorithm. The other is a modified serial iterative decoding scheme for high performance. In addition, the simulation results for the proposed scheme are shown and compared with the conventional scheme. Finally, we introduce some practical considerations for hardware implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.