• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.224-237
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• 2015

In this paper, we introduce the fast decoding method with the SIMD (Single Instruction Multiple Data) instructions for HEVC RExt (High Efficiency Video Coding Range Extensions). Several tools of HEVC RExt such as intra prediction, interpolation, inverse-quantization, inverse-transform, and clipping modules can be classified as the proper modules for applying the SIMD instructions. In consideration of bit-depth increasement of RExt, intra prediction, interpolation, inverse-quantization, inverse-transform, and clipping modules are accelerated by SSE (Streaming SIMD Extension) instructions. In addition, we propose effective implementations for interpolation filter, inverse-quantization, and clipping modules by utilizing a set of AVX2 (Advanced Vector eXtension 2) instructions that can use 256 bits register. The evaluation of the proposed methods were performed on the private HEVC RExt decoder developed based on HM 16.0. The experimental results show that the developed RExt decoder reduces 12% average decoding time, compared with the conventional sequential method.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.11-19
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• 2014

In this paper, a low complexity parallel sphere decoder algorithm is proposed for high-order MIMO system. It reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by static tree-pruning and dynamic tree-pruning using scalable node operators, and offers near-maximum likelihood decoding performance. Moreover, it also offers hardware-friendly node operation algorithm through fixing the variable computational complexity caused by the sequential nature of the conventional SD algorithm. A Monte Carlo simulation shows our proposed algorithm decreases the average number of expanded nodes by 55% with only 6.3% increase of the normalized decoding time compared to a full parallelized FSD algorithm for high-order MIMO communication system with 16 QAM modulation.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.207-216
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• 2023

For decoding-compatible parallel Deflate algorithm, this study proposed a new method of the control header being made in such a way that essential information for parallel compression and decompression are stored in the Disposed Bit Area (DBA) of the non-compression block and being inserted into the compressed blocks. Through this, parallel compression and decompression are possible while maintaining perfect compatibility with the existing decoder. After applying this method, the compression time was reduced by up to 71.2% compared to the sequential processing method, and the parallel decompression time was reduced by up to 65.7%. In particular, it is well known that parallel decompression is impossible due to the structural limitations of the Deflate algorithm. However, the decoder equipped with the proposed method enables high-speed parallel decompression at the algorithm level and maintains compatibility, so that parallelly compressed data can be decoded normally by existing decoder programs.

• Korean Journal of Optics and Photonics
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• v.19 no.3
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• pp.237-241
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• 2008

An all-optical encryption system built on the basis of electrical logic circuit design principles is proposed, using semiconductor optical amplifier (SOA) exclusive or (XOR) logic gates. Numerical techniques (steady-state and dynamic) were employed in a sequential manner to optimize the system parameters, speeding up the overall design process. The results from both numerical and experimental testbeds show that the encoding/decoding of the optical signal can be achieved at a 10 Gbps data rate with a conventional SOA cascade without serious degradation in the data quality.

• ETRI Journal
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• v.45 no.5
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• pp.822-835
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• 2023

Recent semantic segmentation frameworks usually combine low-level and high-level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder-decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large-scale, real-world datasets to demonstrate the effectiveness of our approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7A
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• pp.535-544
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• 2012

Multi-Input Multi-Output (MIMO) transmission is now considered as one of essential techniques enabling high rate data transmissions in wireless communication systems. In addition, severe channel impairments in wireless systems should be compensated by using highly efficient forward error correction (FEC) codes. Turbo codes or low density parity check (LDPC) codes, using iterative decoding with soft decision detection information (SDDI), are the most common examples. The excellent performance of these codes should be conditioned on accurate estimation of SDDI from the MIMO detection process. In this paper, we propose a soft MIMO detection scheme using QR decomposition of channel matrices as an efficient means to provide accurate SDDI to the iterative decoder. The proposed method employed a two sequential soft MIMO detection process in order to reduce computational complexity. Compared to the soft ZF method calculating the direct inverse of the channel matrix, the complexity of the proposed method can be further reduced as the number of antennas is increased, without any performance degradation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10C
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• pp.1469-1483
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• 2004

We propose a packet scheduling algorithms for streaming media. We assume that the receiver periodically reports back the channel throughput. From the original video data, the importance level of a video packet is determined by its relative position within its group of pictures, taking into account the motion-texture discrimination and temporal scalability. Thus, we generate a number of nested substreams. Using feedback information from the receiver and statistical characteristics of the video, we model the streaming system as a queueing system, compute the run-time decoding failure probability of a Same in each substream based on effective bandwidth approach, and determine the optimum substream to be sent at that moment in time. Since the optimum substream is updated periodically, the resulting sending order is different from the original playback order. From experiments with real video data, we show that our proposed scheduling scheme outperforms the conventional sequential sending scheme.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.258-268
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• 2021

Point cloud content is immersive content that represents real-world objects with three-dimensional (3D) points. In the process of acquiring point cloud data or encoding and decoding point cloud data, the resolution of point cloud content could be degraded. In this paper, we propose a method of progressively enhancing the resolution of sequential point cloud contents through inter-frame registration. To register a point cloud, the iterative closest point (ICP) algorithm is commonly used. Existing ICP algorithms can transform rigid bodies, but there is a disadvantage that transformation is not possible for non-rigid bodies having motion vectors in different directions locally, such as point cloud content. We overcome the limitations of the existing ICP-based method by registering regions with motion vectors in different directions locally between the point cloud content of the current frame and the previous frame. In this manner, the resolution of the point cloud content with geometric movement is enhanced through the process of registering points between frames. We provide four different point cloud content that has been enhanced with our method in the experiment.

• Journal of Yeungnam Medical Science
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• v.18 no.2
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• pp.253-266
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• 2001

Background: The causes of ADHD(attention deficit hyperactivity disorder) are various, so it is impossible to understand the whole characteristics of ADHD, only with simple intelligence testing scales. We compared cognitive characteristics of ADHD group with normal controls with Korean Kaufman Assessment Battery for Children(K-ABC). It is well known to evaluate neuropsychological and cognitive aspects of the children. Materials and Methods: Age and sex matched 40 ADHD patients and 40 normal controls tested with the K-ABC. Each subscales compared between pre-treatment patients and controls, pre-treatment and post-treatment in patient group, post-treatment patients and controls. Results: Significant differences are observed in sequential processing, simultaneous processing, cognitive processing and achievement between pre-treatment patients and controls, and in gestalt closure between pre-treatment and post-treatment patients group. But there are no significant differences between pre-treatment patients and controls in gestalt closure and reading/decoding. Conclusions: Methylphenidate improved the scores of simultaneous scale, which means improvement of executive functions such as divided attention, analysis and organization. Methylphenidate also reduced distractibility.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.579-586
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• 2021

Time series forecasting refers to predicting future time information based on past time information. Accurately predicting future information is crucial because it is used for establishing strategies or making policy decisions in various fields. Recently, a transformer model has been mainly studied for a time series prediction model. However, the existing transformer model has a limitation in that it has an auto-regressive structure in which the output result is input again when the prediction sequence is output. This limitation causes a problem in that accuracy is lowered when predicting a distant time point. This paper proposes a sequential decoding model focusing on the style transformation technique to handle these problems and make more precise time series forecasting. The proposed model has a structure in which the contents of past data are extracted from the transformer-encoder and reflected in the style-based decoder to generate the predictive sequence. Unlike the decoder structure of the conventional auto-regressive transformer, this structure has the advantage of being able to more accurately predict information from a distant view because the prediction sequence is output all at once. As a result of conducting a prediction experiment with various time series datasets with different data characteristics, it was shown that the model presented in this paper has better prediction accuracy than other existing time series prediction models.