• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.777-782
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• 2020

As the demand for the talented with computing thinking and programming skills increases the importance of coding education is increasing. Although various coding instructor training programs have been implemented nationwide, little research has been conducted analyzing the current status and contents of coding instructor training programs. Therefore, this thesis presents the design, development process and managing strategies of the 'Coding Instructor Training Courses for 4C Talent Development'. The training program consists of introductory courses and practical coding courses. In the introductory courses, learners acquires the basic knowledge required of coding instructors, and then proceeds to the practical courses to systematically learn the pedagogical knowledge and skills required to educate learners from kindergarten through high school. The case study introduced in this paper is expected to provide useful information to the educators planning and managing the coding instructor training program in the future.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.563-572
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• 2020

In this study, through a total of 73 lectures and practice for 14 days, preliminary coding instructors developed teaching materials for elementary and junior high school students and verified the effectiveness of the coding instructor training course. The coding instructor training course was hosted by the "◯◯ Creative Economy Innovation Center" for 25 preliminary coding instructors, and was conducted at the "◯◯ University", and 15 elementary and junior high school students who participated in the class were openly recruited. The teaching materials were developed according to the procedure of the ADDIE model based on the results of the pre-requirement analysis conducted with 20 incumbent elementary school teachers majoring in computer education. As a result of running a training course for coding instructors focusing on classroom practice, it was found that the creativity of pre-coding instructors improved.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.529-535
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• 2000

In this paper, we propose a new method of designing the vector quantizer which is robustness to coding results and independent of statistical characteristics of an input image in wavelet transformed image coding processes. The most critical drawback of a conventional vector quantizer is the degradation of coding capability resulted from the discordance between quantizer objective image and statistical characteristics of training sequence which is for generating representing vector. In order to resolve the problem of conventional methods, we use independent random-variables and pseudo image to which image correlation and edge component were added, as a training sequence for generating representing vector. We have done a computer simulation in order to compare coding capability between a vector quantizer designed by the proposed method and one with the conventional method using real image as same as that is objective to coding of training sequence used in codebook generation. The results show the superiority of the proposed vector quantizer method at the aspect of coding capability compared to conventional one. They also clarify the problems of conventional methods.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.332-340
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• 2022

Recently, with the development of deep learning and artificial neural network technologies, research on the application of neural network has been actively conducted in the field of video coding. In particular, deep learning-based intra prediction is being studied as a way to overcome the performance limitations of the existing intra prediction techniques. This paper presents a method of context-adaptive neural network-based intra prediction model training and its coding performance analysis. In other words, in this paper, we implement and train a known intra prediction model based on convolutional neural network (CNN) that predicts a current block using contextual information from reference blocks. Then, we integrate the trained model into HM16.19 as an additional intra prediction mode and evaluate the coding performance of the trained model. Experimental results show that the trained model gives 0.28% BD-rate bit saving over HEVC in All Intra (AI) coding mode. In addition, the coding performance change of training considering block partition is also presented.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.1-8
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• 2016

Coding education is an effective convergence type educational tool. While solving problems and designing programs, students can enhance problem solving ability, logical reasoning ability and creative thinking. Researches on coding education are done primarily for elementary school and middle school students. However, researches on college students are lacking. Today, educating college students about coding is in dire need. Although there are efforts to promote the importance of coding education and make it requirements. People find it difficult to find ways to provide training. There is a need for researches on coding as universal education. Therefore, this research proposed educational training using app inventor based on flipped running in order to effectively promote coding education. This study conducted the survey and the personal interview to measure the effectiveness of coding education. It is hoped that, through coding education, students who do not major in coding could combined their knowledge of their major with coding to improve their problem solving ability to solve various problems based on computing knowledge and approach.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.243-247
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• 2005

We propose an efficient block least-mean-square (BLMS) adaptive algorithm, in conjunction with error control coding, for direct-sequence code division multiple access (DS-CDMA) systems. The proposed adaptive receiver incorporates decision feedback detection and channel encoding in order to improve the performance of the standard LMS algorithm in convolutionally coded systems. The BLMS algorithm involves two modes of operation: (i) The training mode where an uncoded training sequence is used for initial filter tap-weights adaptation, and (ii) the decision-directed where the filter weights are adapted, using the BLMS algorithm, after decoding/encoding operation. It is shown that the proposed adaptive receiver structure is able to compensate for the signal-to­noise ratio (SNR) loss incurred due to the switching from uncoded training mode to coded decision-directed mode. Our results show that by using the proposed adaptive receiver (with decision feed­back block adaptation) one can achieve a much better performance than both the coded LMS with no decision feedback employed. The convergence behavior of the proposed BLMS receiver is simulated and compared to the standard LMS with and without channel coding. We also examine the steady-state bit-error rate (BER) performance of the proposed adaptive BLMS and standard LMS, both with convolutional coding, where we show that the former is more superior than the latter especially at large SNRs ($SNR\;\geq\;9\;dB$).

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.210-213
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• 1994

In this paper, we present a combination of trellis coded vector quantization and code-excited linear prediction coding, termed trellis excitation coding, for an efficient 4.8 kbps speech coding system. A training sequence-based algorithm is developed for designing an otimized codebook subject to the TEC structure. Also, we discuss the trellis symbol release rules that avoid excessive encoding delay. Finally, simulation results for the TEC coder are given at bit rate of 4.8 kbps.

• Journal of Biomedical Engineering Research
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• v.15 no.1
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• pp.83-88
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• 1994

The transform trellis code is an optimal source code as a block size and the constraint length of a shift register go to infinite for stationary Gaussian sources with the squared-error distortion measure. However to implement this code, we have to choose the finite block size and constraint length. Moreover real-world sources are inherently non stationary. To overcome these difficulties, we developed a training algorithm for the transform trellis code. The trained transform trellis code which uses the same rates to each block led to a variation in the resulting distortion from one block to another. To alleviate this non-uniformity in the encoded image, we constructed clusters from the variance of the training data and assigned different rates for each cluster.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3286-3300
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• 2016

High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.

• ETRI Journal
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• v.42 no.3
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• pp.341-350
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• 2020

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple-input multiple-output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space-time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple-input multiple-output (MIMO) data transmission technique in the WSNs. The DSC-MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC-MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single-input single-output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.