• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.514-535
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• 2019

Information-centric networks operate under the assumption that all network components have built-in caching capabilities. Integrating the caching strategies of information centric networking (ICN) with wireless virtualization improves the gain of virtual infrastructure content caching. In this paper, we propose a framework for software-defined information centric virtualized wireless device-to-device (D2D) networks. Enabling D2D communications in virtualized ICN increases the spectral efficiency due to reuse and proximity gains while the software-defined network (SDN) as a platform also simplifies the computational overhead. In this framework, we propose a joint virtual resource and cache allocation solution for latency-sensitive applications in the next-generation cellular networks. As the formulated problem is NP-hard, we design low-complexity heuristic algorithms which are intuitive and efficient. In our proposed framework, different services can share a pool of infrastructure items. We evaluate our proposed framework and algorithm through extensive simulations. The results demonstrate significant improvements in terms of visiting latency, end user QoE, InP resource utilization and MVNO utility gain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.37-43
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• 2023

Compressed still image or video bitstreams require protection from channel errors in a wireless channel. Embedded Zerotree Coding(EZW), SPIHT could have provided unprecedented high performance in image compression with low complexity. If bit error is generated by dint of wireless channel transmission problem, the loss of synchronization on between encoder and decoder causes serious performance degradation. But wavelet zerotree coding algorithms are producing variable-length codewords, extremely sensitive to bit errors. The idea is to partition the lifting coefficients. A many partition of lifting transform coefficients distributes channel error from wireless channel to each partition. Therefore synchronization problem that caused quality deterioration in still image and video stream was improved.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.115-121
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• 2006

In this paper, we proposed about data error check and correction on channel transmission in the communication system. LDPC codes are used for minimizing channel errors by modeling AWGN Channel as a VDSL system. Because LDPC Codes use low density parity bit, mathematical complexity is low and relating processing time becomes shorten. Also the performance of LDPC code is better than that of turbo code in long code word on iterative decoding algorithm. This algorithm is better than conventional algorithms to correct errors, the proposed algorithm assigns weights for errors concerning parity bits. The proposed weighted Bit-flipping algorithm is better than the conventional Bit-flipping algorithm and we are recognized improve gain rate of 1 dB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4389-4411
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• 2018

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.54-63
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• 2011

This paper proposes a fast learning-based interpolation algorithm to up-scale an input low-resolution image into a high-resolution image. In conventional learning-based super-resolution, a certain relationship between low-resolution and high-resolution images is learned from various training images and a specific high frequency synthesis information is derived. And then, an arbitrary low resolution image can be super-resolved using the high frequency synthesis information. However, such super-resolution algorithms require heavy memory space to store huge synthesis information as well as significant computation due to two-dimensional matching process. In order to mitigate this problem, this paper presents one-dimensional patch-based learning and synthesis. So, we can noticeably reduce memory cost and computational complexity. Simulation results show that the proposed algorithm provides higher PSNR and SSIM of about 0.7dB and 0.01 on average, respectively than conventional bicubic interpolation algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.202-207
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• 2013

RFID technology is applied to port logistics applications since it monitors objects wirelessly without line of sight and constructs ubiquitous system with low cost. Changes of stock status in the warehouse environment make the technology more important for managing such frequent storing and un-storing. Although the RFID has beneficial characteristics of low cost and low complexity, simultaneous responses of RFID tags cause tag identification collision due to absence of elaborate medium access control scheme. Several algorithms have been proposed to overcome the tag collision problem. However, it is difficult to adopt the methods to the logistics systems that has varying loads since there was no consideration on RFID tag's mobility. Therefore, we propose an efficient RFID tag anti-collision protocol, and simulation results demonstrate performance improvement by using the proposed scheme.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.1-10
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• 2019

Since big-data text mining extracts many features and data, clustering and classification can result in high computational complexity and low reliability of the analysis results. In particular, a term document matrix obtained through text mining represents term-document features, but produces a sparse matrix. We designed an advanced genetic algorithm (GA) to extract features in text mining for detection model. Term frequency inverse document frequency (TF-IDF) is used to reflect the document-term relationships in feature extraction. Through a repetitive process, a predetermined number of features are selected. And, we used the sparsity score to improve the performance of detection model. If a spam mail data set has the high sparsity, detection model have low performance and is difficult to search the optimization detection model. In addition, we find a low sparsity model that have also high TF-IDF score by using s(F) where the numerator in fitness function. We also verified its performance by applying the proposed algorithm to text classification. As a result, we have found that our algorithm shows higher performance (speed and accuracy) in attack mail classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1349-1354
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• 2017

In this paper, we propose a fast hand-gesture recognition algorithm for embedded system. Existing hand-gesture recognition algorithm has a difficulty to use in a low performance system such as embedded systems and mobile devices because of high computational complexity of contour tracing method that extracts all points of hand contour. Instead of using algorithms based on contour tracing, the proposed algorithm uses concentric-circle tracing method to estimate the abstracted contour of fingers, then classify hand-gestures by extracting features. The proposed algorithm has an average recognition rate of 95% and an average execution time of 1.29ms, which shows a maximum performance improvement of 44% compared with algorithm using the existing contour tracing method. It is confirmed that the algorithm can be used in a low performance system such as embedded systems and mobile devices.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.201-208
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• 2018

High Efficiency Video Coding range extensions (HEVC RExt) is a kind of extension model of HEVC. HEVC RExt was specially designed for dealing the high quality images. HEVC RExt is very essential for studio editing which handle the very high quality and various type of images. There are some problems to dealing these massive data in studio editing. One of the most important procedure is re-encoding and decoding procedure during the editing. Various codecs are widely used for studio data editing. But most of the codecs have common problems to dealing the massive data in studio editing. First, the re-encoding and decoding processes are frequently occurred during the studio data editing and it brings enormous time-consuming and video quality loss. This paper, we suggest new video coding structure for the efficient studio video editing. The coding structure which is called "ultra-low delay (ULD)". It has the very simple and low-delayed referencing structure. To simplify the referencing structure, we can minimize the number of the frames which need decoding and re-encoding process. It also prevents the quality degradation caused by the frequent re-encoding. Various fast coding algorithms are also proposed for efficient editing such as tool-level optimization, multi-serve based distributed coding and SIMD (Single instruction, multiple data) based parallel processing. It can reduce the enormous computational complexity during the editing procedure. The proposed method shows 9500 times faster coding speed with negligible loss of quality. The proposed method also shows better coding gain compare to "intra only" structure. We can confirm that the proposed method can solve the existing problems of the studio video editing efficiently.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.70-77
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• 2017

Recently, Internet of Things (IoT) and deep learning techniques have affected video surveillance systems in various ways. The surveillance features that perform detection, tracking, and classification of specific objects in Closed Circuit Television (CCTV) video are becoming more intelligent. This paper presents real-time algorithm that can run in a PC environment using only a low power CPU. Traditional tracking algorithms combine background modeling using the Gaussian Mixture Model (GMM), Hungarian algorithm, and a Kalman filter; they have relatively low complexity but high detection errors. To supplement this, deep learning technology was used, which can be trained from a large amounts of data. In particular, an SRGB(Sequential RGB)-3 Layer CNN was used on tracked objects to emphasize the features of moving people. Performance evaluation comparing the proposed algorithm with existing ones using HOG and SVM showed move-in and move-out error rate reductions by 7.6 % and 9.0 %, respectively.