• Journal of Communications and Networks
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• v.13 no.5
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• pp.472-480
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• 2011

In this paper, we investigate two new candidate transmission schemes, non-orthogonal frequency reuse (NOFR) and beam-hopping (BH). They operate in different domains (frequency and time/space, respectively), and we want to know which domain shows overall best performance. We propose a novel formulation of the signal-to-interference plus noise ratio (SINR) which allows us to prove the frequency/time duality of these schemes. Further, we propose two novel capacity optimization approaches assuming per-beam SINR constraints in order to use the satellite resources (e.g., power and bandwidth) more efficiently. Moreover, we develop a general methodology to include technological constraints due to realistic implementations, and obtain the main factors that prevent the two technologies dual of each other in practice, and formulate the technological gap between them. The Shannon capacity (upper bound) and current state-of-the-art coding and modulations are analyzed in order to quantify the gap and to evaluate the performance of the two candidate schemes. Simulation results show significant improvements in terms of power gain, spectral efficiency and traffic matching ratio when comparing with conventional systems, which are designed based on uniform bandwidth and power allocation. The results also show that BH system turns out to show a less complex design and performs better than NOFR system specially for non-real time services.

• Journal of Broadcast Engineering
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• v.13 no.2
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• pp.172-178
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• 2008

Orthogonal Frequency Division Multiplexing (OFDM) is one of the most promising technologies for high data rate wireless communications. OFDM has been adopted in wireless standards such as digital audio/video broadcasting. The combination of OFDM and cooperative diversity techniques can provide the diversity gain and/or increased capacity. In this paper, the cooperative coding using cyclic delay diversity (CDD) for multiuser OFDM systems is introduced. To improve the beneficial effects of relays's cooperation, CDD is adopted in cooperative transmission of relays. Simulation results show the bit error rate (BER) for various consideration. The proposed scheme provides improved performance compared to delay.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.34-41
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• 2020

Nowadays, the advanced smart convergences of the artificial intelligence (AI) and the internet of things (IoT) have been more and more important, in the fifth generation (5G) and beyond 5G (B5G) mobile communication. In 5G and B5G mobile networks, non-orthogonal multiple access (NOMA) has been extensively investigated as one of the most promising multiple access (MA) technologies. In this paper, we investigate the achievable data rate for the asymmetric binary pulse amplitude modulation (2PAM), in non-orthogonal multiple access (NOMA). First, we derive the closed-form expression for the achievable data rate of the asymmetric 2PAM NOMA. Then it is shown that the achievable data rate of the asymmetric 2PAM NOMA reduces for the stronger channel user over the entire range of power allocation, whereas the achievable data rate of the asymmetric 2PAM NOMA increases for the weaker channel user improves over the power allocation range less than 50%. We also show that the sum rate of the asymmetric 2PAM NOMA is larger than that of the conventional standard 2PAM NOMA, over the power allocation range larger than 25%. In result, the asymmetric 2PAM could be a promising modulation scheme for NOMA of 5G systems, with the proper power allocation.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.262-270
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• 2022

In the past two decades, there has been an increasing interest in project knowledge management, as knowledge is a crucial resource for project management success. Knowledge capture and sharing are two effective project management practices. Capturing and sharing project knowledge has become more efficient due to technological advances. Nevertheless, present technologies face several technical, functional, and usage obstacles and constraints. Thus, Blockchain technology might provide promising answers, yet, there is still a dearth of understanding regarding the technology's proper and practical application. Consequently, the goal of this study was to fill the gap in the literature about the adoption of Blockchain technology and to investigate the project stakeholders' acceptance and willingness to utilize the technology for capturing and sharing project knowledge. Due to this inquiry's exploratory and inductive characteristics, qualitative research methodology was used, namely the Grounded Theory research approach. Accordingly, eighteen in-depth, semi-structured interviews were conducted to collect the data. Concurrent data collection and analysis were undertaken, with findings emerging after three coding steps. Four influencing factors and one moderating factor were identified as affecting users' acceptance of Blockchain technology for capturing and sharing project knowledge. Consequently, the results of the study aimed to fill a gap in the existing literature by undertaking a comprehensive analysis of the unrealized potential of Blockchain technology to improve knowledge capture and sharing in the project management environment.

• Educational Technology International
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• v.19 no.2
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• pp.199-228
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• 2018

The main purposes of this study were (a) to analyze the research trend of educational use of tangible technology, (b) to identify tangible learning mechanisms, and potential benefits of learning with tangible technology, and (c) to provide references and future research directions. We conducted a systematic literature review to search for academic papers published in recent five years (from 2013 to 2017) in the major databases. Forty papers were coded and analyzed by the established coding framework in four dimensions: (a) basic publication information, (b) learning context, (c) learning mechanism, and (d) learning benefits. Overall, the results show that tangible technology has been used more for young learners in the kindergarten and primary school contexts mainly for science learning, to achieve both cognitive and affective learning outcomes, by coupling tangible objects with tabletops and desktop computers. From the synthesis of the review findings, this study suggests that the affordances of tangible technology useful for learning include embodied interaction, physical manipulations, and the physical-digital representational mapping. With such technical affordances, tangible technologies have the great potential in three particular areas in education: (a) learning spatial relationships, (b) making the invisible visible, and (c) reinforcing abstract concepts through the correspondence of representations. In conclusion, we suggest some areas for future research endeavors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1708-1734
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• 2022

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.

• Journal of Platform Technology
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• v.12 no.1
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• pp.32-43
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• 2024

In recent years, a platform providing a Visual Programming development environment capable of 3D editing and interaction editing in an In-VR environment to quickly prototype VR/AR contents for education of VR and AR for general users and children. In the past, VR contents were mostly viewed by users. However, thanks to the rapid development of recent computing technologies, VR contents interacting with users have emerged as a device capable of tracking user behavior in a small size It was able to appear. In addition, because VR is extended to AR and MR, it can be used in all three virtual environments and requires efficient user interface(UI). In this paper, we propose UI based on eye tracking. Eye-tracking-based UI not only reduces the amount of time the user directly manipulates the controller, but also dramatically lowers the time spent on simple operations, while reducing the need for a dedicated controller by allowing multiple types of controllers to be used in combination. The proposed platform can easily create a prototype of their intended VR/AR App(or content) even for users(beginners) who do not have a certain level of knowledge and experience in 3D graphics and software coding, and share it with others. Therefore, this paper proposes a method to use VAL more effectively in a 5G environment.

• Journal of Engineering Education Research
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• v.8 no.1
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• pp.71-83
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• 2005

This paper presents an internet-based self-learning educational system which can be enhancing efficiency in the learning process of Java language. The proposed self-learning educational system is called Java Web Player(JWP), which is a Java application program and is executable through Java Web Start technologies. Also, three important sequential learning processes : concept learning process, programming practice process and assessment process are integrated in the proposed JWP using Java Web Start technologies. This JWP enables the learners to achieve efficient and interesting self-learning since the learning process is designed to enhance the multimedia capabilities on the basis of various educational technologies. Furthermore, internet-based on-line voice presentation and its related texts together with moving images are synchronized for efficient language learning process. Also, a simple and useful Java compiler is included in the JWP for providing language practice environment such as coding, editing, executing and debugging Java source files. Finally, repeated practice can make the learners to understand easily the key concepts of Java language. Simple multiple choices are given suddenly to the learners while they are studying through the JWP and the test results are displayed on the message box. This assessment process is very essential to increase the learner's academic capability.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.31-43
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• 2022

Traditional video compression has developed so far based on hybrid compression methods through motion prediction, residual coding, and quantization. With the rapid development of technology through artificial neural networks in recent years, research on image compression and video compression based on artificial neural networks is also progressing rapidly, showing competitiveness compared to the performance of traditional video compression codecs. In this paper, a new method capable of improving the performance of such an artificial neural network-based video compression model is presented. Basically, we take the rate-distortion optimization method using the auto-encoder and entropy model adopted by the existing learned video compression model and shifts some components of the latent information that are difficult for entropy model to estimate when transmitting compressed latent representation to the decoder side from the encoder side, and finally compensates the distortion of lost information. In this way, the existing neural network based video compression framework, MFVC (Motion Free Video Compression) is improved and the BDBR (Bjøntegaard Delta-Rate) calculated based on H.264 is nearly twice the amount of bits (-27%) of MFVC (-14%). The proposed method has the advantage of being widely applicable to neural network based image or video compression technologies, not only to MFVC, but also to models using latent information and entropy model.