• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.17-27
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• 2012

Broadcast-based data dissemination has become a widely accepted approach of communication in the mobile computing environment. However, with a large set of data items, the expected delay of receiving a desired data increases due to the sequential nature of the broadcast channel. This paper explores the issue of designing proper data allocation on multiple broadcast channels to reduce this wait time, and proposes a new data allocation scheme named near optimal data allocation(NODA). The proposed scheme first partitions all data items in K group based on the theoretical lower-bound of the average expected delay to determine data items which each broadcast channel has to broadcast. Then, NODA further partitions each group of data items in B groups using extended dynamic programming algorithm to broadcast data items allocated on the same broadcast channel in different frequencies. The proposed scheme is capable of minimizing the average expected delay time since it can broadcast data items allocated on the same channel reflecting their popularities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.63-72
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• 2011

Broadcast-based data dissemination has become a widely accepted approach of communication in the mobile computing environment. However, with a large set of data items, the expected delay of receiving a desired data increases due to the sequential nature of the broadcast channel. With the objective of minimizing this wait time, this paper explores the problem of data broadcast over multiple channels. In traditional approaches, data items are partitioned based on their access probabilities and allocated on multiple channels, assuming flat data scheduling per channel. If the data items allocated on the same channel are broadcast in different frequencies based on their access probabilities, the performance will be enhanced further. In this respect, this paper proposes a new broadcast scheduling scheme named two level dynamic programming(TLDP) which can reflect a variation of access probabilities among data items allocated on the same channel.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.487-491
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• 2009

The inverse halftoning is processing to restore the image made binary to former step image. There are smoothing and gaussian filtering in the technique so far. However, there are still a lot of insufficient points in past inverse halftoning. The removal of the noise and the edge enhansment are closely related in inverse halftoning. It is difficult to do both the noise rednctiom and the edge enhansment in high accuracy at the same time in the technique so far. The technique that can achieve both the removal of the noise and the emphasis of Edge at the same time is expected as future tasks. Then, it was tried to apply the Kalmann filtering to inverse halftoning. In the actual experiment, the effectiveness of the application of the Kalmann filtering to inverse halftoning comparing it with the technique so far was shown.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.122-127
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• 1998

In this paper, we focus on the information retrieval of human individuals who are recorded on the video database. Our purpose is to index persons by their faces or voice and to retrieve their existing time sections on the video data. The database system can track as well as extract a face or voice of a certain person and construct a model of the individual person in self-organization mode. If he appears again at different time, the system can put the mark of the same person to the associated frames. In this way, the same person can be retrieved even if the system does not know his exact name. As the face and speaker modeling, a subspace method is employed to improve the indexing accuracy.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.51-58
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• 2019

In this paper, we propose to characteristics of mobile phone dance live broadcast platform and the second-generation technology acceptance model TAM2, this paper established the user acceptance model of mobile phone dance live broadcast platform, aiming to study the influencing factors of users' acceptance on mobile phone dance live broadcast platform. Based on the empirical analysis of user survey, the model is validated, and the relationship between variables in the model is clarified. It is also confirmed that human-computer interaction, scene interaction, relationship interaction and other factors will affect the user's acceptance on mobile phone dance live broadcast platform. At the same time, based on the relationship among variables obtained in the research, this paper tries to analyze how the variables affect each other based on the actual practice of mobile phone dance live broadcast platform. In addition, the video design strategy and marketing strategy for further development of mobile phone dance live broadcast platform are given to help the platform and dance creators to carry out better promotion on the mobile side. In the end, this paper summarizes the shortcomings of this study and points out further research directions in the future, providing a reference for researchers in the field of mobile phone dance live broadcast platform acceptance.

• Phonetics and Speech Sciences
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• v.11 no.4
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• pp.89-96
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• 2019

In this paper, we propose a deep learning architecture that can effectively detect speech segmentation in broadcast contents. We also propose a multi-scale time-dilated layer for learning the temporal changes of feature vectors. We implement several comparison models to verify the performance of proposed model and calculated the frame-by-frame F-score, precision, and recall. Both the proposed model and the comparison model are trained with the same training data, and we train the model using 32 hours of Korean broadcast data which is composed of various genres (drama, news, documentary, and so on). Our proposed model shows the best performance with F-score 91.7% in Korean broadcast data. The British and Spanish broadcast data also show the highest performance with F-score 87.9% and 92.6%. As a result, our proposed model can contribute to the improvement of performance of speech detection by learning the temporal changes of the feature vectors.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.75-81
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• 1997

A new DTPP (Desk-Top Program Production) system has been developed that enables multiple program producers (directors) working at different locations to collaborate over a computer network and prepare a single program for broadcasting. In this system, information is shared among users by exchanging data edited on non-linear editing terminals in program post-production work over a network in real time. In short, the new DTPP system provides a collaborative work space for producing TV programs. The system does not make use of a special server for collaborative work but rather multiple interconnected editing terminals having the same functions. In this configuration, data at a terminal which has just been edited by some operation is forwarded to all other connected terminals for updating. This form of information sharing, however, requires that some sort of data synchronizing method be established since multiple terminals are operating on the same data simultaneously. We therefore adopt a method whereby the system synchronizes the clocks on each terminal at the time of connection and sends an operation time stamp together with edited data. This enables most recently modified data to be identified and all information on all terminals to be updated appropriately. This paper provides an overview of this new collaborative DTPP system and describes the techniques for exchanging edited data and synchronizing data.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.375-386
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• 2016

Most existing studies on broadcast services in orthogonal frequency division multiplexing (OFDM) systems have focused on how to allocate the transmission power to the subcarriers. However, because a broadcasting system must guarantee quality of service to all users, the performance of the broadcast service dominantly depends on the channel state of the user who has the lowest received signal-to-noise ratio among users. To reduce the effect of the worst user on the system performance, we propose a joint delivery scheme of unicast and broadcast transmissions in OFDM systems with broadcast and unicast best-effort users. In the proposed joint delivery scheme, the BS delivers the broadcast information using both the broadcast and unicast subcarriers at the same time in order to improve the performance of the broadcast service. The object of the proposed scheme is to minimize the outage probability of the broadcast service while maximizing the sum-rate of best-effort users. For the proposed joint delivery scheme, we develop an adaptive power and subcarrier allocation algorithm under the constraint of total transmission power. This paper shows that the optimal power allocation over each subcarrier in the proposed scheme has a multi-level water filling form. Because the power allocation and the subcarrier assignment problems should be jointly solved, we develop an iterative algorithm to find the optimal solution. Numerical results show that the proposed joint delivery scheme with adaptive power and subcarrier allocation outperforms the conventional scheme in terms of the outage probability of the broadcast service and the sum-rate of best-effort users.

• ETRI Journal
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• v.38 no.3
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• pp.435-443
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• 2016

In this paper, an efficient beam tracking algorithm for a regularized zero-forcing (RZF) approach in slowly fading multiple-input and single-output (MISO) broadcast channels is considered. By modifying an RZF equation, an RZF beam tracking algorithm is proposed using matrix perturbation theory. The proposed algorithm utilizes both beams from the previous time step and channel difference (between the previous and current time steps) to calculate the RZF beams. The tracking performance of the proposed algorithm is analyzed in terms of the mean square error (MSE) between a tracking approach and an exact recomputing approach, and in terms of the additional MSE caused by the beam tracking error at the receiver. Numerical results show that the proposed algorithm has almost the same performance as the exact recomputing approach in terms of the sum rate.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.244-247
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• 2020

Data augmentation has been helpful in improving the performance in deep learning, when we have a limited data and random erasing is one of the augmentations that have shown impressive performance in deep learning in multiple domains. But the main issue is that sometime it loses good features when randomly selected region is erased by some random values, that does not improve performance as it should. We target that problem in way that good features should not be lost and also want random erasing at the same time. For that purpose, we introduce new augmentation technique named Intra-Class Random Erasing (ICRE) that focuses on data to learn robust features of the same class samples by randomly exchanging randomly selected region. We perform multiple experiments by using different models including resnet18, VGG16 over variety of the datasets including ESC10, UrbanSound8K. Our approach has shown effectiveness over others methods including random erasing.