• 제목/요약/키워드: JMIS

검색결과 294건 처리시간 0.013초

Performance Analysis of Future Video Coding (FVC) Standard Technology

  • Choi, Young-Ju;Kim, Ji-Hae;Lee, Jong-Hyeok;Kim, Byung-Gyu
    • Journal of Multimedia Information System
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    • 제4권2호
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    • pp.73-78
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    • 2017
  • The Future Video Coding (FVC) is a new state of the art video compression standard that is going to standardize, as the next generation of High Efficiency Video Coding (HEVC) standard. The FVC standard applies newly designed block structure, which is called quadtree plus binary tree (QTBT) to improve the coding efficiency. Also, intra and inter prediction parts were changed to improve the coding performance when comparing to the previous coding standard such as HEVC and H.264/AVC. Experimental results shows that we are able to achieve the average BD-rate reduction of 25.46%, 38.00% and 35.78% for Y, U and V, respectively. In terms of complexity, the FVC takes about 14 times longer than the consumed time of HEVC encoder.

An Art-Robot Expressing Emotion with Color Light and Behavior by Human-Object Interaction

  • Kwon, Yanghee;Kim, Sangwook
    • Journal of Multimedia Information System
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    • 제4권2호
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    • pp.83-88
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    • 2017
  • The era of the fourth industrial revolution, which will bring about a great wave of change in the 21st century, is the age of super-connection that links humans to humans, objects to objects, and humans to objects. In the smart city and the smart space which are evolving further, emotional engineering is a field of interdisciplinary researches that still attract attention with the development of technology. This paper proposes an emotional object prototype as a possibility of emotional interaction in the relation between human and object. By suggesting emotional objects that produce color changes and movements through the emotional interactions between humans and objects against the current social issue-loneliness of modern people, we have approached the influence of our lives in the relation with objects. It is expected that emotional objects that are approached from the fundamental view will be able to be in our lives as a viable cultural intermediary in our future living space.

Detection of Rice Disease Using Bayes' Classifier and Minimum Distance Classifier

  • Sharma, Vikas;Mir, Aftab Ahmad;Sarwr, Abid
    • Journal of Multimedia Information System
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    • 제7권1호
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    • pp.17-24
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    • 2020
  • Rice (Oryza Sativa) is an important source of food for the people of our country, even though of world also .It is also considered as the staple food of our country and we know agriculture is the main source country's economy, hence the crop of Rice plays a vital role over it. For increasing the growth and production of rice crop, ground-breaking technique for the detection of any type of disease occurring in rice can be detected and categorization of rice crop diseases has been proposed in this paper. In this research paper, we perform comparison between two classifiers namely MDC and Bayes' classifiers Survey over different digital image processing techniques has been done for the detection of disease in rice crops. The proposed technique involves the samples of 200 digital images of diseased rice leaf images of five different types of rice crop diseases. The overall accuracy that we achieved by using Bayes' Classifiers and MDC are 69.358 percent and 81.06 percent respectively.

Enriching Natural Monument with User-Generated Mobile Augmented Reality Mashup

  • Shin, Choonsung;Hong, Sung-Hee;Yoon, Hyoseok
    • Journal of Multimedia Information System
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    • 제7권1호
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    • pp.25-32
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    • 2020
  • This paper proposes a mobile augmented reality mashup for cultural heritage sites such as natural monuments. Several benefits of mobile augmented reality solutions are ideal for preserving and protecting cultural heritage sites. By presenting mobile augmented reality mashup scenarios and mobile mashup framework, we introduce how user-generated multimedia contents can be added. We present two scenarios of Mashup Viewer and Mashup Maker. In Mashup Viewer mode, visitors can create new AR contents using mashup tools for memo, Twitter, images and statistical graphs. In Mashup Maker mode, other visitors also can view the user-generated multimedia AR contents using QR codes as access points. To show feasibility of our approach in mobile platforms, we compare several detection algorithms on PC and mobile platform and report on deployment of our approach in a natural monument museum. With our proposed mashup tools, visitors to the cultural heritage sites can enjoy default AR contents provided by the site administrators and also participate as active content producers and consumers.

Machine Learning Techniques for Speech Recognition using the Magnitude

  • Krishnan, C. Gopala;Robinson, Y. Harold;Chilamkurti, Naveen
    • Journal of Multimedia Information System
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    • 제7권1호
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    • pp.33-40
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    • 2020
  • Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is the Data mining task of inferring a function from labeled training data. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper demonstrates an overview of major technological standpoint and gratitude of the elementary development of speech recognition and provides impression method has been developed in every stage of speech recognition using supervised learning. The project will use DNN to recognize speeches using magnitudes with large datasets.

Drivable Area Detection with Region-based CNN Models to Support Autonomous Driving

  • Jeon, Hyojin;Cho, Soosun
    • Journal of Multimedia Information System
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    • 제7권1호
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    • pp.41-44
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    • 2020
  • In autonomous driving, object recognition based on machine learning is one of the core software technologies. In particular, the object recognition using deep learning becomes an essential element for autonomous driving software to operate. In this paper, we introduce a drivable area detection method based on Region-based CNN model to support autonomous driving. To effectively detect the drivable area, we used the BDD dataset for model training and demonstrated its effectiveness. As a result, our R-CNN model using BDD datasets showed interesting results in training and testing for detection of drivable areas.

Security Attacks and Challenges of VANETs : A Literature Survey

  • Quyoom, Abdul;Mir, Aftab Ahmad;Sarwar, Abid
    • Journal of Multimedia Information System
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    • 제7권1호
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    • pp.45-54
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    • 2020
  • This paper presented a brief introduction along with various wireless standards which provide an interactive way of interaction among the vehicles and provides effective communication in VANET. Security issues such as confidentiality, authenticity, integrity, availability and non-repudiation, which aims to secure communication between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). A detailed discussion and analysis of various possible attacks based on security services are also presented that address security and privacy concern in VANETs. Finally a general analysis of possible challenges is mentioned. This paper can serve as a source and reference in building the new technique for VANETs.

Impact of ITSM Military Service Quality and Value on Service Trust

  • Woo, Hanchul;Lee, Sangdo;Huh, Jun-Ho;Jeong, Sukjae
    • Journal of Multimedia Information System
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    • 제7권1호
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    • pp.55-72
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    • 2020
  • As the IT service environment grows, it is critical in terms of IT service quality to minimize the occurrence of failures due to changes in applications and to diagnose and recover in a short period of time how failure will affect the business. Thus, the Defense Acquisition Program Administration (DAPA) has been building and operating ITSMs to implement IT service management in a leading manner. Information Technology Service Management (ITSM) is divided into events, obstacles, changes, versions and setup management to ensure flexibility and stability in service delivery. It is also operated separately from service level, availability, capacity, financial and IT service continuity management to ensure service quality and cost efficiency. Based on ITSM military service history, this study looks at the impact of quality of service on value, satisfaction, and trust. The results of the analysis are highly valuable for future ITSM implementation and operation.

An Evaluation Framework for Defense Informatization Policy

  • Jung, Hosang;Lee, Sangho
    • Journal of Multimedia Information System
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    • 제7권1호
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    • pp.73-86
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    • 2020
  • The well-known sentence, "You can't manage what you don't measure" suggests the importance of measurement. The Ministry of National Defense (MND) in Korea is measuring the effort of informatization for various dimensions such as validity, adequacy, and effectiveness using the MND evaluation system to obtain positive and significant effects from informatization. MND views the defense informatization domain as divided into the defense informatization policy, the defense informatization project, and the defense informatization level, which can measure the informatization capability of the MND and the armed forces or organizations. Furthermore, it feels there is some limitation, such as those related to ambiguity and reliability, present in the system. To overcome the limitations in the existing system to evaluate the defense informatization policy, this study proposes a revised evaluation framework for the policy of defense informatization, its indicators, and measurement methods.

R-to-R Extraction and Preprocessing Procedure for an Automated Diagnosis of Various Diseases from ECG Data

  • Timothy, Vincentius;Prihatmanto, Ary Setijadi;Rhee, Kyung-Hyune
    • Journal of Multimedia Information System
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    • 제3권2호
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    • pp.1-8
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    • 2016
  • In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.