• Journal of Multimedia Information System
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• v.5 no.2
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• pp.105-110
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• 2018

Autonomous cars require the integration of multiple communication systems for driving safety. Many carmakers unveil mirrorless concept cars aiming to replace rear and sideview mirrors in vehicles with camera monitoring systems, which eliminate blind spots and reduce risk. This paper presents optical vehicle-to-vehicle (V2V) communications for autonomous mirrorless cars. The flicker-free light emitting diode (LED) light sources, providing illumination and data transmission simultaneously, and a high speed camera are used as transmitters and a receiver in the OCC link, respectively. The rear side vehicle transmits both future action data and vehicle type data using a headlamp or daytime running light, and the front vehicle can receive OCC data from the camera that replaces side mirrors so as not to prevent accidents while driving. Experimental results showed that action and vehicle type information were sent by LED light sources successfully to the front vehicle's camera via the OCC link and proved that OCC-based V2V communications for mirrorless cars can be a viable solution to improve driving safety.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.121-130
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• 2018

We developed multimedia esophageal catheters for use with birds to measure and record ECG and angular velocity while anesthesized, at rest, and in flight. These catheters enable estimates of blood pressure based on readings given by an angular velocity sensor and by RR intervals of ECG affected by EMG. In our experiments, the catheters had the following characteristics: 1. Esophageal catheters offer a topological advantage with 8-dB SNR improvement due to elimination of electromyography (EMG). 2. We observed a very strong correlation between blood pressure and the angular velocity of esophageal catheter axial rotation. 3. The impulse conduction pathway (Purkinje fibers) of the cardiac ventricle has a direction opposite to that of the mammalian pathway. 4. Sympathetic nerves predominate in flight, and RR interval variations are strongly suppressed. The electrophysiological data obtained by this study provided especially the state of the avian autonomic nervous system activity, so we can suspect individual's health condition. If the change of the RR interval was small, we can perform an isolation or screening from the group that prevent the pandemics of avian influenza. This catheter shall be useful to analysis an avian autonomic system, to perform a screening, and to make a positive policy against the massive infected avian influenza.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.131-138
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• 2018

This work describes a binary image based fast Difference of Gaussian (DoG) filter using zero-dimensional (0-d) convolution and state machine look up tables (LUTs) for image and video stitching hardware platforms. The proposed approach for using binary images to obtain DoG filtering can significantly reduce the data size compared to conventional gray scale based DoG filters, yet binary images still preserve the key features of the image such as contours, edges, and corners. Furthermore, the binary image based DoG filtering can be realized with zero-dimensional convolution and state machine LUTs which eliminates the major portion of the adder and multiplier blocks that are generally used in conventional DoG filter hardware engines. This enables fast computation time along with the data size reduction which can lead to compact and low power image and video stitching hardware blocks. The proposed DoG filter using binary images has been implemented with a FPGA (Altera DE2-115), and the results have been verified.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.91-98
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• 2018

We devised a bird-borne transceiver unit for S-band packet radio communications based on the CC2500 transceiver, a device manufactured by Texas Instruments (TI). Our assessments determined the optimal parameters needed to achieve successful bird-to-center communication over a distance of 18 km and bird-to-bird communication over a distance of 200 m. These parameters included optimal modulation methods, transmission rates, and antennas. We equipped the transceiver unit with a modified dipole antenna (collinear antenna), which we tested in a 10 m anechoic chamber. Our experimental assessments and circuit design review identified the following parameters: 2FSK modulation method; 14.28 kHz frequency shift; 101.56 kHz IF reception bandwidth; and Manchester encoding (+). Our assessments showed bird-to-bird communications could be achieved over a distance of 200 m assuming MSK, FEC (+), and 500 kbps. Following tests by an official body, we obtained 28 sets of a type approval license for 2.4 GHz. In cooperation with the Yamashina Institute for Ornithology, we attempted to tag and release six or more swans. This unit gives us the ability to obtain vital data on swans. We expect this data to provide significant benefits, including clues on improving screening for infected individuals.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.175-182
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• 2021

Oral cancer is ranked second most diagnosed cancer among Indian population and ranked sixth all around the world. Oral cancer is one of the deadliest cancers with high mortality rate and very less 5-year survival rates even after treatment. It becomes necessary to detect oral malignancies as early as possible so that timely treatment may be given to patient and increase the survival chances. In recent years deep learning based frameworks have been proposed by many researchers that can detect malignancies from medical images. In this paper we have proposed a deep learning-based framework which detects oral cancer from histopathology images very efficiently. We have designed our model to split the color channels and extract deep features from these individual channels rather than single combined channel with the help of Efficient NET B3. These features from different channels are fused by using feature fusion module designed as a layer and placed before dense layers of Efficient NET. The experiments were performed on our own dataset collected from hospitals. We also performed experiments of BreakHis, and ICML datasets to evaluate our model. The results produced by our model are very good as compared to previously reported results.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.197-202
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• 2021

Bitcoin and blockchain are often making headlines not only on TV or media but also among the public in today's society. These technologies have been developed after the risk of the centralized financial system came to the fore during the 2007 global financial crisis. Since then, an anonymous inventor called Satoshi Nakamoto penned the bitcoin white paper where a blockchain-based reference implementation was introduced. Bitcoin was able to achieve unprecedented growth by positioning itself as one of the top global currencies in terms of market capitalization after five years since its development. The pace of Vietnam's economic development is notably fast among Asian nations, while the nation was expected to be a Southeast Asian blockchain hub but they have banned virtual currency trading recently. However, they've also designated the State Bank of Vietnam (SBV) as a responsible agency for the research of blockchain-based cryptocurrencies, the construction of a service ecosystem, and their test operations. The fast-growing economy, increasing number of smartphone users, and the Vietnam government's support policies for startups substantiate these efforts. Therefore, this paper attempts to study the current status of Vietnam's blockchain technology that has been considered to be the center of blockchain systems right behind Singapore, and its implications for Korean companies.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.147-158
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• 2021

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

• Journal of Multimedia Information System
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• v.6 no.3
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• pp.147-154
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• 2019

In a variety of fields, highly developed technology is being combined to create a lot of value. In order to keep up with this global trend, the Ministry of Education, which is in charge of national education, continuously develops and applies content for creative education to textbooks. The continuous development of content for creative education is not only related to national interests, but also to the continued development of mankind. Today, the succession and development of human knowledge is in charge of the education system. Research into an effective educational system is necessary for effective succession of rapidly developing science and technology and building up technical personnel with such skills. In particular, the computer science field is faster in development than other scientific fields and has accumulated many technologies, indicating that it takes a lot of time and good teaching to foster talent that can effectively utilize the technology. In this paper, elementary school subjects were analyzed to achieve the purpose of cultivating talent in the field of computer science. In addition, we have investigated techniques related to computer programming learning not covered in elementary school subjects. So we developed content that students need to practice. Next, we taught the content to randomly selected elementary school students and assessed their educational effectiveness. As a result of training using the content we developed, 55.37% increased academic performance.

• Journal of Multimedia Information System
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• v.6 no.3
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• pp.131-138
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• 2019

With the rapid evolution of communication technology, it became possible to overcome the spatial and temporal limitations faced by humans to some extent. Furthermore, the quality of personal life was revolutionized with the emergence of the personal communication device commonly known as the smart phone. In terms of defense networks, however, due to restrictions from the military and security perspectives, the use of smart phones has been prohibited and controlled in the army; thus, they are not being used for any defense strategy purposes as yet. Despite the current consideration of smart phones for military communication, due to the difficulties of network configuration and the high cost of the necessary communication devices, the main tools of communication between soldiers are limited to the use of flag, voice or hand signals, which are all very primitive. Although these primitive tools can be very effective in certain cases, they cannot overcome temporal and spatial limitations. Likewise, depending on the level of the communication skills of each individual, communication efficiency can vary significantly. As the term of military service continues to be shortened, however, types of communication of varying efficiency depending on the levels of skills of each individual newly added to the military is not desirable at all. To address this problem, it is essential to prepare an intuitive network configuration that facilitates use by soldiers in a short period of time by easily configuring the strategy network at a low cost while maintaining its security. Therefore, in this article, the author proposes a Zigbee-based local strategic network by using Opnet and performs a simulation accordingly.