• KIPS Transactions on Computer and Communication Systems
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• v.10 no.1
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• pp.1-6
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• 2021

Recently, incidents such as proxy surgery (unlicensed medical practice) have been reported in the media that threaten the safety of patients. Alternatives such as the introduction of operating room surveillance camera devices to prevent proxy surgery are emerging, but there are practical difficulties in implementing them due to strong opposition from the medical community. However, the social credibility of doctors is falling as incidents such as proxy surgery occur frequently. In this paper, we propose a medical staff identification system combining Beacon and iris recognition. The system adds reliability by operating on the blockchain network. The system performs primary identification by performing user authentication through iris recognition and proves that the medical staff is in the operating room through beacons. It also ensures patient trust in the surgeon by receiving beacon signals in the background and performing iris authentication at random intervals to prevent medical staff from leaving the operating room after only performing initial certification.

• Phonetics and Speech Sciences
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• v.13 no.2
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• pp.103-109
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• 2021

In this study, we investigated longitudinal music perception of adult cochlear implant (CI) users and how acoustic stimulation with CI affects their music performance. A total of 163 participants' data were analyzed retrospectively. 96 participants were using acoustic stimulation with CI and 67 participants were using electrical stimulation only via CI. The music performance (melody identification, appreciation, and satisfaction) data were collected pre-implantation, 1-year, and 2-year post-implantation. Mixed repeated measures of ANOVA and pairwise analysis adjusted by Tukey were used for the statistics. As result, in both groups, there were significant improvements in melody identification, music appreciation, and music satisfaction at 1-year, and 2-year post-implantation than a pre-implantation, but there was no significant difference between 1 and 2 years in any of the variables. Also, the group of acoustic stimulation with CI showed better perception skill of melody identification than the CI-only group. However, no differences found in music appreciation and satisfaction between the two groups, and possible explanations were discussed. In conclusion, acoustic and/or electrical hearing devices benefit the recipients in music performance over time. Although acoustic stimulation accompanied with electrical stimulation could benefit the recipients in terms of listening skills, those benefits may not extend to the subjective acceptance of music. These results suggest the need for improved sound processing mechanisms and music rehabilitation.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.1-9
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• 2019

With the development of imaging information communication technology in modern society, imaging acquisition and mass production technology have developed rapidly. However, crime rates using these technology are increased and forensic studies are conducted to prevent it. Identification techniques for image acquisition devices are studied a lot, but the field is limited to images. In this paper, camera model identification technique for video, not image is proposed. We analyzed video frames using the trained model with images. Through training and analysis by considering the frame characteristics of video, we showed the superiority of the model using the P frame. Then, we presented a video camera model identification system by applying a majority-based decision algorithm. In the experiment using 5 video camera models, we obtained maximum 96.18% accuracy for each frame identification and the proposed video camera model identification system achieved 100% identification rate for each camera model.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.321-332
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• 2020

Decentralized approaches are extensively researched by academia and industry in order to cover up the flaws of existing systems in terms of data privacy. Blockchain and decentralized learning are prominent representatives of a deconcentrated approach. Blockchain is secure by design since the data record is irrevocable, tamper-resistant, consensus-based decision making, and inexpensive of overall transactions. On the other hand, decentralized learning empowers a number of devices collectively in improving a deep learning model without exposing the dataset publicly. To motivate participants to use their resources in building models, a decent and proportional incentive system is a necessity. A centralized incentive mechanism is likely inconvenient to be adopted in decentralized learning since it relies on the middleman that still suffers from bottleneck issues. Therefore, we design an incentive model for decentralized learning applications by leveraging the Ethereum smart contract. The simulation results satisfy the design goals. We also outline the concerns in implementing the presented scheme for sensitive data regarding privacy and data leakage.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.12-19
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• 2021

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1662-1669
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• 2020

Google cloud includes data uploaded and synchronized by users, as well as synchronization history of all cloud services, users' smartphone usage, and location information. Therefore, Google cloud data can be useful for digital forensics from a user behavior analysis perspective. Through this paper, we have identified the types of cloud data that can be acquired using Google's Takeout service and developed a tool that can be usefully utilized in digital forensics research and investigation by screening and analyzing the data required for analyzing user behavior. Because Google cloud data is synchronized through Google accounts regardless of the type of computing device, Google service data used on various devices such as PCs, smartphones, and tablet PCs can be acquired through Google accounts without the device. Therefore, the results of this paper's research are expected to be very useful for digital forensics research and investigation in the current situation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.108-114
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• 2021

AVTE(Air Vehicle Test Equipment) is a device to check status of on-board aircraft equipment before and after flight for performing successful UAV(Unmanned Aerial Vehicle) missions. This paper describes software design and test sequence of the AVTE for enabling easy-manual check by the operator and convenient automatic check of on-board electric equipment respectively. The proposed AVTE inspects BIT(Built-In Test) results of on-board LRUs(Line Replacement Units) including avionics and sensor sub-system devices. Also, it monitors all the LRU status and check the normality of aircraft equipment by means of setting specific values of the LRUs and confirming the expected test results. The AVTE prints the test results as a form of report to easily check the normal conditions of the aircraft equipment and operates automatically without operator interaction, thus being thought to effectively reduce workload of the operator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1431-1436
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• 2020

In modern society, various video devices such as CCTV and black boxes are used for convenience. However, noise is frequently generated in the process of transmitting and receiving video images and video signals photographed at night. If such noise is not eliminated, the problem that the image is difficult to identify is generated. Accordingly, noise elimination of images in the image information is an indispensable step. Salt and Pepper noises are typical impulse noises among image noises. Previous research has been carried out as a method for eliminating noise, and CWMF, MMF and A-TMF are typical methods. In common, such a filter exhibits excellent performance in a low-density noise area, but a disadvantage is that noise elimination performance in a high-density noise area is somewhat insufficient. Accordingly, the proposed algorithm uses the inflection point of the histogram graph to separate areas and remove singular points, and proposes a weighting filter utilizing histogram distribution. PSNR was used for objective judgment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1224-1230
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• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1254-1260
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• 2019

Recently, 5G cellular systems have been attracted great attention as a key enabler for Industry 4.0. In this paper, we propose a novel random access based on pilot-assisted opportunistic transmission to support internet-of-things (IoT) scenario in cellular networks. A key feature of our proposed scheme is to enable each of IoT devices to attempt opportunistic transmission of its data packet in Step 3 with randomly selected uplink pilot signal. Both the opportunistic transmission and the pilot randomization in Step 3 are effective to significantly mitigate the occurrence of packet collisions. We mathematically analyze our proposed scheme in terms of packet collision probability and uplink resource efficiency. Through simulations, we verify the validity of our analysis and evaluate the performance of our proposed scheme. Numerical results show that our proposed scheme outperforms other competitive schemes.