• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.59-65
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• 2022

Virtual memory is used as the method to ensure the safety of the system through memory protection in the real-time system. TLB miss caused by using virtual memory makes the real-time system WCET more pessimistically. TLB lockdown can be applied as a method to improve this problem. However, processors with limited TLB lockdown entries, a selection criterion is needed to efficiently utilize the TLB lockdown entry. In this paper, the most frequently accessed virtual pages in the process are applied to the TLB lockdown by analyzing memory profiling. The results showed that micro data TLB miss stall cycle and main data TLB miss stall cycle of the processor decreased by at least 4.7% and up to 29.7%.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.177-183
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• 2022

To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.289-296
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• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.259-265
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• 2023

Pedestrian detection is used to effectively detect pedestrians in various situations based on deep learning. Pedestrian detection has difficulty detecting pedestrians due to problems such as camera performance, pedestrian description, height, and occlusion. Even in the same pedestrian, performance in detecting them can differ according to the height of the pedestrian. The height of general pedestrians encompasses various scales, such as those of infants, adolescents, and adults, so when the model is applied to one group, the extraction of data becomes inaccurate. Therefore, this study proposed a pedestrian detection method that fine-tunes the pedestrian area by Refining Layer and Feature Concatenation to consider various heights of pedestrians. Through this, the score and location value for the pedestrian area were finely adjusted. Experiments on four types of test data demonstrate that the proposed model achieves 2-5% higher average precision (AP) compared to Faster R-CNN and DRPN.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.293-301
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• 2023

With the advancement of artificial intelligence and its various use cases, accessing it through edge computing environments is gaining traction. However, due to the nature of edge computing environments, efficient management and optimization of clusters distributed in different geographical locations is considered a major challenge. To address these issues, this paper proposes a centralization and automation tool called KubEVC-Agent based on Kubernetes. KubEVC-Agent centralizes the deployment, operation, and management of edge clusters and presents a use case of the data transformation for optimizing intra-cluster communication. This paper describes the components of KubEVC-Agent, its working principle, and experimental results to verify its effectiveness.

• Journal of IKEEE
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• v.22 no.4
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• pp.1214-1217
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• 2018

In this paper, we propose the development of a Small $360^{\circ}$ Oral Scanner embedded board. The proposed small $360^{\circ}$ oral scanner embedded board consists of image level and transfer method changing part FPGA part, memory part and FIFO to USB transfer part. The image level and transmission mode change unit divides the MIPI format oral image received through the small $360^{\circ}$ oral cavity image sensor and the image sensor into low power signal mode and high speed signal mode and distributes them to the port and transfers the level shift to the FPGA unit. The FPGA unit performs functions such as $360^{\circ}$ image distortion correction, image correction, image processing, and image compression. In the FIFO to USB transfer section, the RAW data transferred through the FIFO in the FPGA is transferred to the PC using USB 3.0, USB 3.1, etc. using the transceiver chip. In order to evaluate the efficiency of the proposed small $360^{\circ}$ oral scanner embedded board, it has been tested by an authorized testing institute. As a result, the frame rate per second is over 60 fps and the data transfer rate is 4.99 Gb/second

• Journal of IKEEE
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• v.16 no.3
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• pp.265-273
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• 2012

Data stability and leakage power dissipation have become a critical issue in scaled SRAM design. In this paper, an advanced 8T SRAM cell improving the read and write stability of data storage elements as well as reducing the leakage current in the idle mode is presented. During the read operation, the bit-cell keeps the noise-vulnerable data 'low' node voltage close to the ground level, and thus producing near-ideal voltage transfer characteristics essential for robust read functionality. In the write operation, a negative bias on the cell facilitates to change the contents of the bit. Unlike the conventional 6T cell, there is no conflicting read and write requirement on sizing the transistors. In the standby mode, the built-in stacked device in the 8T cell reduces the leakage current significantly. The 8T SRAM cell implemented in a 130 nm CMOS technology demonstrates almost 100 % higher read stability while bearing 20 % better write-ability at 1.2 V typical condition, and a reduction by 45 % in leakage power consumption compared to the standard 6T cell. The stability enhancement and leakage power reduction provided with the proposed bit-cell are confirmed under process, voltage and temperature variations.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.169-178
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• 2018

Currently, various sensors have been used for advanced driver assistance systems. In order to overcome the limitations of individual sensors, sensor fusion has recently attracted the attention in the field of intelligence vehicles. Thus, vision and radar based sensor fusion has become a popular concept. The typical method of sensor fusion involves vision sensor that recognizes targets based on ROIs (Regions Of Interest) generated by radar sensors. Especially, because AVM (Around View Monitor) cameras due to their wide-angle lenses have limitations of detection performance over near distance and around the edges of the angle of view, for high performance of sensor fusion using AVM cameras and radar sensors the exact ROI extraction of the radar sensor is very important. In order to resolve this problem, we proposed a sensor fusion scheme based on commercial radar modules of the vendor Delphi. First, we configured multiple radar data logging systems together with AVM cameras. We also designed radar post-processing algorithms to extract the exact ROIs. Finally, using the developed hardware and software platforms, we verified the post-data processing algorithm under indoor and outdoor environments.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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• pp.79-83
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• 2009

Streaming Technology can support the real-time playback without downloading and storing multimedia data in local HDD. So, client browser or plug-in can represent multimedia data before the end of file transmission using streaming technology. Recently, the demand for efficient real-time playback and transmission of large amounts of multimedia data is growing rapidly. But most users' connections over network are not fast and stable enough to download large chunks of multimedia data. In this paper, we propose an intelligent IP streaming system based on personalized media service. The proposed IP streaming system enables users to get an intelligent recommendation of multimedia contents based on the user preference information stored on the streaming server or the home media server. The supposed intelligent IP streaming system consists of Server Metadata Agent, Pumping Server, Contents Storage Server, Client Metadata Agent and Streaming Player. And in order to implement the personalized media service, the user information, user preference information and client device information are managed under database concept. Moreover, users are assured of seamless access of streamed content event if they switch to another client device by implementing streaming system based on user identification and device information. We evaluate our approach with manufacturing home server system and simulation results.

• Journal of Information Science Theory and Practice
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• v.2 no.2
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• pp.6-15
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• 2014

Initial based disambiguation of author names is a common data pre-processing step in bibliometrics. It is widely accepted that this procedure can introduce errors into network data and any subsequent analytical results. What is not sufficiently understood is the precise impact of this step on the data and findings. We present an empirical answer to this question by comparing the impact of two commonly used initial based disambiguation methods against a reasonable proxy for ground truth data. We use DBLP, a database covering major journals and conferences in computer science and information science, as a source. We find that initial based disambiguation induces strong distortions in network metrics on the graph and node level: Authors become embedded in ties for which there is no empirical support, thus increasing their sphere of influence and diversity of involvement. Consequently, networks generated with initial-based disambiguation are more coherent and interconnected than the actual underlying networks, and individual authors appear to be more productive and more strongly embedded than they actually are.