• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.436-439
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• 2005

As mobile computers and embedded systems are becoming popular recently, we need to study how to utilize the resources such as power, space, CPU clocks, and memory efficiently. In traditional embedded system development, we were interested in resource usage based on hardware but, as software is becoming more important, we need to study how to analyze the resource usage based on software. In this research, we propose a new method called 'Instruction Level Resource Usage Analysis Method' and implement it as a resource usage analysis tool called 'I-Debugger'. I-Debugger is constructed on three layers: debugging layer which controls the execution of software on instruction level, statistic layer which gathers real-time data and convert to useful information, and analysis layer which generate useful information to specific applications. We have applied the debugger to some simple problem and found that our method is useful in developing resource efficient embedded systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.201-204
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• 2017

People live in their everyday life busy with studies, part-time jobs, and searching for an ideal job. In their busy routine, they try to find time for themselves and expose their emotions through diverse social network services(SNS). We made a service that we can plant a virtual tree in places we daily visit and go by. You can keep note on the virtual tree and look through the past records. It is a reality based mobile application service that can be used like a diary.In this project we chose the tree as the metaphor and tried to express time passing in a specific place. As our memory is a part of our daily life, we emphasized the meaning of space important.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.631-634
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• 2009

Recent advances in wireless communication, electronics, MEMS device, sensor and battery technology have made it possible to manufacture low-cost, low-power, multi-function tiny sensor nodes. A large number of tiny sensor nodes form sensor network through wireless communication. Sensor networks represent a significant improvement over traditional sensors, research on Zigbee wireless image transmission has been a topic in industrial and scientific fields. In this paper, we design a Zigbee wireless image sensor node and multimedia monitoring server system. It consists of embedded processor, memory, CMOS image sensor, image acquisition and processing unit, Zigbee RF module, power supply unit and remote monitoring server system. In the future, we will further improve our Zigbee wireless image sensor node and monitoring server system. Besides, energy-efficient Zigbee wireless image transmission protocol and interworking with mobile network will be our work focus.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.79-85
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• 2021

The purpose of this study was to investigate the existing augmented reality (AR) contents in education and to develop digital AR contents to promote the learning outcomes in nursing skills education. AR contents has been widely used in education such as children's books, coloring, or exhibition experiences, but rarely developed in nursing education. We wanted to develop AR contents on nursing skills which required memorization of complex contents. In order to improve nursing students' memorization skills, we developed AR techniques holding and executing cameras along with game elements of time and points. In order to give the effect of placing objects in front of the user's eyes, an augmented reality camera was applied, and a total of 40 levels were created to produce the sequence of nursing techniques. This study showed that learning contents in the medical field requiring expertise could be implemented as AR contents. The content developed in this study will be used as a learning aid for nursing students.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1243-1252
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• 2012

Due to the widespread use of smart devices, threats of direct observation attacks such as shoulder surfing and recording attacks, by which user secrets can be stolen at user interfaces, are increasing greatly. Although formal security models are necessary to evaluate the possibility of and security against those attacks, such a model does not exist. In this paper, based on the previous work in which a HCI cognitive model was firstly utilized for analyzing security, we propose STM-GOMS model as an improvement of GOMS-based model with regard to memory limitations. We then apply STM-GOMS model for analyzing usability and security of a password entry scheme commonly used in smart devices and show the scheme is vulnerable to the shoulder-surfing attack. We finally conduct user experiments to show the results that support the validity of STM-GOMS modeling and analysis.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.99-103
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• 2021

Recently, convolutional neural networks (CNNs) have been widely utilized due to their outstanding performance in various computer vision fields. However, due to their computational-intensive and high memory requirements, it is difficult to deploy CNNs on hardware platforms that have limited resources, such as mobile devices and IoT devices. To address these limitations, a neural network compression research is underway to reduce the size of neural networks while maintaining their performance. This paper proposes a CNN compression technique that dynamically adjusts the thresholds of pruning, one of the neural network compression techniques. Unlike the conventional pruning that experimentally or heuristically sets the thresholds that determine the weights to be pruned, the proposed technique can dynamically find the optimal thresholds that prevent accuracy degradation and output the light-weight neural network in less time. To validate the performance of the proposed technique, the LeNet was trained using the MNIST dataset and the light-weight LeNet could be automatically obtained 1.3 to 3 times faster without loss of accuracy.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.76-84
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• 2022

In this paper, in order to compose the visual perception cognitive function training content that can be linked with the IPUZZLE image block, an interactive content device that utilizes photos and videos of smartphones. Four areas of visual memory, visual continuity, spatial relationship, and visual discrimination were derived and the content operation, application method, and scenario were written. It was intended to continuously give and induce children's desire to participate in training by designing the content image and developing the existing learning terrain visual and perceptual cognitive function training materials in the form of mobile mini-games. Experiential activities were conducted for general children and their guardians using the developed contents, and the results were found to be significant in terms of concentration, effect, and effect compared to basic puzzle toys. It is expected that this thesis will be a meaningful data for the study of cognitive function improvement activities based on digital toys and contents.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.355-366
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• 2022

This paper proposes a microcode-based neural network accelerator controller for artificial intelligence accelerators that can be reconstructed using a programmable architecture and provide the advantages of low-power and ultra-small chip size. In order for the target accelerator to support various neural network models, the neural network model can be converted into microcode through microcode compiler and mounted on accelerator to control the operators of the accelerator such as datapath and memory access. While the proposed controller and accelerator can run various CNN models, in this paper, we tested them using the YOLOv2-Tiny CNN model. Using a system clock of 200 MHz, the Controller and accelerator achieved an inference time of 137.9 ms/image for VOC 2012 dataset to detect object, 99.5ms/image for mask detection dataset to detect wearing mask. When implementing an accelerator equipped with the proposed controller as a silicon chip, the gate count is 618,388, which corresponds to 65.5% reduction in chip area compared with an accelerator employing a CPU-based controller (RISC-V).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.