• Journal of information and communication convergence engineering
• /
• v.9 no.6
• /
• pp.683-688
• /
• 2011

The wireless sensor network (WSN) is well known for an enabling technology for the ubiquitous environment such as real-time surveillance system, habitat monitoring, home automation and healthcare applications. However, the WSN featuring wireless communication through air, a resource constraints device and irregular network topology, is threatened by malicious nodes such as eavesdropping, forgery, illegal modification or denial of services. For this reason, security in the WSN is key factor for utilizing the sensor network into the commercial way. There is a series of symmetric cryptography proposed by laboratory or industry for a long time. Among of them, recently proposed HUMMINGBIRD algorithm, motivated by the design of the well-known Enigma machine, is much more suitable to resource constrained devices, including smart card, sensor node and RFID tags in terms of computational complexity and block size. It also provides resistance to the most common attacks such as linear and differential cryptanalysis. In this paper, we implements ultra-lightweight cryptography, HUMMINGBIRD algorithm into the resource constrained device, sensor node as a perfectly customized design of sensor node.

• International journal of advanced smart convergence
• /
• v.4 no.2
• /
• pp.69-75
• /
• 2015

There are several possible places which are accessible in many buildings and facilities, various types of systems have been utilized such as access control or surveillance depending on the purpose. Especially if security is important, it must go through the various authentication procedures when people can access. Until now many access control systems have been proposed and developed, they are applied and utilized to companies which security is needed. However, as time passes the problems with existing access control systems occur or the vulnerabilities related to access control are reported, as technology advances. The solution to this, we propose authentication technology related to access control using LED-QR tag.

• Journal of the Semiconductor & Display Technology
• /
• v.17 no.2
• /
• pp.67-70
• /
• 2018

Object detection and tracking is an exciting and interesting research area in the field of computer vision, and its technologies have been widely used in various application systems such as surveillance, military, and augmented reality. This paper proposes and implements a novel and more robust object recognition and tracking system to localize and track multiple objects from input images, which estimates target state using the likelihoods obtained from multiple CNNs. As the experimental result, the proposed algorithm is effective to handle multi-modal target appearances and other exceptions.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.319-322
• /
• 2011

본 논문에서는 스마트 폰과 가정 내의 이동 로봇을 결합하여, 스마트 폰 사용자가 이동 로봇을 통해 원격으로 가정의 수상한 침입자나 거동이 불편한 노약자 혹은 어린 아이들을 살펴볼 수 있도록 개발된 지능형 감시 시스템의 설계와 구현에 대해 소개한다. 이동 로봇의 제한적인 인식 능력과 계산 능력을 고려하여, 이동 로봇의 완전한 자율성에만 의존하여 감시 작업을 수행하지 않고, 사용자와 로봇의 혼합 제어 방식으로 감시 로봇을 제어하도록 설계하였다.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.182-189
• /
• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.1-15
• /
• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.22-29
• /
• 2010

A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.4
• /
• pp.461-466
• /
• 2016

In this paper, a capsule body shaped surveillance/monitoring device is developed. The device includes a camera and GPS module to transmit live video data and real time GPS coordinates respectively using the Intel Edison module. A control application is developed for the smart phones and tablets to wirelessly view the live video stream and location of the capsule device and also to switch between the multiple capsule devices installed at different locations. The coordination between the developed device and the smart phone / tablet is done using the wireless function of the Intel Edison module.

• The Journal of Information Systems
• /
• v.19 no.4
• /
• pp.183-206
• /
• 2010

The technology acceptance model (TAM) is a lot of cited in information technology adoption and usage researches. But TAM has been conducted primarily in volitional environments of the adoption of new technology. This paper discusses technology acceptance in accounting information systems to examine TAM with Characteristics of Organizations and Individuals in mandated using Combined Guard System. Combined Guard System is a scientific guard system that is composed of automated surveillance system, automated sensing system and control system. GOP Scientific Guard System is operated by GOP unit in Korean Army O Division from 2006. In this study, using the extended technology acceptance model, we have analyzed factors which affect the usage intention of GOP Scientific Guard System in mandated using environment. Based upon previous researches, we have selected Support of management unit, Training, Perceived Risk, Subjective Knowledge and Computer Self-efficacy, perceived usefulness, perceived ease of use, and usage intention as variables and proposed a research model. We collected 253 survey questionnaires from Korean army officer and soldier who are serviced at GOP unit in O Division, and analyzed the data using SPSS 12.0 and SmartPLS 2.0M3. According to the results by PLS analysis, According to the results by PLS analysis, Training and Subjective Knowledge did not affect Perceived usefulness, but the other hypotheses were accepted. And Perceived usefulness, and Ease of use influenced the Usage intention. The results of this study will increase Characteristics of Organizations and Individuals on GOP Scientific Guard System and eventually contribute to establishing the activation of Combined Guard System.

• Journal of the Korea Convergence Society
• /
• v.8 no.3
• /
• pp.15-22
• /
• 2017

People generally believe that disaster forecast and warning systems and response systems are well established in the age of cutting edge technology. As a matter of fact, reliable systems to respond to disasters are not properly equipped, as we witnessed the Sewol ferry disaster in 2014. The existing forecast and warning systems are based on sensor information with low efficiency, and image information is only operated by monitoring staff manually. In addition, the interconnection between a warning system and a response system in order to decide how to cope with the recognized disaster is very insufficient. This paper introduces the CCTV based disaster recognition and real time crisis response system composed of the CCTV image recognition engine and the crisis response technique. This system has brought the possibility to overcome the limitations of existing sensor based forecast and warning systems, and to resolve the problems in the absence of monitoring staff when responding to crisis.