• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.80-87
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• 2006

In this paper, we analyze the performance of IEEE 802.11b WLAN communication between access point(AP) and mobile equipment(ME) in 2.4 GHz band with noise and interference factors. WLAN communication at in-vehicle environment is assumed as the communication between main vehicle controller and electronic device such as sensor, ECU (Electrical Control Unit) in vehicle on telematics field for implementing wireless vehicle control system. Received interference level from other system's mobile equipment in the same band and automobile noise from each part of vehicle can be the main factors that can cause increasing error rate of control signal. With these (actors, we focus on the Eb/No the BER performance of WLAN for analyzing the characteristic of interference factors by the measured bit error rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.299-302
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• 2014

Due to the convergence between digital devices and the development of wireless communication technology, bit-signal sensor miniaturization, building an Electronic Medical Record (EMR) which is a digital version of a paper chart that contains all of a patient's medical history and the information of Electronic Health Record (EHR), Ubiquitous healthcare (u-Healthcare) that can monitor their health status and provide personal healthcare service anytime and anywhere. Also, the appearance of cloud computing technology is one of the factors that accelerate the development of u-healthcare service. However, if the individual information to be used maliciously during the u-healthcare service utilization, leads to serious problems directly related to the individual's life because if it goes beyond the level of simple health screening and treatment, it may not provide accurate and reliable healthcare services. For this reason, we analyzed a variety of security issues related to u-healthcare service in cloud computing environment and described about directions of secure health information sharing system construction. In addition, we suggest the future developmental direction for th activation of u-healthcare industry.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.253-263
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• 2013

Frequency Scanning Interferometry(FSI) system, one of the most promising optical surface measurement techniques, generally results in superior optical performance comparing with other 3-dimensional measuring methods as its hardware structure is fixed in operation and only the light frequency is scanned in a specific spectral band without vertical scanning of the target surface or the objective lens. FSI system collects a set of images of interference fringe by changing the frequency of light source. After that, it transforms intensity data of acquired image into frequency information, and calculates the height profile of target objects with the help of frequency analysis based on Fast Fourier Transform(FFT). However, it still suffers from optical noise on target surfaces and relatively long processing time due to the number of images acquired in frequency scanning phase. 1) a Polarization-based Frequency Scanning Interferometry(PFSI) is proposed for optical noise robustness. It consists of tunable laser for light source, ${\lambda}/4$ plate in front of reference mirror, ${\lambda}/4$ plate in front of target object, polarizing beam splitter, polarizer in front of image sensor, polarizer in front of the fiber coupled light source, ${\lambda}/2$ plate between PBS and polarizer of the light source. Using the proposed system, we can solve the problem of fringe image with low contrast by using polarization technique. Also, we can control light distribution of object beam and reference beam. 2) the signal processing acceleration method is proposed for PFSI, based on parallel processing architecture, which consists of parallel processing hardware and software such as Graphic Processing Unit(GPU) and Compute Unified Device Architecture(CUDA). As a result, the processing time reaches into tact time level of real-time processing. Finally, the proposed system is evaluated in terms of accuracy and processing speed through a series of experiment and the obtained results show the effectiveness of the proposed system and method.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..