• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.205-211
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• 2019

Steganalysis is a technique that aims to detect and recover data hidden by steganography. Steganalytic methods detect hidden data by analyzing visual and statistical distortions caused during data embedding. However, for recovering the hidden data, they need to know which steganographic methods the hidden data has been embedded by. Therefore, we propose a hierarchical convolutional neural network (CNN) structure that identifies a steganographic method applied to an input image through multi-level classification. We trained four base CNNs (each is a binary classifier that determines whether or not a steganographic method has been applied to an input image or which of two different steganographic methods has been applied to an input image) and connected them hierarchically. Experimental results demonstrate that the proposed hierarchical CNN structure can identify four different steganographic methods (LSB, PVD, WOW, and UNIWARD) with an accuracy of 79%.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.132-137
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• 2019

The amount of data generated from medical images is increasingly exceeding the limits of professional visual analysis, and the need for automated medical image analysis is increasing. For this reason, this study evaluated the classification and accuracy according to the presence or absence of tumor using Inception V3 deep learning model, using MRI medical images showing normal and tumor findings. As a result, the accuracy of the deep learning model was 90% for the training data set and 86% for the validation data set. The loss rate was 0.56 for the training data set and 1.28 for the validation data set. In future studies, it is necessary to secure the data of publicly available medical images to improve the performance of the deep learning model and to ensure the reliability of the evaluation, and to implement modeling by improving the accuracy of labeling through labeling classification.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.206-212
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• 2021

Society is developing more and more, and communication methods are developing in many ways. However, developed communication is a way for the non-disabled and has no effect on the deaf. Therefore, in this paper, a CNN-based sign language translation program is designed and implemented to help deaf people communicate. Sign language translation programs translate sign language images entered through WebCam according to meaning based on data. The sign language translation program uses 24,000 pieces of Korean vowel data produced directly and conducts U-Net segmentation to train effective classification models. In the implemented sign language translation program, 'ㅋ' showed the best performance among all sign language data with 97% accuracy and 99% F1-Score, while 'ㅣ' showed the highest performance among vowel data with 94% accuracy and 95.5% F1-Score.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4A
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• pp.364-370
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• 2002

In this paper, we propose a VLSI array architecture for high speed processing of FBMA. First of all, the sequential FBMA is transformed into a single assignment code by using the index space expansion, and then the dependance graph is obtained from it. The two dimensional VLSI array is derived by projecting the dependance graph along the optimal direction. Since the candidate blocks in the search range are overlapped with columns as well as rows, the processing elements of the VLSI array are designed to reuse the overlapped data. As the results, the number of data inputs is reduced so that the processing performance is improved. The proposed VLSI array has (N$^2$+1)${\times}$(2p+1) processing elements and (N+2p) input ports where N is the block size and p is the maximum search range. The computation time of the rat reference block is (N$^2$+2(p+1)N+6p), and the block pipeline period is (3N+4p-1).

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.1-6
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• 2000

In this paper, the hybrid system of HMM and neural network is proposed and show better recognition rate of the post-process procedure which minimizes the process error of recognition than that of HMM(Hidden Markov Model) only used. After the HMM training by training data, testing data that are not taken part in the training are sent to HMM. The output probability from HMM output by testing data is used for the training data of the neural network, post processor. After neural network training, the hybrid system is completed. This hybrid system makes the recognition rate improvement of about $4.5\%$ in MLP and about $2\%$ in RBFN and gives the solution to training time of conventional hybrid system and to decrease of the recognition rate due to the lack of training data in real-time speech recognition system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.113-118
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• 2023

The WiFi fingerprint method for location estimation within the home has the advantage of using the existing infrastructure and estimating absolute coordinates, so many studies are being conducted. Existing studies have mainly focused on the study of localization algorithms, but the improvement of accuracy has reached its limits. However, since a wireless LAN receiver such as a smartphone cannot measure signals smaller than the reception sensitivity of radio signals, the position estimation error varies depending on the method of processing these values. In this paper, we proposed a method to increase the location estimation accuracy by pre-processing the received signal data of the measured wireless LAN router in various ways and applying it to the existing algorithm, and greatly improved accuracy was obtained. In addition, the preprocessed data was applied to the KNN method and the CNN method and the performance was compared.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.188-195
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• 1999

The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.

• Journal of Navigation and Port Research
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• v.38 no.4
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• pp.391-397
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• 2014

Wide Area Differential GNSS(WA-DGNSS) was developed in order to improve the accuracy and integrity performance of GNSS. In this paper, overall structure of Pseudolite-Based Augmentation System(PBAS) and experimental methods which enables the post-processing test with commercial receiver will be described. For generating augmenting message, GPS measurement collected from five NDGPS reference stations were processed by reference station S/W and master station S/W. The accuracy of augmenting message was tested by comparing SP3, IONEX data. In the test, RF signal of user was collected and correction data were generated. After that, RF signal was broadcasted with pseudolite signal. Test was conducted using three commercial receiver and the performance was compared with MSAS and standalone user. From the position output of each receiver, it was shown that improved position was obtained by applying augmenting message.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.183-186
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• 2004

본 논문에서는 수중 초음파 통신용 QPSK 버스트 수신기를 DSP시스템을 이용하여 구현하기위한 시스템 설계에 대하여 논한다. 본 논문에서 고려하는 시스템은 25kHz의 반송주파수를 사용하고, 심벌율은 5kHz이며, 데이터 전송율은 10,000bps이다. 송신기에서 심벌정보를 전송하기 위해 펄스성형필터를 거친 신호를 디지털 믹서기를 이용하여 디지털 영역에서 반송주파수 대역으로 신호를 변조한 후 200kHz로 샘플링하는 D/A변환기를 이용하여 전송 아날로그 신호를 생성한다. 수신기에서는 수신 신호를 디지털로 처리하기 위하여 100kHz로 free running하는 A/D 변환기를 이용하여 수신 데이터를 얻는다. 수신기에서는 32심벌 길이의 프리앰블을 이용하여 프레임 동기를 찾음과 동시에 개략적인 심벌시간 동기와 위상편이를 추정한다. 추정한 위상편이값은 2차 PLL (phase-looked loop)의 초기값으로 사용하여 위상 추적을 수행하는 전송 시스템이다. 또한, 된 논문에서는 실해역 전송 시험 테이터를 통하여 조류의 변화에 의해 발생하는 Doppler 편이를 보상하기 위하여 PLL이 필수적으로 필요함을 보인다.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.82-88
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• 2012

In this paper, using the Android-based mobile platform designed and integrated U-healthcare systems for personal health care system is proposed. Integrated Biometric systems, electrocardiogram (ECG), oxygen saturation, blood pressure, respiration, body temperature, such as measuring vital signs throughout the module and signal processing biometric information through wireless communication module based on the Android mobile platform is transmitted to the gateway. Biometric data transmitted from a mobile health monitoring system, or transmitted to the server of U-healthcare was designed. By implementing vital signs monitoring system has been measured in vivo by monitoring data to determine current health status of caregivers had the advantage of being able to guarantee mobility respectively. This system is designed as personal health management and monitoring system for emergency patients will be helpful in the development looks U-healthcare system.