• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.583-586
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• 2004

Transition jitter noise is one of major sources of detection errors in high density recording channels. Implementation complexity of the optimal detector for such channels is high due to the data dependency and correlated nature of the jitter noise. In this paper, two types of hardware efficient sub-optimal detectors are derived by modifying branch metric of Viterbi algorithm and applied to partial response (PR) channels combined with run length limited modulation coding. The additional complexity over the conventional Viterbi algorithm to incorporate the modified branch metric is either a multiplication or an addition for each branch metric in the Viterbi trellis.

• Smart Media Journal
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• v.4 no.1
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• pp.39-43
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• 2015

A vehicle black box helps to investigate the cause of accident by recording time, and videos as wells as shock information of the time of accident Lately, intelligent black box with accident prevention as well as existing functions is being studied. This paper proposes an applicable algorithm for vehicle black boxes that prevent any accident likely to occur while a car is parked, like robbery, theft or hit-and-run. Proposed algorithm provides object recognition, face detection and alarm as the object approaches car. Tests on the algorithm prove that it can recognize an approaching object, identify and set alarm if needed, depending on each risk level.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.261-268
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• 1998

Electrogastrography(EGG), the cutaneous recording of the myoelectrical activity of the stomach using surface electrodes, is a non-invasive technique to detect gastric motility disorder, We developed a detection and analysis system for the EGG signal, which consists of hardware(bio-amplifier, filter) and softwere(user interface, analysis algorithm, patient database). The EGG signal was amplified and filtered by 3 channel bio-amplifiers, and simultaneously digitized and stored on IBM PC with a sampling frequency of 16 Hz. The stored EGG signal was analyzed using developed algorithm to extract clinically useful information from the signal. The developed system has tested through animal experiments, and is under clinical evaluation.

• Journal of The Korean Association of Information Education
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• v.22 no.5
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• pp.605-611
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• 2018

On-Line Lectures are becoming more common due to the MOOK service and the expansion of national policy in Korea. Especially, It is being changed to new remote mixed style from traditional lecture in universities. We propose and implement a remote contents making tool with audio synchronization function based on more with less resources. To implement our proposed algorithm, we design an interactive interface to assign multiple cutting intervals and convert an input video to print a new result. In experimental, we can confirm our algorithm works properly with average performance value 9.3% cpu share ratio and 87mega byte ram usage(CPU 2.60GHz, 820*600 Area).

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Journal of Conservation Science
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• v.38 no.2
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• pp.154-165
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• 2022

The color appearance of cultural heritage are essential factors for manufacturing technique interpretation, conservation treatment usage, and condition monitoring. Therefore, this study systematically established color reproduction procedures based on the digital color management system for the portrait of Gwon Eungsu. Moreover, various application strategies for recording and conserving the cultural heritage were proposed. Overall color reproduction processes were conducted in the following order: photography condition setting, standard color measurements, digital photography, color correction, and color space creation. Therefore, compared with the color appearance, the digital image applied to a camera maker profile indicated an average color difference of 𝜟10.1. However, the digital reproduction result based on the color management system exhibits an average color difference of 𝜟1.1, which is close to the color appearance. This means that although digital photography conditions are optimized, recording the color appearance is difficult when relying on the correction algorithm developed by the camera maker. Therefore, the digital color reproduction of cultural heritage is required through color correction and color space creation based on the raw digital image, which is a crucial process for documenting the color appearance. Additionally, the recording of color appearance through digital color reproduction is important for condition evaluation, conservation treatment, and restoration of cultural heritage. Furthermore, standard data of imaging analysis are available for discoloration monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.2942-2950
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• 2010

Car black box helps to investigate the cause of accident by recording time, position and videos as well as shock information. In addition, the car black box need a function to support safe driving for preventing accident. The representative driving support function is a lane departure warning. In order to implement the function, it is necessary to carry out the image processing to detect the lane first. The image processing algorithm requires computational burden to handle so much data and complicated structure of algorithm. This paper describes the efficient image processing algorithm with relatively low amount of computation for car black box embedded platform to detect lanes from the real-time lane image.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.645-661
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• 2012

Recently, pirates are infesting on the sea and they have been hijacking the several vessels for example Samho Dream and Samho Jewelry of Korea. One of the items to reduce the risk is to adopt the invader detection system. If the pirates break in to the ship, the detection system can monitor the pirates and then call the security alarm. The crew can gain time to hide to the safe room and the report can be automatically sent to the control room to cope with the situation. For the invader detection, an unmanned observation system was proposed using the image detection algorithm that extracts the invader image from the recording image. To detect the motion area, the difference value was calculated between the current image and the prior image of the invader, and the 'AND' operator was used in calculated image and edge line. The image noise was reduced based on the morphology operation and then the image was transformed into morphological information. Finally, a neural network model was applied to recognize the invader. In the experimental results, it was confirmed that the proposed approach can improve the performance of the recognition in the invader monitoring system.

• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.275-279
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• 2015

There are several methods to record three dimensional (3D) information of objects such as lens array based integral imaging, synthetic aperture integral imaging (SAII), computer synthesized integral imaging (CSII), axially distributed image sensing (ADS), and axially distributed stereo image sensing (ADSS). ADSS method is capable of recording partially occluded 3D objects and reconstructing high-resolution slice plane images. In this paper, we present a computational method for depth extraction of partially occluded 3D objects using ADSS. In the proposed method, the high resolution elemental stereo image pairs are recorded by simply moving the stereo camera along the optical axis and the recorded elemental image pairs are used to reconstruct 3D slice images using the computational reconstruction algorithm. To extract depth information of partially occluded 3D object, we utilize the edge enhancement and simple block matching algorithm between two reconstructed slice image pair. To demonstrate the proposed method, we carry out the preliminary experiments and the results are presented.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.199-205
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• 2005

When recording single-unit responses from neural systems, a common problem is the accurate detection of spikes (action potentials) in the presence of competing unwanted (noise) signals. While some sources of noise can be readily dealt with through filtering or 'template subtraction' techniques, other sources present a more difficult problem. In particular, noise components introduced by power supplies, which contain harmonics of the power-line frequency, can be particularly troublesome in that they can mimic the shape of the desired spikes. Thus, standard 'template subtraction' techniques or notch-filtering approaches are not appropriate. In this study, we propose the use of a novel template-subtraction scheme that involves estimating the power-line noise waveform and using cross-correlation techniques to subtract them from the recordings. This technique requires two key steps: (1) cross-correlation analysis of each recorded waveform extracts a robust representation of the power-line noise waveform and (2) a second level of cross-correlation to successfully subtract that representation from each recorded waveform. This paper describes this algorithm and provides examples of its implementation using actual recorded waveforms that are contaminated with these noise signals. An improvement (reduction) in the noise level is reported, as are suggestions for future implementation of this strategy.