• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7451-7457
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• 2013

Background: Understanding and enhancing change capabilities, including Practice Adaptive Reserve (PAR), of Community Health Centers (CHCs) may mitigate cancer-related health disparities. Materials and Methods: Using stratified random sampling, we recruited 232 staff from seven CHCs serving Asian Pacific Islander communities to complete a self-administered survey. We performed multilevel regression analyses to examine PAR composite scores by CHC, position type, and number of years worked at their clinic. Results: The mean PAR score was 0.7 (s.d. 0.14). Higher scores were associated with a greater perceived likelihood that clinic staff would participate in an evidence-based intervention (EBI). Constructs such as communication, clinic flow, sensemaking, change valence, and resource availability were positively associated with EBI implementation or trended toward significance. Conclusions: PAR scores are positively associated with perceived likelihood of clinic staff participation in cancer screening EBI. Future research is needed to determine PAR levels most conducive to implementing change and to developing interventions that enhance Adaptive Reserve.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.502-504
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• 1995

In video image compression, it is important to extract the exact notion information from image sequence in order to perform the data compression, the field rate conversion, and the motion compensated interpolation effectively. It is well known that the location of the smallest sum of absolute difference(SAD) does not always give the true motion vector(MV) since the MV obtained via full block search is often corrupted by noise. In this paper, we first classifies the input blocks into 3 categories : the background, the shade-motion, and the edge-motion. According to the characteristics of the classified blocks, multiple locations of relatively small SAD are searched with an adaptive search window by using the proposed method. The proposed method picks MVs among those candidates by using temporal correlation. Since temporal correlation reveals the noise level in a particular region of the video image sequence, we are able to reduce the search are very effectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.718-723
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• 2013

In general, the boundary portion of the background and objects are the rapidly changing point and an important elements to analyze characteristics of image. Using these boundary parts, information about the position or shape of an object in the image are detected, and many studies have been continued in order to detect it. Existing methods are that implementation of algorithm is comparatively simple and its processing speed is fast, but edge detection characteristics is insufficient because weighted values are applied to all the pixels equally. Therefore, in this paper, we proposed an algorithm using region segmentation of the mask in order to adaptive edge detection according to image, and the results processed by proposed algorithm indicated superior edge detection characteristics in edge area.

• Progress in Superconductivity
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• v.4 no.1
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• pp.53-58
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• 2002

Noise from electronic instrumentation is invariably present in biomedical signals, although the art of instrumentation design is such that this noise source may be negligible. And sometimes signals of interest are contaminated or degraded by signals of similar type from another source. Biomedical signals are omni-presently contaminated by these background noises that span nearly all frequency bandwidths. In the magneto-cardiogram (MCG), several digital filters have been designed for the elimination of the power-line interference, broadband white noise, surrounding magnetic noise, and baseline wondering. In addition to the introduced FIR filter, notch, adaptive filter using the least mean square (LMS) algorithm, and recurrent neural network (RNN) filter, a new filtering method for effective noise canceling in MCG signals is proposed in this paper, which is realized by the wavelet packets. The experimental results show that the proposed filter using wavelet packet performs efficiently with respect to noise rejection. To verify this, two characteristics were analyzed and compared with LMS adaptive filter, SNR of filtered signal and attractor pattern using the nonlinear dynamics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1161-1168
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• 2003

In this paper, we propose an improved statistical model-based voice activity detection algorithm and threshold update method. We first improve signal-to-noise ratio by using speech enhancement preprocessing algorithm combined power subtraction method and matched filter, then apply it to LLR test optimum decision rule for improving the performance even in low SNR conditions. And we propose an adaptive threshold update method that was not concerned in any papers. We also perform extensive computer simulations to demonstrate the performance improvement of the proposed VAD algorithm employing the proposed speech enhancement preprocessing algorithm and adaptive threshold update method under various background noise environments. Finally we verify our results by comparing ITU-T G.729 Annex B.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.19-26
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• 2016

Among the various services for mobile banking, user authentication method using bank security card is still very useful. We can use mobile banking easily and safely in case of saving encoded security codes in smart phone and entering codes automatically whenever user authentication is required without bank security card. In this paper automatic recognition algorithm of security codes of bank security card is proposed in oder to enroll the encoded security codes into smart phone using smart phone camera. Advanced adaptive binarization is used for extracting digit segments from various background image pattern and adaptive 2-dimensional layout analysis method is developed for segmentation and recognition of damaged or touched digits. Experimental results of proposed algorithm using Android and iPhone, show excellent security code recognition results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.88-94
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• 2009

This paper proposes real time object detection and tracking algorithm that can be applied to security and supervisory system field. A proposed system is devide into object detection phase and object tracking phase. In object detection, we suggest Adaptive background subtraction method and Adaptive block based model which are advanced motion detecting methods to detect exact object motions. In object tracking, we design a multiple vehicle tracking system based on Kalman filtering. As a result of experiment, motion of moving object can be estimated. the result of tracking multipul object was not lost and object was tracked correctly. Also, we obtained improved result from long range detection and tracking.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.3
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• pp.110-117
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• 2014

This paper reports a depth-adaptive sharpness adjustment algorithm for stereoscopic perception improvement, and presents its field-programmable gate array (FPGA) implementation results. The first step of the proposed algorithm was to estimate the depth information of an input stereo video on a block basis. Second, the objects in the input video were segmented according to their depths. Third, the sharpness of the foreground objects was enhanced and that of the background was maintained or weakened. This paper proposes a new sharpness enhancement algorithm to suppress visually annoying artifacts, such as jagging and halos. The simulation results show that the proposed algorithm can improve stereoscopic perception without intentional depth adjustments. In addition, the hardware architecture of the proposed algorithm was designed and implemented on a general-purpose FPGA board. Real-time processing for full high-definition stereo videos was accomplished using 30,278 look-up tables, 24,553 registers, and 1,794,297 bits of memory at an operating frequency of 200MHz.

• 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.