• The Journal of the Korea Contents Association
• /
• v.14 no.12
• /
• pp.854-862
• /
• 2014

Attention deficit hyperactivity disorder (ADHD) students' intellctual defects, learning problems, and poor academic achievements seem to be due to significantly lower intelligence compared to the normal students, but rather the characteristic of inability to pay attention at a given time can be seen as the more attributing reason. In this study, a comparison between the ADHD students and the normal students will be performed using a fMRI analysis in order to differentiate the brain function between the two groups during a working memory task performance and to assess the difference in the activated regions of the brain. Clinical survey examinations and fMRI measurements were performed for a group of 26 elementary students from the Incheon area. The stimulus of fMRI was a working memory. Cartography statistically analyzed parameters and the Statistical Package of Social Sciences using single-sample t-test, two-sample t-test, were analyzed by multiple regression analysis, the statistical significance level was p<0.05 in, respectively. The disproportionate developments could be seen in the ADHD students group such as the frontal cortex, parietal cortex, thalamus, and caudate nucleus, among others. In addition, as some students felt the increase in the difficulty of working memory task performance, the orbitofrontal cortex and the hippocampus were activated, which seems to be the result of an effort for looking for an answer. More types of ADHD students needs to be secured as research subjects, and more stimulations for fMRI experiments should be considered as it would be useful in the overall evaluation of brain function.

• The Korean Journal of Nuclear Medicine
• /
• v.36 no.3
• /
• pp.166-176
• /
• 2002

Purpose: The purpose of this study is to investigate the effects of visual activation and quantitative analysis of regional cerebral blood flow. Visual activation was known to increase regional cerebral blood flow in the visual cortex in occipital lobe. We evaluated that change in the distribution of $^{99m}Tc-HMPAO$ (Hexamethyl propylene amine oxime) to reflect in regional cerebral blood flow. Materials and Methods: The six volunteers were injected with 925 MBq (mean ages: 26.75 years, n=6, 3men, 3women) underwent MRI and $^{99m}Tc-HMPAO$ SPECT during a rest state with closed eyes and visual stimulated with 8 Hz LED. We delineate the legion of interest and calculated the mean count per voxel in each of the fifteen slices to quantitative analysis. The ROI to whole brain ratio and regional index was calculated pixel to pixel subtraction visual non-activation image from visual activation image and constructed brain map using a statistical parameter map (SPM99). Results: The mean regional cerebral blood flow was increased due to visual stimulation. The increase rate of the mean regional cerebral blood flow which of the activation region in primary visual cortex of occipital lobe was $32.50{\pm}5.67%$. The significant activation sites using a statistical parameter of brain constructed a rendering image and image fusion with SPECT and MRI. Conclusion: Visual activation was revealed significant increase through quantitative analysis in visual cortex. Activation region was certified in Talairach coordinate and primary visual cortex (Ba17),visual association area (Ba18,19) of Brodmann.

• Progress in Medical Physics
• /
• v.14 no.2
• /
• pp.108-123
• /
• 2003

The purpose of this study is to evaluate the contribution of $^{18}$ F-FDG brain PET in the differentiating Idiopathic parkinson's diesease (IPD), progressive supranuclear palsy (PSP), and multiple system atrophy (MSA). We studied 24 patients with parkinsonism : 8 patients (mean age 67.9$\pm$10.7 y: M/F : 3/5) with IPD, 9 patients (57.9$\pm$9.2 y : M/F : 4/5) with MSA and 7 patients (67.6$\pm$4.8 y : M/F 3/4) with PSP. All patients with parkinsonism and 22 age-matched normal controls underwent $^{18}$ F FDG PET in 3D mode after the injection of 370 MBq $^{118}$ F FDG. The patients with IPD, MSh and PSP were compared with a normal control group by a two-sided t-test of SPM99 (uncorrected P<0.001, extent threshold>100 voxel). All three parkinsonism groups, showed significant hypometabolism in the cerebral neocortex compared to the normal control group. However, the three groups displayed different metabolism in the subcortical structure, brain stem, and cerebellum. In IPD, there was no significant hypometabolism in the putamen, brain stem and cerebellum. However, MSA patients showed significant hypometabolism in the striatum, pons, and cerebellum compared to the normal controls and IPD patients. In addition, PSP showed significant hypometabolism in the caudate nuclei, the thalamus, midbrain, and the cingulate gyrus compared to the normal controls, the IPD, and MSA groups (IPD vs Normal sensitivity/specificity : 75%/l00%, MSA vs Normal sensitivity/specificity :100%/87%, PSP vs Normal sensitivity/specificity : 86%/94%). Our results show that the regional metabolism of IPD, MSA, and PSP is different mainly in the striatum, thalamus, brain stem and cerebellum. An assessment of the $^{18}$ F-FDG PET scan images using SPM may be a useful adjunct to a clinical examination in making a differential diagnosis of Parkinsonism.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.4
• /
• pp.30-39
• /
• 2003

In this paper, we carried out the performance evaluation of HM-Net(Hidden Markov Network) speech recognition system for Korean speech databases. We adopted to construct acoustic models using the HM-Nets modified by HMMs(Hidden Markov Models), which are widely used as the statistical modeling methods. HM-Nets are carried out the state splitting for contextual and temporal domain by PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) algorithm, which is modified the original SSS algorithm. Especially it adopted the phonetic decision tree to effectively express the context information not appear in training speech data on contextual domain state splitting. In case of temporal domain state splitting, to effectively represent information of each phoneme maintenance in the state splitting is carried out, and then the optimal model network of triphone types are constructed by in the parameter. Speech recognition was performed using the one-pass Viterbi beam search algorithm with phone-pair/word-pair grammar for phoneme/word recognition, respectively and using the multi-pass search algorithm with n-gram language models for sentence recognition. The tree-structured lexicon was used in order to decrease the number of nodes by sharing the same prefixes among words. In this paper, the performance evaluation of HM-Net speech recognition system is carried out for various recognition conditions. Through the experiments, we verified that it has very superior recognition performance compared with the previous introduced recognition system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.3
• /
• pp.348-356
• /
• 2014

The existing effective path-length model of ITU-R has some drawbacks: The prediction error is quite large compared to domestic measurement data and it is an empirical model in which the physical characteristics of rain cells are not considered. In this paper, a theoretical model for effective path-length using the rain-cell concept was proposed and its validity was verified using the measurement data. To analyze the statistical characteristics of rain cell parameters, the weather-radar data(CAPPI) measured by Korea Meterological Administration were analyzed and the correction factor was properly introduced to fit the Chollian beacon measurement data of ETRI(Electronics and Telecommunications Research Institute). To verify the proposed effective path-length model, it was compared with the Mugunghwa No. 5 beacon data measured in Chungnam National University with the support of ADD(Agency for Defense Development). It was confirmed that the prediction results of the proposed model are in good agreement with the measurement data.

• Journal of Broadcast Engineering
• /
• v.13 no.5
• /
• pp.732-743
• /
• 2008

In H.264/AVC, 4$\times$4 block transform is used for intra and inter prediction instead of 8$\times$8 block transform. Using small block size coding, H.264/AVC obtains high temporal prediction efficiency, however, it has limitation in utilizing spatial redundancy. Motivated on these points, we propose a multi-dimensional transform which achieves both the accuracy of temporal prediction as well as effective use of spatial redundancy. From preliminary experiments, the proposed multi-dimensional transform achieves higher energy compaction than 2-D DCT used in H.264. We designed an integer-based transform and quantization coder for multi-dimensional coder. Moreover, several additional methods for multi-dimensional coder are proposed, which are cube forming, scan order, mode decision and updating parameters. The Context-based Adaptive Variable-Length Coding (CAVLC) used in H.264 was employed for the entropy coder. Simulation results show that the performance of the multi-dimensional codec appears similar to that of H.264 in lower bit rates although the rate-distortion curves of the multi-dimensional DCT measured by entropy and the number of non-zero coefficients show remarkably higher performance than those of H.264/AVC. This implies that more efficient entropy coder optimized to the statistics of multi-dimensional DCT coefficients and rate-distortion operation are needed to take full advantage of the multi-dimensional DCT. There remains many issues and future works about multi-dimensional coder to improve coding efficiency over H.264/AVC.

• Journal of Broadcast Engineering
• /
• v.22 no.3
• /
• pp.295-312
• /
• 2017

Digital video content is the most information-intensive and high-value content. Therefore, it is necessary to protect the intellectual property rights of these contents, and this paper also proposes a watermarking method of digital image for this purpose. The proposed method uses the frequency characteristics of 2-Dimensional Discrete Wavelet Transform (2D-DWT) for digital images and digital watermark on global data without using local or specific data of the image for watermark embedding. The method to insert digital watermark data uses a simple Quantization Index Modulation (QIM) and a multiple watermarking method that inserts the same watermark data in multiple. When extracting a watermark, multiple watermarks are extracted and the final watermark data is determined by a simple statistical method. This method is an empirical method for experimentally determining the parameters in the watermark embedding process. The proposed method performs experiments on various images against various attacks and shows the superiority of the proposed method by comparing the performance with the representative existing methods.

• Phonetics and Speech Sciences
• /
• v.12 no.3
• /
• pp.65-71
• /
• 2020

Feature normalization is a method to reduce the effect of environmental mismatch between the training and test conditions through the normalization of statistical characteristics of acoustic feature parameters. It demonstrates excellent performance improvement in the traditional Gaussian mixture model-hidden Markov model (GMM-HMM)-based speech recognition system. However, in a deep neural network (DNN)-based speech recognition system, minimizing the effects of environmental mismatch does not necessarily lead to the best performance improvement. In this paper, we attribute the cause of this phenomenon to information loss due to excessive feature normalization. We investigate whether there is a feature normalization method that maximizes the speech recognition performance by properly reducing the impact of environmental mismatch, while preserving useful information for training acoustic models. To this end, we introduce the mean and exponentiated variance normalization (MEVN), which is a compromise between the mean normalization (MN) and the mean and variance normalization (MVN), and compare the performance of DNN-based speech recognition system in noisy and reverberant environments according to the degree of variance normalization. Experimental results reveal that a slight performance improvement is obtained with the MEVN over the MN and the MVN, depending on the degree of variance normalization.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.6
• /
• pp.634-638
• /
• 2018

Haze is a factor that degrades the performance of various image processing algorithms, such as those for detection, tracking, and recognition using an electro-optical sensor. For robust operation of an electro-optical sensor-based unmanned system used outdoors, an algorithm capable of effectively removing haze is needed. As a haze removal method using a single electro-optical sensor, the dark channel prior using statistical properties of the electro-optical sensor is most widely known. Previous methods used a square filter in the process of obtaining a transmission using the dark channel prior. When a square filter is used, the effect of removing haze becomes smaller as the size of the filter becomes larger. When the size of the filter becomes excessively small, over-saturation occurs, and color information in the image is lost. Since the size of the filter greatly affects the performance of the algorithm, a relatively large filter is generally used, or a small filter is used so that no over-saturation occurs, depending on the image. In this paper, we propose an improved haze removal method using color image segmentation. The parameters of the color image segmentation are automatically set according to the information complexity of the image, and the over-saturation phenomenon does not occur by estimating the amount of transmission based on the parameters.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.2
• /
• pp.272-278
• /
• 2015

This paper analyzed interference impact between wireless headset and Wireless Fidelity which get a high possibility of simultaneously operating in the Industry Science Medical(ISM) bands. Analysis methods of Minimum Coupling Loss(MCL) and Monte Carlo(MC) were applied by using the characters and parameters referenced to Institute of Electrical and Electronic Engineers(IEEE) 802.11b. The protection distance between interferer (WiFi transmitter) and victim (Wireless headset receiver) was calculated through the MCL. The protection distance was obtained for the case of single interferer, and the maximum allowable number of multiple interferers was computed according to the cell radius of a victim system to meet 5 % below of interference probability by using a statistical analysis based on the MC method. The analysis results are expected to be used as a guideline to coexist the wireless headset and low power devices in the ISM band.