Signal to Noise Ratio of MR Spectrum by variation echo time : comparison of 1.5T and 3.0T (original) (raw)
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Effects of 3D Topography on Magnetotelluric Responses
2007
For precise interpretation of magnetotelluric (MT) data distorted by irregular surface terrain, topography effects are investigated by computing apparent resistivities, phases, tippers and induction vectors for a three-dimensional (3D) hill-and-valley model. To compute MT responses for the 3D surface topography model, we use a 3D MT modeling algorithm based on an edge finite-element method which is free from vector parasites. Distortions on the apparent resistivity and phase are mainly caused by distorted currents that flow along surface topography. The distribution of tipper amplitudes over both hill and valley are the same, while the tipper points toward the center of hill and the base of the valley. The real part of induction vector also points in the same direction as that of tipper, while the imaginary part in the opposite direction.
베이즈 분류기를 이용한 수중 배경소음하의 과도신호 분류
Journal of Ocean Engineering and Technology, 2012
In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using 16 th order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.
Classification of Transient Signals in Ocean Background Noise Using Bayesian Classifier
Journal of Ocean Engineering and Technology, 2012
In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using 16 th order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.
Journal of the Korean Society of Magnetic Resonance in Medicine, 2013
To investigate the correlations between Seoul Neuropsychological Screening Battery (SNSB) scores and the gray matter volumes (GMV) in patients with Alzheimer' s disease (AD) and mild cognitive impairment (MCI) and cognitively normal (CN) elderly subjects with correcting the genotypes. Materials and Methods: Total 75 subjects were enrolled with 25 subjects for each group. The apolipoprotein E (APOE) epsilon genotypes, SNSB scores, and the 3D T1-weighted images were obtained from all subjects. Correlations between SNSB scores and GMV were investigated with the multiple regression method for each subject group using both voxel-based and region-of-interest-based analyses with covariates of age, gender, and the genotype. Results: In the AD group, Rey Complex Figure Test (RCFT) delayed recall scores were positively correlated with GMV. In the MCI group, Seoul Verbal Learning Test (SVLT) scores were positively correlated with GMV. In the CN group, GMV negatively correlated with Boston Naming Test (K-BNT) scores and Mini-Mental State Examimation (K-MMSE) scores, but positively correlated with RCFT scores. Conclusion: When we used covariates of age, gender, and the genotype, we found statistically significant correlations between some SNSB scores and GMV at some brain regions. It may be necessary to further investigate a longitudinal study to understand the correlation.
The Journal of the Acoustical Society of Korea, 2012
An underwater transient signal is distinguished from an ambient noise. Database for the underwater transient signal is required since the underwater transient signal shows various characteristics depending on acoustic features. In the paper, hence, sound mask-filter was applied to extract the transient signals which exist temporally and locally in the ocean. The standard signal was chosen and cross-correlated with the raw signal. A mask-filter for a transient signal was obtained using the threshold which was decided by the maximum likelihood method in the envelope of the cross-correlated signal. Using the sound mask-filter, the transient signal of a sea catfish {Galeichthys felis (Linnaeus)} was extracted from the underwater ambient noise. Similarly, the man-made signal was added into the noise and it was extracted by the same method. We also have demonstrated the significance of the transient signal through comparing the extracted signals depending on the standard signal. In the results, the proposed method, sound mask-filtering, could be utilized as a database construction of the transient signals in underwater noise. Particularly, this study would be useful to extract the wanted signal from arbitrary signals.
Parametric Array Signal Generating System using Transducer Array
The Journal of the Acoustical Society of Korea, 2013
We present a parametric array signal generating system using 3×16 transducer array which is composed of multi-resonant frequency transducers of 20kHz and 32.5kHz. To drive transducer array, sixteen channel amplifier using LM1875 chips is designed and implemented, and the PXI system based on the LabView 8.6 for arbitrary signal generation and analysis is used. Using the proposed system, we measure sound pressure level and beam pattern of difference frequency and verify the nonlinear effect of difference frequency. The theoretical absorption range and the Rayleigh distance are 15.51m and 1.933m, respectively and we verify that sound pressure of difference frequency is accumulated and increased at the near-field shorter than the Rayleigh distance. We verify that the beam pattern of the measured difference frequency and the beam pattern obtained by the superposition of two primary frequencies are similar, and high directional parametric signal was generated.
Microvariation between Tungusic and Mongolic vowel harmony
ALTAI HAKPO, 2011
This paper presents a contrastive hierarchy (Dresher 2009) analysis of the microvariation between Oroqen (a Tungusic language) and Khalkha Mongolian (a Mongolic language) vowel harmony. Although these two languages have the same type of vowel harmony processes, namely tongue root harmony and labial harmony, there is a minimal contrast between the two with respect to the phonological behavior of the vowel /i/: Tungusic /i/ is opaque whereas Mongolic /i/ is transparent to labial harmony (van der Hulst & Smith 1988). This microvariation can be modeled as the minimal difference in the language-specific contrastive hierarchy: Tungusic [low] > [coronal] > [labial] > [RTR] (Zhang 1996) vs. Mongolic [coronal] > [low] > [labial] > [RTR] (Ko 2011). These minimally distinct hierarchies assign different output specifications for /i/, i.e., [−low, +cor] for Tungusic /i/ and simply [+cor] for Mongolic /i/, which explains the transparency of Mongolic /i/ to labial harmony assuming labial harmony in Tungusic and Mongolic as a 'height-stratified' harmony (Mester 1986).