• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.40-46
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• 2012

Purpose : To investigate the feasibility of in vivo proton magnetic resonance spectroscopy (MRS) for evaluation of lung cancer. Materials and Methods: This prospective study was approved by the institutional review board of our hospital and informed consent was obtained in all patients. Ten patients (7 men, 3 women; mean age, 64.4) with pathologicallyproven lung cancer (mean, 56.8 mm; range, 44-77 mm) were enrolled to 1.5 T MRS using a single-voxel respiration-triggered point-resolved spectroscopic sequence. Technical success rate and the reason of technical failure, if any, were investigated. Results: Out of 10 lung cancers, analyzable MRS spectra were obtained in 8 tumors (technical success rate, 80%). Two MRS datasets were not able to be analyzed due to serious baseline distortion. Choline and lipid signals were detected as major metabolites in analyzable MRS spectra. Conclusion: In vivo proton MRS method using a single-voxel respiration-triggered point-resolved spectroscopic sequence is feasible in obtaining the MR spectra of lung cancer because these spectra were analyzable and high success rate was shown in our study although there was the limitation of small patient group.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.28-41
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• 2017

In this paper, the fast encoding algorithm in High Efficiency Video Coding (HEVC) encoder was studied. For the encoding efficiency, the current HEVC reference software is divided the input image into Coding Tree Unit (CTU). then, it should be re-divided into CU up to maximum depth in form of quad-tree for RDO (Rate-Distortion Optimization) in encoding precess. But, it is one of the reason why complexity is high in the encoding precess. In this paper, to reduce the high complexity in the encoding process, it proposed the method by determining the maximum depth of the CU using a hierarchical clustering at the pre-processing. The hierarchical clustering results represented an average combination of motion vectors (MV) on neighboring blocks. Experimental results showed that the proposed method could achieve an average of 16% time saving with minimal BD-rate loss at 1080p video resolution. When combined the previous fast algorithm, the proposed method could achieve an average 45.13% time saving with 1.84% BD-rate loss.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.413-421
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• 2008

The paper derives asymptotic formulas for the MSE distortion and the signal-to-noise ratio of a mismatched fixed-rate minimum MSE Laplacian quantizer. These closed-form formulas are expressed in terms of the number N of quantization points, the mean displacement $\mu$, and the ratio $\rho$ of the standard deviation of the source to that for which the quantizer is optimally designed. Numerical results show that the principal formula is accurate in that, for rate R=$log_2N{\geq}6$, it predicts signal-to-noise ratios within 1% of the true values for a wide range of $\mu$, and $\rho$. The new findings herein include the fact that, for heavy variance mismatch of ${\rho}>3/2$, the signal-to-noise ratio increases at the rate of $9/\rho$ dB/bit, which is slower than the usual 6 dB/bit, and the fact that an optimal uniform quantizer, though optimally designed, is slightly more than critically mismatched to the source. It is also found that signal-to-noise ratio loss due to $\mu$ is moderate. The derived formulas can be useful in quantization of speech or music signals, which are modeled well as Laplacian sources and have changing short-term variances.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.

• Phonetics and Speech Sciences
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• v.13 no.2
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• pp.57-66
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• 2021

This study focuses on the issue of automatic severity classification of dysarthric speakers based on speech intelligibility. Speech intelligibility is a complex measure that is affected by the features of multiple speech dimensions. However, most previous studies are restricted to using features from a single speech dimension. To effectively capture the characteristics of the speech disorder, we extracted features of multiple speech dimensions: voice quality, prosody, and pronunciation. Voice quality consists of jitter, shimmer, Harmonic to Noise Ratio (HNR), number of voice breaks, and degree of voice breaks. Prosody includes speech rate (total duration, speech duration, speaking rate, articulation rate), pitch (F0 mean/std/min/max/med/25quartile/75 quartile), and rhythm (%V, deltas, Varcos, rPVIs, nPVIs). Pronunciation contains Percentage of Correct Phonemes (Percentage of Correct Consonants/Vowels/Total phonemes) and degree of vowel distortion (Vowel Space Area, Formant Centralized Ratio, Vowel Articulatory Index, F2-Ratio). Experiments were conducted using various feature combinations. The experimental results indicate that using features from all three speech dimensions gives the best result, with a 80.15 F1-score, compared to using features from just one or two speech dimensions. The result implies voice quality, prosody, and pronunciation features should all be considered in automatic severity classification of dysarthria.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.454-464
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• 2023

This paper proposes a low-power 8-bit asynchronous SAR ADC with a sampling rate of 1 MS/s for sensor node applications. The ADC uses bootstrapped switches to improve linearity and applies a VCM-based CDAC switching technique to reduce the power consumption and area of the DAC. Conventional synchronous SAR ADCs that operate in synchronization with an external clock suffer from high power consumption due to the use of a clock faster than the sampling rate, which can be overcome by using an asynchronous SAR ADC structure that handles internal comparisons in an asynchronous manner. In addition, the SAR logic is designed using dynamic logic circuits to reduce the large digital power consumption that occurs in low resolution ADC designs. The proposed ADC was simulated in a 180-nm CMOS process, and at a 1.8 V supply voltage and a sampling rate of 1 MS/s, it consumed 46.06 𝜇W of power, achieved an SNDR of 49.76 dB and an ENOB of 7.9738 bits, and obtained a FoM of 183.2 fJ/conv-step. The simulated DNL and INL are +0.186/-0.157 LSB and +0.111/-0.169 LSB.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.830-841
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• 2000

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at iteration a upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RL algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the (B+1)times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1101-1108
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• 2012

In this paper, we proposed an algorithm for real-time lane detecting against luminance variation using morphological image processing and edge-based region segmentation. In order to apply the most appropriate threshold value, the adaptive threshold was used in every frame, and perspective transform was applied to correct image distortion. After that, we designated ROI for detecting the only lane and established standard to limit region of ROI. We compared performance about the accuracy and speed when we used morphological method and do not used. Experimental result showed that the proposed algorithm improved the accuracy to 98.8% of detection rate and speed of 36.72ms per frame with the morphological method.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.61-74
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• 2004

In this paper, a contort- and speaker-dependent cepstrum extraction method and a channel normalization method for minimizing the loss of speaker characteristics in the cepstrum were proposed for a robust speaker recognition system over the channel. The proposed extraction method creates a cepstrum based on the pitch synchronous analysis using the inherent pitch of the speaker. Therefore, the cepstrum called the 〃pitch synchronous cepstrum〃 (PSC) represents the impulse response of the vocal tract more accurately in voiced speech. And the PSC can compensate for channel distortion because the pitch is more robust in a channel environment than the spectrum of speech. And the proposed channel normalization method, the 〃formant-broadened pitch synchronous CMS〃 (FBPSCMS), applies the Formant-Broadened CMS to the PSC and improves the accuracy of the intraframe processing. We compared the text-independent closed-set speaker identification on 56 females and 112 males using TIMIT and NTIMIT database, respectively. The results show that pitch synchronous km improves the error reduction rate by up to 7.7% in comparison with conventional short-time cepstrum and the error rates of the FBPSCMS are more stable and lower than those of pole-filtered CMS.