• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.209-210
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• 1998

Noise characteristics of preamplifier, the most essential part of on-chip signal processing circuitry for the active semiconductor neural probe, is the important factor determining the overall signal-to-noise-ratio (SNR). We present a systematic design method for the optimization of SNR, based on the spectral characteristics of the electrode, circuit noise and extracelluar action potential. Analytical expression is derived to calculate total output noise power. Output SNR of 2-stage CMOS preamplifier is tailored to meet the given specification while the layout area is minimized.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.52-57
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• 2024

To measure the vortex structures within the turbulent boundary layer, a hot-wire sensor was mounted on a stepper motor controller and moved to the designated measurement points. Near the surface within the flow field, the velocity is relatively slow, making the measurements highly sensitive to electromagnetic interference (EMI) during signal processing. This EMI primarily originates from the power supplies of computers and other electronic equipment. In our experimental setup, EMI was introduced into BNC cables connected to the hot-wire sensor from the powered stepper motor. When power was supplied to the motor controller to move the hot-wire sensor, EMI appeared on the oscilloscope screen. Consequently, unexpected noise was present in the data measured by the hot-wire sensor. To mitigate this and enhance the signal-to-noise ratio (SNR) during measurements, the connecting cables were shielded, and an old computer without EMI shielding was replaced.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2E
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• pp.62-68
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• 2003

This paper addresses a novel noise-compensation scheme to solve the mismatch problem between training and testing condition for the automatic speech recognition (ASR) system, specifically in car environment. The conventional spectral subtraction schemes rely on the signal-to-noise ratio (SNR) such that attenuation is imposed on that part of the spectrum that appears to have low SNR, and accentuation is made on that part of high SNR. However, these schemes are based on the postulation that the power spectrum of noise is in general at the lower level in magnitude than that of speech. Therefore, while such postulation is adequate for high SNR environment, it is grossly inadequate for low SNR scenarios such as that of car environment. This paper proposes an efficient spectral subtraction scheme focused specifically to low SNR noisy environment by extracting harmonics distinctively in speech spectrum. Representative experiments confirm the superior performance of the proposed method over conventional methods. The experiments are conducted using car noise-corrupted utterances of Aurora2 corpus.

• Journal of radiological science and technology
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• v.36 no.4
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• pp.273-280
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• 2013

This study was proven for the T-spine breathing technique in lateral projection, using computer radiography (CR), charge coupled device (CCD), indirect digital radiography (IDR) and direct digital radiography (DDR). All images were evaluated and compared with CNR and SNR measured with the mean pixels and the standard deviation as setting ROI of spinous process, pedicle, vertebral body, intervertebral foramen and intervertebral disk using Image J. In experiment results of 4 type detectors, T-spine breathing technique was indicated as excellent in ROI of spinous process, pedicle, vertebral body, intervertebral foramen and intervertebral disk. As T-spine breathing technique indicated excellent images compared to the existing T-spine lateral radiography, this method would be useful for elderly patients who have difficulty in deep exhalation. This study was indicated the application possibility of T-spine breathing technique by presenting contrast to noise ratio (CNR) and signal to noise ratio (SNR) with quantitative value in 4 type detectors.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 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.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.544-551
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• 2023

Speech enhancement used to improve the perceptual quality and intelligibility of noise speech has been studied as a method using a complex-valued spectrum that can improve both magnitude and phase in a method using a magnitude spectrum. In this paper, a study was conducted on how to apply attention mechanism to complex-valued spectrum-based speech enhancement systems to further improve the intelligibility and quality of noise speech. The attention is performed based on additive attention and allows the attention weight to be calculated in consideration of the complex-valued spectrum. In addition, the global average pooling was used to consider the importance of the feature map. Complex-valued spectrum-based speech enhancement was performed based on the Deep Complex U-Net (DCUNET) model, and additive attention was conducted based on the proposed method in the Attention U-Net model. The results of the experiments on noise speech in a living room environment showed that the proposed method is improved performance over the baseline model according to evaluation metrics such as Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short Time Object Intelligence (STOI), and consistently improved performance across various background noise environments and low Signal-to-Noise Ratio (SNR) conditions. Through this, the proposed speech enhancement system demonstrated its effectiveness in improving the intelligibility and quality of noisy speech.

• Current Optics and Photonics
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• v.6 no.6
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• pp.608-618
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• 2022

By analyzing the signal-to-noise ratio (SNR) theory of the conventional true color night vision system, we found that the output image SNR is limited by the wavelength range of the system response λ₁ and λ₂. Therefore, we built a double-channel four-band true color night vision system to expand the system response to improve the output image SNR. In the meantime, we proposed an image fusion method based on principal component analysis (PCA) and nonsubsampled shearlet transform (NSST) to obtain the true color night vision images. Through experiments, a method based on edge extraction of the targets and spatial dimension decorrelation was proposed to calculate the SNR of the obtained images and we calculated the correlation coefficient (CC) between the edge graphs of obtained and reference images. The results showed that the SNR of the images of four scenes obtained by our system were 125.0%, 145.8%, 86.0% and 51.8% higher, respectively, than that of the conventional tri-band system and CC was also higher, which demonstrated that our system can get true color images with better quality.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1756-1760
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• 2015

This paper focuses on a priori signal to noise ratio (SNR) estimation method for the speech enhancement. There are many researches for speech enhancement with several ambient noise cancellation methods. The method based on spectral subtraction (SS) which is widely used in noise reduction has a trade-off between the performance and the distortion of the signals. So the need of adaptive method like an estimated a priori SNR being able to making a high performance and low distortion is increasing. The decision directed (DD) approach is used to determine a priori SNR in noisy speech signals. A priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a modified a priori SNR estimator and the weighted rational transfer function for speech enhancement with wind noises. The experimental result shows the performance of our proposed estimator is better Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862) compare to the conventional DD approach-based systems and different noise reduction methods.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.422-429
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• 2013

Advances in wireless sensor network (WSN) technology have enabled small and low-cost sensors with the capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. In the WSN, the sensor nodes have a limited transmission range and their processing and storage capabilities as well as their energy resources are limited. A triple umpiring system has already been proved for its better performance in WSNs. The clustering technique is effective in prolonging the lifetime of the WSN. In this study, we have modified the ad-hoc on demand distance vector routing by incorporating signal-to-noise ratio (SNR) based dynamic clustering. The proposed scheme, which is an efficient and secure routing protocol for wireless sensor networks through SNR-based dynamic clustering (ESRPSDC) mechanisms, can partition the nodes into clusters and select the cluster head (CH) among the nodes based on the energy, and non CH nodes join with a specific CH based on the SNR values. Error recovery has been implemented during the inter-cluster routing in order to avoid end-to-end error recovery. Security has been achieved by isolating the malicious nodes using sink-based routing pattern analysis. Extensive investigation studies using a global mobile simulator have shown that this hybrid ESRP significantly improves the energy efficiency and packet reception rate as compared with the SNR unaware routing algorithms such as the low energy aware adaptive clustering hierarchy and power efficient gathering in sensor information systems.

• Journal of the Korean Society of Radiology
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• v.14 no.1
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• pp.53-60
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• 2020

The purpose of this study is to identify factors affecting picture quality in Roadmap images, which were studied by varying the dilution rate, collimation field and flow rate of contrast medium. For a quantitative evaluation of the quality of the picture, a 3mm vessel model Water Phantom was self-produced using acrylic, a roadmap image was acquired with a self-produced vascular model Water Phantom, and the SNR(Signal to Noise Ratio) and CNR (Contrast to Noise Ratio) were analyzed. CM:N/S In the study on the change of dilution rate, CM:N/S dilution rate changed to (100%~10%:100%), and the measurement of the roadmap image taken using the vascular model Water Phantom showed that the measurement value of SNR gradually decreased as the N/S dilution rate was increased, and the measurement of CNR was gradually reduced. It was confirmed that the higher the dilution rate of CM:N/S, the lower the SNR and CNR, and also significant image can be obtained at the dilution rate of CM:N/S (100%~70:30%). The study showed the value of SNR and CNR in Roadmap image was increased as the Collimation Field was narrowed to the center of the vascular phantom; the Collimation Field was narrowed to the center of the vessel model by 2cm intervals to 0cm through 12cm. To verify the relationship with Roadmap image and Flow Rate, volume of the autoinjector was kept constant at 15 and the flow rate was gradually increased 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. The value of SNR and CNR of images taken by using water Phantom gradually decreased as the Flow Rate increased, but at Flow Rate 9 and 10, the SNR and CNR value was increase. It was not possible to confirm the relationship with SNR and CNR by ROI mean value and Background mean value. It is considered that further study is needed to evaluate the correlation about Roadmap image and Flow Rate. In conclusion, as the dilution rate of N/S in contrast medium was increased, the value of SNR and CNR was decreased. The narrower the Collimation Field, the higher image quality by increasing value of SNR and CNR. However, it is not confirmed the relationship Roadmap image and Flow Rate. It is considered that appropriate contrast medium concentration to minimize the effects of kidney and proper Collimation Field to improve contrast of image and reduce exposure X-ray during procedure is needed.