• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.494-503
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• 2002

A motion vector re-estimation algorithm for image downscaling in discrete consine transform domain is presented. Kernel functions are difined using SAD (Aum of Absolute Difference) and edge information of a macroblock. The proposed method uses these kernel functions to re-estimate a new motion vector of the downscaled image. The motion vectors from the incoming bitstream of transcoder are reused to reduce computation burden of the block-matching motion estimation, and we also reuse the given motion vectors. Several experiments in this paper show that the computation efficiency and the PSNR (Peak Signal to Noise Ratio) and better than the previous methods.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.534-542
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• 2019

In active SONAR, several different methods are used to detect range-Doppler information of the target. Compressive sensing based method is more accurate than conventional methods and shows superior performance. There are several compressive sensing algorithms for range-Doppler estimation of active sonar. The ability of each algorithm depends on algorithm type, mutual coherence of sensing matrix, and signal to noise ratio. In this paper, we compared and analyzed computational performance and accuracy of various compressive sensing algorithms for range-Doppler estimation of active sonar. The performance of OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), BPDN (CVX) (Basis Pursuit Denoising), LARS (Least Angle Regression) algorithms is respectively estimated for varying SNR (Signal to Noise Ratio), and mutual coherence. The optimal compressive sensing algorithm is presented according to the situation.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.38-40
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• 2007

In this paper, we propose a novel architecture, which is based on DCT (Discrete Cosine Transform), for ME (Motion Estimation) and MC (Motion Compensation). The traditional algorithms of ME and MC based on DCT did not suffer the advantage of the coarseness of the 2-dimensional DCT (2-D DCT) coefficients to reduce the operational time. Therefore, we derive a recursion equation for transform-domain ME and MC and design the structure by using highly regular, parallel, and pipeline processing elements. The main difference with others is removing the IDCT block by using to transform domain. Therefore, the performance of our algorithm is more efficient in practical image processing such as DVR (Digital Video Recorder) system. We present the simulation result which is compare with the spatial domain methods. it shows reducing the calculation cost. compression ratio. and peak signal to noise ratio (PSNR).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.434-439
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• 2019

This paper proposed a control algorithm to compensate the delay time to improve the signal to noise, and the proposed control algorithm estimate the target information to apply the continuous wave radar equation. The proposed control algorithm improves the output signal of each array element bv multiplying the weight of the receive signal to the signal to noise ratio. Radar radiate a signal in spatial and the target information is estimated by the echoed signal from the target. But the signal in the wireless communication environment occurs the delay time due to the multipath which appear human and natural structures. It is difficult to accurately estimate the desired information because of the degradation for the system performance due to the interference signal and the signal distortion. The target information can be improved by compensating the delay signal to apply the weight to the received signal by using the uniform linear array antenna. As a simulation result, we show that the performance of the proposed control algorithm and the non-compensated delay time are compared. The proposed control algorithm proved that the target distance estimation information is improved.

• Journal of Advanced Navigation Technology
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• v.16 no.3
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• pp.426-432
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• 2012

In this paper, we investigate the signal to noise ratio (SNR) estimation performance of Cross quadrature amplitude modulation (QAM), which is being used for asymmetric digital subscriber line (ADSL), very high bit rate digital subscriber line (VDSL), and digital video broadcasting - cable (DVB-C), and has been found to be useful in adaptive modulation and blind equalization. At first, the symbol error rate (SER) performance of Cross QAM is analyzed in Rayleigh fading channel. Then we suggest error vector magnitude (EVM) based SNR estimation utilizing the selected constellation points having different types of decision region from one another, and verify that SNR estimation performance of each points have different performance pattern through simulation. From the simulation results, it has been found that when suggested selected constellation points are used for SNR estimation in Cross QAM, estimation performance is enhanced in additive white Gaussian noise (AWGN) channel or Ricean fading channel.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.481-486
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• 2012

Accurate and efficient estimation of the number of sources is critical for providing the parameter of targets in problems of array signal processing and blind source separation among other such problems. When conventional estimators work in unfavorable scenarios, e.g., at low signal-to-noise ratio (SNR), with a small number of snapshots, or for sources with a different strength, it is challenging to maintain good performance. In this paper, the detection limit of the minimum description length (MDL) estimator and the signal strength required for reliable detection are first discussed. Though a comparison, we analyze the reason that performances of classical estimators deteriorate completely in unfavorable scenarios. After discussing the limiting distribution of eigenvalues of the sample covariance matrix, we propose a new approach for estimating the number of sources which is based on a sequential hypothesis test. The new estimator performs better in unfavorable scenarios and is consistent in the traditional asymptotic sense. Finally, numerical evaluations indicate that the proposed estimator performs well when compared with other traditional estimators at low SNR and in the finite sample size case, especially when weak signals are superimposed on the strong signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.311-317
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• 2007

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.4
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• pp.393-399
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• 2008

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

• 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 the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.494-500
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• 2020

This paper proposes a new distance estimation technique for indoor localization in ultra wideband (UWB) systems. The proposed technique is based on recurrent neural network (RNN), one of the deep learning methods. The RNN is known to be useful to deal with time series data, and since UWB signals can be seen as a time series data, RNN is employed in this paper. Specifically, the transmitted UWB signal passes through IEEE802.15.4a indoor channel model, and from the received signal, the RNN regressor is trained to estimate the distance from the transmitter to the receiver. To verify the performance of the trained RNN regressor, new received UWB signals are used and the conventional threshold based technique is also compared. For the performance measure, root mean square error (RMSE) is assessed. According to the computer simulation results, the proposed distance estimator is always much better than the conventional technique in all signal-to-noise ratios and distances between the transmitter and the receiver.