• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.2
• /
• pp.105-110
• /
• 2018

This paper compare the adaptive equalization performance of NM-MMA (Novel Mixed-MMA) algorithm which using the mixed const function by scaling factor values. The mixed cost function of NM-MMA composed of the appropriate weighted addition of gradient vector in the MMA and SE-MMA cost function, and updating the tap coefficient based on these function, it is possible to improve the convergence speed and MSE value of current algorithm. The computer simulation was performed in the same channel, step size, SNR environment by changing the scaling factor, and its performance were compared appling the equalizer output constellation, residual isi, MD, MSE, SER. As a result of computer simulation, the residual values of performance index were reduced in case of the scaling factor of MMA cost function was greater than the scaling factor of SE-MMA. and the convergence speed was improved in case of the scaling factor of SE-MMA was greater than the MMA.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.81-87
• /
• 2016

This paper analysis the AM-SCS-MMA (Adaptive Modulus-Soft Constraint Satisfaction-MMA) based on the adaptive modulus and minimus-disturbance technique in order to improve the stability and robustness in low signal to noise power of current MMA adaptive equalization algorithm. In AM-SCS-MMA, it updates the filter coefficient applying the adaptive modulus and minimum-disturbance technique of deterministic optimization problem instead of LMS or gradient descend algorithm for obtain the minimize the cost function of adaptive equalization. It is possible to improve the equalizer filter stability, robustness to the various noise characteristic and simultaneous reducing the intersymbol interference due to the amplitude and phase distortion occurred at channel. The computer simulation were performed for confirming the improved performance of SCS-MMA. For these, the output signal constellation of equalizer, residual isi, MSE, EMSE (Excess MSE) which means the channel traking capability and SER which means the robustness were applied. As a result of computer simulation, the AM-SCS-MMA have slow convergence time and less residual quantities after steady state, more good robustness in the poor signal to noise ratio, but poor in channel tracking capabilities was confirmed than MMA.

• Journal of Korea Multimedia Society
• /
• v.13 no.10
• /
• pp.1463-1473
• /
• 2010

In this paper, we propose a modified watershed algorithm to extract more correct edge and reduce processing time. Two new algorithms are proposed in this paper. The first one is applying two conventional watershed expansion methods known as rainfall and immersion simulation jointly. We analyze the advantage and problem of each simulation and then propose a new expansion method that keeps the advantage and removes the problem in order to extract more correct edge and reduce processing time. The second is a new priority decision algorithm to obtain more correct edge of a region. Some zero-crossing points of gradient are expected to be edge of a region but the conventional method has a limitation that it cannot extract those points as edge. Therefore we propose a new priority decision algorithm for watershed in order to get more correct edge. We compare the proposed method with the conventional method through experiments and prove that the proposed method can extract more correct edge of region.

• Journal of Broadcast Engineering
• /
• v.22 no.1
• /
• pp.62-69
• /
• 2017

Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.4
• /
• pp.997-1005
• /
• 1998

This paper presents a modified on-off algorithm based on the gradient method for providing the phase of each antenna element more accurately and simply compared to the conventional on-off algorithm. The sup4erisority of theproposed method is due to the fact that the proposed method finds the increase and decrease of the array output power more accurately by utilizing the gradient of array output power with respect to the instantaneous phase of array element. The array antenna adopting to the proposed method formsmaximum beam-pattern along the direction of the desired signal by aligning the phase of every antenna enement. The proposed method is applied to both linear and two-dimentional aray for analyzing the result. The capability of the real-time processing of the proposed technique is confirmed by implementing the proposed algorithm with TMS320C30 Evaluation Module. Since the computational load required to form the beam-pattern per snapshot is small, the proposed method is suitable for the mobile communication system of which the response must be fast. By the results obtained from the application of the proposed method to the CDMA mobile communication environment, it is vreified that the performance of the received signal is consideralbly improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.2
• /
• pp.113-118
• /
• 2017

This paper propose the NM-MMA (Novel Mixed-MMA) that is possible to improving the convergence speed of current MMA algorithm and reducing the high MSE of SE-MMA algorithm, and its equalization performance were analyzed. The cost function of the NM-MMA configured as the sum of appropriate weights of gradient vector of current MMA and SE-MMA, and then it used for the updating the tap coefficient of equalizer. The computer simulation was performed applying the same environment in the channel, step size and signal to noise ratio, and the same performance index in equalizer output signal constellation, residual isi, MSE, SER was used. As a result of computer simulation, the proposed NM-MMA has fast convergence time than MMA, and less in MSE and SER performance compared to SE-MMA.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.3
• /
• pp.156-162
• /
• 2018

The LMF (Least Mean Fourth) algorithm is well known for its fast convergence and low steady-state error especially in non-Gaussian noise environments. Recently, there has been increasing interest in the LMS (Least Mean Square) algorithms with self-adjusted step size. It is because the self-adjusted step-size LMS algorithms have shown to outperform the conventional fixed step-size LMS in the various situations. In this paper, a self-adjusted step-size LMF algorithm is proposed, which adopts an averaged gradient based step size as a self-adjusted step size. It is expected that the proposed algorithm also outperforms the conventional fixed step-size LMF. The superiority of the proposed algorithm is confirmed by the simulations in the time invariant and time variant channels.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.5
• /
• pp.3434-3439
• /
• 2015

As a criterion of information theoretic learning, the Euclidean distance (ED) of two error probability distribution functions (minimum ED of error, MEDE) has been adopted in nonlinear (decision feedback, DF) supervised equalizer algorithms and has shown significantly improved performance in severe channel distortion and impulsive noise environments. However, the MEDE-DF algorithm has the problem of heavy computational complexity. In this paper, the recursive ED for MEDE-DF algorithm is derived first, and then the feed-forward and feedback section gradients for weight update are estimated recursively. To prove the effectiveness of the recursive gradient estimation for the MEDE-DF algorithm, the number of multiplications are compared and MSE performance in impulsive noise and underwater communication environments is compared through computer simulation. The ratio of the number of multiplications between the proposed DF and the conventional MEDE-DF algorithm is revealed to be $2(9N+4):2(3N^2+3N)$ for the sample size N with the same MSE learning performance in the impulsive noise and underwater channel environment.

• Journal of Broadcast Engineering
• /
• v.24 no.5
• /
• pp.870-878
• /
• 2019

In this paper, we propose an Attention-based BLSTM for predicting the video compression standard of a video. Recently, in NLP, many researches have been studied to predict the next word of sentences, classify and translate sentences by their semantics using the structure of RNN, and they were commercialized as chatbots, AI speakers and translator applications, etc. LSTM is designed to solve the gradient vanishing problem in RNN, and is used in NLP. The proposed algorithm makes video compression standard prediction possible by applying BLSTM and Attention algorithm which focuses on the most important word in a sentence to a bitstream of a video, not an sentence of a natural language.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.891-907
• /
• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.