• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.115-121
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• 2013

This paper related with the DSE-MMA (Dithered Sign-Error MMA) that is the simplification of computational arithmetic number in blind equalization algorithm in order to compensates the intersymbol interference which occurs the passing the nonlinear communication channel in the presence of the band limit and phase distortion. The SE-MMA algorithm has a merit of H/W implementation for the possible to reduction of computational arithmetic number using the 1 bit quantizer in stead of multiplication in the updating the equalizer tap weight. But it degradates the overall blind equalization algorithm performance by the information loss at the quantization process compare to the MMA. The DSE-MMA which implements the dithered signed-error concepts by using the dither signal before qualtization are added to MMA, then the improved SNR performance which represents the roburstness of equalization algorithm are obtained. It has a concurrently compensation capability of the amplitude and phase distortion due to intersymbol interference like as the SE-MMA and MMA algorithm. The paper uses the equalizer output signal, residual isi, MD, MSE learning curve and SER curve for the performance index of blind equalization algorithm, and the computer simulation were performed in order to compare the SE-MMA and DSE-MMA applying the same performance index. As a result of simulation, the DSE-MMA can improving the roburstness and the value of every performance index after steady state than the SE-MMA, and confirmed that the DSE-MMA has slow convergence speed which meaning the reaching the seady state from initial state of adaptive equalization filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.152-159
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• 2005

For high-speed data communication in band-limited channels, main of the bit error are fading and ISI(Inter-Symbol Interference). The common way of dealing with ISI is using equalization in the receiver. In this thesis, channel adaptive equalizer which uses Fuzzy Stochastic Gradient(FSG) and Constant Modulus Algorithm(CMA) is nonlinear equalizer, or Blind equalizer, that works directly on the signals with no training sequences required. This equalizer employs Takagi-Sugeno's fuzzy model that uses the FSG algorithm, to automatically regulate the step size of the descent gradient vector, combining fast convergence rate and low mean square error(MSE), and the CMA which is a special case of Godard's algorithm, to having multiple dispersion constants($R_p$).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.109-114
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• 2016

This paper relates with the performance improvement of CCA (Compact Constellation Algorithm) equalization algorithm by adding the adaptive step size control in order to the minimization of intersymbol interference and additive noise effects that is occurs in the channel for digital radio transmissionl. In general, the fixed step size was used in order to adaptation in equalizer algorithm. But in proposed algorithm, the variable step size were adapted that is proposional to the nonlinear function of error signal for equalization. In order to show the improved equalizatation performance, the output signal constellation of equalizer, residual isi, maximum distortion, MSE and SER were used, then it were compared with the present CCA algorithm. As a result of computer simulation, the adaptive step size CCA has more better performance in the every performance index compared to the fixed step size CCA after in the steay state.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.63-69
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• 2013

This paper related with the performance of SE-MMA (Signed-Error MMA) that is the reduction of computational operation number in algorithm than MMA blind eualization algorithm which are possible to elimination of intersymbol interferance in the band limited and time dispersive nonlinear communication channel. In MMA algorithm which are possible to reduction of amplitude and phase rotation by intersymbol interference that is occurred in channel without using the training sequence, it uses the error signal that is the difference of the equalizer output and constant modulus, the statisticlly characteristic of transmitted signal. But in SE-MMA, it uses the polarity of the error signal, then it is possible to reduce the updating the tap coefficient and to simplify the H/W implementation. The computer simulation were performed in order to compare the performance of SE-MMA and conventional MMA algorithm. For this, the recovered signal constellation that is the output of the equalizer, the convergence performance by MSE, MD (maximum distortion) and residual isi characteristic learning curve, SER were used. As a result of simulation, the SE-MMA has more fast convergence speed than the MMA. But in the other index after reaching the seady state, it gives more worst performance values in the used index.

• 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.