• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.56-66
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• 2001

The spectral subtraction algorithm using a criterion based on the human perception has been recently developed. The speech processed with Virag's algorithm sounds more pleasant to a human listener than those obtained by the classical methods. However, Virag's algorithm requires a robust voice activity detector (VAD). In the ESS (extended spectral subtraction) algorithm without VAD, the residual noise becomes more noticeable as the SNR decrease. In this paper we propose a new speech enhancement method, the combination of Wiener filter and spectral subtraction based on noise masking characteristics in the human auditory system. There is no need of VAD because the noise can be successively updated even during speech activity using Wiener filter. The adjustment of the subtraction parameter based on the masking threshold makes the residual noise inaudible. The proposed method has been compared with conventional spectral subtraction algorithms. Objective and subjective evaluation of the proposed system is performed with several noise types having different time-frequency distributions. The application of objective measures, the study of the speech spectrograms, as well as subjective listening tests, confirm that the enhanced speech with proposed algorithm is more pleasant to a human listener.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.333-344
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• 2010

This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using $640{\times}480$ images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.50-59
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• 2011

In this paper, we present a spectrum sensing method by exploiting the relationship of previous and current detected data sets in frequency domain. Most of the traditional spectrum sensing methods only consider the current detected data sets of Primary User (PU). Previous state of PU is a kind of conditional probability that strengthens the reliability of the detector. By considering the relationship of the previous and current spectrum sensing, cross entropy-based spectrum sensing is proposed to detect PU signal more effectively, which has a strengthened performance and is robust. When previous detected signal is noise, the discriminating ability of cross entropy-based spectrum sensing is no better than conventional entropy-based spectrum sensing. To address this problem, we propose an improved cross entropy-based frequency-domain spectrum sensing. Regarding the spectrum sensing scheme, we have derived that the proposed method is superior to the cross entropy-based spectrum sensing. We proceed a comparison of the proposed method with the up-to-date entropy-based spectrum sensing in frequency-domain. The simulation results demonstrate the performance improvement of the proposed spectrum sensing method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.1
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• pp.89-97
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• 2011

We analyze the performance of various techniques to overcome degradation of performance of STAP caused by nonhomogeneous clutter. The performance of NHD that used to eliminate outliers from nonhomogeneous clutter is improved by using the projection statistics(PS) that is robust to multiple outliers. The method of clutter covariance matrix estimation using a median value and the conventional method are also investigated and then compared. From the simulation results of STAP, the method of clutter covariance matrix estimation using a median value shows better performance than the conventional method for the calculation of the SINR loss, and MSMI for the single target and the multiple targets regardless of the NHD methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.88-97
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• 2013

Most of the traditional spectrum sensing methods consider only the current detected data sets of Primary User (PU). However previous state of PU is a kind of conditional probability that strengthens the reliability of the detector. Therefore, in the cross entropy spectrum sensing method, relationship of the previous and current spectrum sensing is considered to detect PU signal more effectively. But these cross entropy spectrum sensing methods only consider the ideal system. In other words, PU always occupy the channel during the same period. However, PU can occupy the channel either for a longer or a shorter period than the ideal case in the real system. For this reason, the spectrum sensing performance can be varied. In this paper, we propose the method that can maintain the performance of spectrum sensing in the real system and we confirm the results with the help of simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8C
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• pp.505-515
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• 2011

This paper describes a normalization method of Haar-like features used for object detection. Previous method which performs variance normalization on Haar-like features requires a lot of calculations, since it uses an additional integral image for calculating the standard deviation of intensities of pixels in a candidate window and increases possibility of false detection in the area where variance of brightness is small. The proposed normalization method can be performed much faster than the previous method by not using additional integral image and classifiers which are trained with the proposed normalization method show robust performance in various lighting conditions. Experimental result shows that the object detector which uses the proposed method is 26% faster than the one which uses the previous method. Detection rate is also improved by 5% without increasing false alarm rate and 45% for the samples whose brightness varies significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6A
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• pp.500-505
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• 2009

In this paper, We present a low complexity candidate list generation scheme in iterative MIMO receiver. Since QAM modulation can be decomposed into HP symbols and LP symbol and HP symbol is robust in error capability, we generate HP symbol list with simple ZF detector output and its corresponding neighbor HP symbols, Then, based on HP symbol list, the LP symbol list is generated by using the sphere decoder. From the second iteration, since apriori value from channel decoder is available, the candidate list is updated based on demodulated apriori value. Through the simulation, we observe that at the first iteration, the BER performance is worse than LSD. However, as the number of iteration is increased, the proposed scheme has almost same performance as LSD. Moreover, the proposed one has reduced candidate list generation time and lower number of candidate list compared with LSD.

• The Journal of the Acoustical Society of Korea
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• v.16 no.6
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• pp.25-35
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• 1997

Most of speech analysis methods developed up to date are based on second order statistics, and one of the biggest drawback of these methods is that they show dramatical performance degradation in noisy environments. On the contrary, the methods using higher order statistics(HOS), which has the property of suppressing Gaussian noise, enable robust feature extraction in noisy environments. In this paper we propose a text-independent speaker identification system using higher order statistics and compare its performance with that using the conventional second-order-statistics-based method in both white and colored noise environments. The proposed speaker identification system is based on the vector quantization approach, and employs HOS-based voiced/unvoiced detector in order to extract feature parameters for voiced speech only, which has non-Gaussian distribution and is known to contain most of speaker-specific characteristics. Experimental results using 50 speaker's database show that higher-order-statistics-based method gives a better identificaiton performance than the conventional second-order-statistics-based method in noisy environments.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.3
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• pp.37-46
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• 2012

In RADAR and SONAR detection systems, noise environment can be classified into homogeneous and heterogeneous environment. Especially heterogeneous environments are modelled as target masking and clutter edge. Since the variability-index (VI) CFAR, a composed CFAR algorithm, dynamically selects one of the mean-level algorithms based on the VI and the MR (mean ratio) test, it is robust to various environments. However, the VI CFAR still suffers from lowered detection probabilities in heterogeneous environments. To overcome these problems, we propose an improved VI CFAR processor where TM (trimmed mean) CFAR and a sub-windowing technique are introduced to minimize the degradation of the detection probabilities appeared in heterogeneous environments. Computer simulation results show that the proposed method has the better performance in terms of detection probability and false alarm probability compared to the VI CFAR and single CFAR algorithms.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.67-76
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• 2012

This paper proposes an efficient method that helps a visually impaired person to detect a pavement borderline. A pedestrian is equipped with a camera so that the front view of a natural scene is captured. Our approach analyzes the captured image and detects the borderline of a pavement in a very robust manner. Our approach performs the task in two steps. In a first step, our approach detects a vanishing point and vanishing lines by applying an edge operator. The edge operator is designed to take a threshold value adaptively so that it can handle a dynamic environment robustly. The second step is to determine the borderlines of a pavement based on vanishing lines detected in the first step. It analyzes the vanishing lines to form VRays that confines the pavement only. The VRays segments out the pavement region in a radial manner. We compared our approach against Canny edge detector. Experimental results show that our approach detects borderlines of a pavement very accurately in various situations.