• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.251-268
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• 2007

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1157-1165
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• 2017

2-D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2-D complex Gabor filter bank consisting of the 2-D complex Gabor filtering outputs at multiple orientations and frequencies. Although several approaches for fast 2-D complex Gabor filtering have been proposed, they primarily focus on reducing the runtime of performing the 2-D complex Gabor filtering once at specific orientation and frequency. To obtain the 2-D complex Gabor filter bank output, existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2-D complex Gabor filter bank by reducing the computational redundancy that arises when performing the Gabor filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor basis kernels to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the 2-D complex Gabor filter bank by reusing intermediate results of the 2-D complex Gabor filtering computed at a specific orientation. Experimental results demonstrate that our method runs faster than state-of-the-arts methods for fast 2-D complex Gabor filtering, while maintaining similar filtering quality.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.2
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• pp.157-169
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• 2013

Scale Invariant Feature Transform (SIFT) generates image features widely used to match objects in different images. Previous work on hardware-based SIFT implementation requires excessive internal memory and hardware logic [1]. In this paper, a new hardware organization is proposed to implement SIFT with less memory and hardware cost than the previous work. To this end, a parallel Gaussian filter bank is adopted to eliminate the buffers that store intermediate results because parallel operations allow all intermediate results available at the same time. Furthermore, the processing order is changed from the raster-scan order to the block-by-block order so that the line buffer size storing the source image is also reduced. These techniques trade the reduction of memory size with a slight increase of the execution time and external memory bandwidth. As a result, the memory size is reduced by 94.4%. The proposed hardware for SIFT implementation includes the Descriptor generation block, which is omitted in the previous work [1]. The addition of the hardwired descriptor generation improves the computation speed by about 30 times when compared with the previous work.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.297-298
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• 2008

A new equalization method for perpendicular magnetic recording channels is proposed. The proposed equalizer incorporates the Gaussian sum approximation into a Kalman filtering framework to mitigate inter-symbol interference in perpendicular magnetic recording systems. The proposed equalizer consists of a bank of linear equalizers using the Kalman filtering algorithm and its output is obtained by combining the outputs of linear equalizers through the Gaussian sum approximation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.639-644
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• 2001

Sequence Code Division Multiple Access) and TDMA(Time Division Multiple Access) system. In this system, the narrowband TDMA signals can cause intolerant interferences to CDMA signals. In this paper, DS-CDMA/TDMA frequency sharing system have been analyzed in AWGN(Additive white Gaussian Noise), MAI (Multi Access Interference) and NI(Narrowband Interference) environment. Also, performance improvement has been obtained by adopting an adaptive notch filtering scheme using complex filter bank and CCI canceller.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.163-172
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• 1996

Recently, it was reported that gaussian distribution and difference of two gaussians (DOG) to have band pass characteristics can be generated by simple iterative processes of the diffusion networks. In this paper, we propose method of improved implementation of a diffusion filter which can reduce total runing time, and operate by simple algorithm in contrast to the latest diffusion filter. We rebuild the diffusion network to a difference network which can generate DOG independently. Different filter characteristics are obtained just by each diffusion process and difference process. Quantitative analysis shows that the center frequency and the selectivity of each filter channel can be varied independently. Also, it would requires smaller amount of hardwares than conventioanl method to build a filter bank.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.130-133
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• 1999

In the view point of further reducing the inter-symbol interferences studied in our previous paper 〔1〕, a quadrature pair of wavelet-based filter banks that are composed of a pair of cosine and sine modulated filter banks is applied to MC-CDMA transmultiplexing. For that fact, the symbol duration gets twice longer than the one in , 〔1〕, the interference effects due to channel overlapping and Doppler spread can be effectively alleviated while increasing the channel utilization efficiency. Moreover, the well-known wavelet properties are exploited to design the prototype filter in such a way to maintain the size of sidelobes much smaller than those of the FFT, the interference reduction effect can be further obtained. To verify the behavior of our proposed quadrature filter bank based MC-CDMA system, the reverse-link bit error rates with respect to SNR under Rayleigh fading and additive white Gaussian noise channel environments are computed. The results show an improved system performance over the conventional MC-CDMA.

• MALSORI
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• no.52
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• pp.133-144
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• 2004

The quality of narrowband speech can be enhanced by the bandwidth extension technology. This paper proposes a mixed excitation and an energy compensation method based on Gaussian Mixture Model (GMM). First, we employ the mixed excitation model having both periodic and aperiodic characteristics in frequency domain. We use a filter bank to extract the periodicity features from the filtered signals and model them based on GMM to estimate the mixed excitation. Second, we separate the acoustic space into the voiced and unvoiced parts of speech to compensate for the energy difference between narrowband speech and reconstructed highband, or lowband speech, more accurately. Objective and subjective evaluations show that the quality of wideband speech reconstructed by the proposed method is superior to that by the conventional bandwidth extension method.

• Journal of Ocean Engineering and Technology
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• v.28 no.6
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• pp.552-559
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• 2014

In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.