• The Journal of the Acoustical Society of Korea
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• v.33 no.2
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• pp.139-145
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• 2014

In this paper, a feature parameter is obtained by applying the Teager energy to the WPD(Wavelet Packet Decomposition) coefficients. The threshold value is obtained based on means and standard deviations of nonspeech frames. Experimental results by using TIMIT speech and NOISEX-92 noise databases show that the proposed algorithm is superior to the typical VAD algorithm. The ROC(Receiver Operating Characteristics) curves are used to compare performance of VAD's for SNR values of ranging from 10 to -10 dB.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.712-715
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• 1999

In this paper we propose high quality audio coding algorithm using psychoacoustic modelling and the adaptive wavelet Packet decomposition. The bit allocation scheme exploits the remnants of temporal correlations that exist in the wavelet packet coefficients by SPIHT. The proposed algorithm achieve almost transparent coding of monophonic compact disk(CD) quality signals at about 44 kbps.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2307-2310
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• 2003

In this paper, a method for face recognition based on the wavelet packet decomposition is proposed. In the proposed method, the input image is decomposed by the 2-level wavelet packet transformation and then the face areas are defined by the Integral Projection technique applied to each of the 1-level subband images, HL and LH. After the defined face areas are divided into three areas, called top, bottom, and border, the mean and the variance of the three areas of the approximation image are computed, and the variance of the single predetermined face area for the rest of 15 detail images, from which the feature vectors of statistical measure are extracted. In this paper we use the wavelet packet decomposition, a generalization of the classical wavelet decomposition, to obtain its richer signal analysis features such as discontinuity in higher derivatives, self-similarity, etc. And we have shown that even with very simple statistical features such as mean values and variance we can make an excellent basis for face classification, if an appropriate probability distance is used.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.607-608
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• 2012

• Smart Structures and Systems
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• v.1 no.1
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• pp.29-46
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• 2005

In this study, a wavelet packet based method is proposed for identifying damage occurrence and damage location for beam-like structures. This method assumes that the displacement or the acceleration response time histories at various locations along a beam-like structure both before and after damage are available for damage assessment. These responses are processed through a proper level of wavelet packet decomposition. The wavelet packet signature (WPS) that consists of wavelet packet component signal energies is calculated. The change of the WPS curvature between the baseline state and the current state is then used to identify the locations of possible damage in the structure. Two numerical studies, one on a 15-storey shear-beam building frame and another on a simply-supported steel beam, and an experimental study on a simply-supported reinforced concrete beam are performed to validate the proposed method. Results show the WPS curvature change can be used to locate both single and sparsely-distributed multiple damages that exist in the structure. Also the accuracy of assessment does not seem to be affected by the presence of 20-15dB measurement noise. One advantage of the proposed method is that it does not require any mathematical model for the structure being monitored and hence can potentially be used for practical application.

• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.174-179
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• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.433-434
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• 2012

• Smart Structures and Systems
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• v.12 no.2
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• pp.181-207
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• 2013

In this paper, a statistical reference-free real-time damage detection methodology is proposed for detecting joint and member damage of truss bridge structures. For the statistical damage sensitive index (DSI), wavelet packet decomposition (WPD) in conjunction with the log likelihood ratio was suggested. A sensitivity test for selecting a wavelet packet that is most sensitive to damage level was conducted and determination of the level of decomposition was also described. Advantages of the proposed method for applications to real-time health monitoring systems were demonstrated by using the log likelihood ratios instead of likelihood ratios. A laboratory truss bridge structure instrumented with accelerometers and a shaker was used for experimental verification tests of the proposed methodology. The statistical reference-free real-time damage detection algorithm was successfully implemented and verified by detecting three damage types frequently observed in truss bridge structures - such as loss of bolts, loosening of bolts at multiple locations, sectional loss of members - without reference signals from pristine structure. The DSI based on WPD and the log likelihood ratio showed consistent and reliable results under different damage scenarios.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.81-88
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• 2018

Spectrum sensing, the key technology of the cognitive radio networks, is used by a secondary user to determine the frequency state of a primary user. The energy detection in the spectrum sensing determines the presence or absence of a primary user according to the intensity of the allocated channel signal. Since this technique simply uses the strength of the signal for spectrum sensing, it is difficult to detect the signal of a primary user in the low SNR band. In this paper, we propose a way to combine spectrum sensing and support vector machine using wavelet packet decomposition to overcome performance degradation in low SNR band. In our proposed scheme, the sensing signals were extracted by wavelet packet decomposition and then used as training data and test data for support vector machine. The simulation results of the proposed scheme are compared with the energy detection using the AUC of the ROC curve and the accuracy according to the SNR band. With simulation results, we demonstrate that the proposed scheme show better determining performance than one of energy detection in the low SNR band.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2327-2330
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• 2003

This paper deals with face detection and recognition using ellipsodal information and wavelet packet analysis. We proposed two methods. First, Face detection method uses general ellipsodal information of human face contour and we find eye position on wavelet transformed face images A novel method for recognition of views of human faces under roughly constant illumination is presented. Second, The proposed Face recognition scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, the Euclidian distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the MIT FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. The proposed system achieved an rate of 97％(MIT data), 95.8％(FERET databace)