Relevant bibliographies by topics / Bi-orthogonal rational discrete wavelet transform

Academic literature on the topic 'Bi-orthogonal rational discrete wavelet transform'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Bi-orthogonal rational discrete wavelet transform"

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Tran Nguyen Nguyen, Si, and Brian W. H. Ng. "Bi-orthogonal rational discrete wavelet transform with multiple regularity orders and application experiments." Signal Processing 93, no. 11 (November 2013): 3014–26. http://dx.doi.org/10.1016/j.sigpro.2013.04.001.

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Sahithi, A., N. Rama Murthy, P. Mounica, and S. Praveena. "Image Fusion by Using Bi-Orthogonal Wavelets in Discrete Wavelet Transform (DWT) Domain." Journal of Remote Sensing GIS & Technology 8, no. 2 (June 18, 2022): 1–6. http://dx.doi.org/10.46610/jorsgt.v08i02.001.

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Image fusion can be widely explained as merging more than one photograph or a few in their factors right into a single photo without the creation of distortion or lack of information. Image fusion pastimes mix complementary as properly as approximate statistics for greater than one photograph to create fused photo output. Hence, the output picture achieved must have to include an extra accurate representation of the position than any of the man or woman supply pix and is greater fantastic for human visible, and pc understanding or in addition picture processing and comparison duties. . The notably used fusion rule is the most willpower scheme. This handy scheme chooses the biggest absolute wavelet coefficient at each location from the given snapshots because of the coefficient on the area within the fused photo. After that, the fused photo is obtained with the aid of using the inverse DWT for the corresponding wavelet coefficient. The well-known significantly used pixel-primarily based fusion rule is the aforementioned maximum favored scheme. This approach can choose the salient elements from the furnished photographs; however, it's miles touchy with noise and artifacts as they have been supposed to have better contrast. When the 2 photographs are degraded with the aid of using the wavelet transform, the approximation picture (low–frequency band) and thing image (high-frequency band) may additionally have one-of-a-type bodily meaning. In this project, a new fusion rule is proposed to feature a wavelet coefficient that treats low-frequency and high-frequency bands with high-quality fusion schemes.
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Ayrulu-Erdem, Birsel, and Billur Barshan. "Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals." Sensors 11, no. 2 (January 28, 2011): 1721–43. http://dx.doi.org/10.3390/s110201721.

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We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.
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Schleif, Frank-Michael, Mathias Lindemann, Mario Diaz, Peter Maaß, Jens Decker, Thomas Elssner, Michael Kuhn, and Herbert Thiele. "Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform." Computing and Visualization in Science 12, no. 4 (March 7, 2008): 189–99. http://dx.doi.org/10.1007/s00791-008-0087-z.

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Amusa, Kamoli, Adeoluwawale Adewusi, Tolulope Erinosho, Sule Salawu, and David Odufejo. "On the application of wavelet transform and Huffman algorithm to Yorùbá language syntax text files compression." Serbian Journal of Electrical Engineering 19, no. 3 (2022): 351–68. http://dx.doi.org/10.2298/sjee2203351a.

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Most algorithms of data compression were developed with English language as target text syntax. However, this paper approaches the problem of Yor?b? text files compression via the use of Discrete Wavelet Transform (DWT) and Huffman algorithm. Text files in Yor?b? language syntax are first converted into signal format that are then decomposed using DWT. The decomposed ASCII code representation of the text files are subsequently encoded using Huffman algorithm. Twenty different variants of DWTs taken from four families of wavelet filters (Haar, Daubechies, Symlets and bi-orthogonal) are considered to select the optimal DWT for Yor?b? text files compression. Furthermore, experiments are carried out in the proposed compression scheme with six different Yor?b? text files extracted from the open sources as input data sets. It is found that out of the twenty variants of DWT investigated, sym6 gives the best output for effective Yor?b? text files compression, due to its relatively high compression ratio, high compression factor and lowest compression error. Thus, sym6 as a wavelet transform is suitable for lossy text compression algorithm meant for Yor?b? language syntax text files.
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Rajpal, Danveer, Akhil Ranjan Garg, Om Prakash Mahela, Hassan Haes Alhelou, and Pierluigi Siano. "A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters." Future Internet 13, no. 9 (September 18, 2021): 239. http://dx.doi.org/10.3390/fi13090239.

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Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.
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GOUTHAM, S., and DIVYA PRABHA. "DESIGN & FPGA IMPLEMENTATION OF EFFICIENT MULTIBAND OFDM USING DWT/DUC/DDC." International Journal of Electronics Signals and Systems, January 2013, 204–7. http://dx.doi.org/10.47893/ijess.2013.1106.

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To increase data rate of wireless medium with higher performance, OFDM (orthogonal frequency division multiplexing) is used. Here DWT (Discrete wavelet transforms) is adopted in place of FFT (Fast Fourier transform) for frequency translation. Modulation schemes such as 16-QAM (Quadrature amplitude modulation) have been used in the development of OFDM system using DWT. In this paper, I propose a DWT-IDWT based OFDM transmitter and receiver .It has been proven that all the wavelet families better over the IFFT-FFT implementation.. The wavelet filter used in the project is Bi-orthoganal (9,7) with N=2. The Project also include implementation of Digital Up Converter and Digital Down Converter at the transmitter and receiver part respectively. The project is implemented on FPGA by designing using Verilog HDL and System Generator.
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Dissertations / Theses on the topic "Bi-orthogonal rational discrete wavelet transform"

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Nguyen, Nguyen Si Tran. "Design of bi-orthogonal rational discrete wavelet transform and the associated applications." Thesis, 2014. http://hdl.handle.net/2440/84673.

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Time-frequency analysis has long been a very useful tool in the field of signal processing, especially in dealing with non-stationary signals. Wavelet transform is amongst many time-frequency analysis techniques whose attributes have been well exploited in many classic applications such as de-noising and compression. In recent years, representation sparsity, a measure of the representation’s ability to condense signals’ energy into few coefficients, has raised much interest from researchers in many fields such as signal processing, information theory and applied mathematics due to its wide range of use. Thus, many classes of time-frequency representations have recently been developed from the conventional ones in maximising the representation sparsity recently. Rational discrete wavelet transform (RADWT), an extended class of the conventional wavelet family, is among those representations. This thesis discusses the design of bi-orthogonal rational discrete wavelet transform which is constructed from finite impulse response (FIR) two-channel rational rate filter banks and the associated potential applications. Techniques for designing the bi-orthogonal rational filter bank are proposed, their advantages and disadvantages are discussed and compared with the existing designs in literature. Experimental examples are provided to illustrate the use of the novel bi-orthogonal RADWT in application such as signal separation. The experiments show sparser signal representations with RADWTs over conventional dyadic discrete wavelet transforms (DWTs). This is then exploited in applications such as de-noising and signal separation based on basis pursuit.
Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2014
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