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Relevant bibliographies by topics / Electrical and Computer Engineering. Signal processing. Wavelets (Mathematics) / Journal articles

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Journal articles on the topic 'Electrical and Computer Engineering. Signal processing. Wavelets (Mathematics)'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Panigrahy, Parth Sarathi, and Paramita Chattopadhyay. "Cascaded signal processing approach for motor fault diagnosis." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 6 (November 5, 2018): 2122–37. http://dx.doi.org/10.1108/compel-11-2017-0476.

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PurposeThe purpose of this paper is to inspect strategic placing of different signal processing techniques like wavelet transform (WT), discrete Hilbert transform (DHT) and fast Fourier transform (FFT) to acquire the qualitative detection of rotor fault in a variable frequency drive-fed induction motor under challenging low slip conditions.Design/methodology/approachThe algorithm is developed using Q2.14 bit format of Xilinx System Generator (XSG)-DSP design tool in MATLAB. The developed algorithm in XSG-MATLAB can be implemented easily in field programmable gate array, as a provision to generate the necessary VHDL code is available by its graphical user interface.FindingsThe applicability of WT is ensured by the effective procedure of base wavelet selection, which is the novelty of the work. It is found that low-order Daubechies (db) wavelets show decent shape matching with current envelope rather than raw current signal. This fact allows to use db1-based discrete wavelet transform-inverse discrete wavelet transform, where economic and multiplier-less design is possible. Prominent identity of 2sfscomponent is found even at low FFT points due to the application of suitable base wavelet.Originality/valueThe proposed method is found to be effective and hardware-friendly, which can be used to design a low-cost diagnostic instrument for industrial applications.

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Chandran, Kalyana Sundaram, and T. Kiruba Angeline. "Identification of Disease Symptoms Using Taste Disorders in Electroencephalogram Signal." Journal of Computational and Theoretical Nanoscience 17, no. 5 (May 1, 2020): 2051–56. http://dx.doi.org/10.1166/jctn.2020.8848.

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A Brain Computer Interface (BCI) is the one which converts the activity of the brain signals into useful and understandable signal. Brain computer interface is also called as Neural-Control Interface (NCI), Direct Neural Interface (DCI) or Brain Interface Machine (BMI). Electroencephalogram (EEG) based brain computer interfaces (BCI) is the technique used to measure the activity of the brain. Electroencephalography (EEG) is a brain wave monitoring and diagnosis. It is the measurement of electrical activity of the brain from the scalp. Taste sensations are important for our body to digest food. Identification of disease symptoms is based on the inhibition of different types of taste and by testing them to find the normality and abnormality of taste. The information is used in detection of disorder such as Parkinson’s disease etc. It is a source of reimbursement for better clinical diagnosis. Our brain continuously produces electrical signals when it operates. Those signals are measured with the equipment called Neurosky Mindwave Mobile headset. It is used to collect the real time brain signal samples. Neurosky is the equipment used in proposed work. Here the pre-processing technique is executed with median filtering. Feature extraction and classification is done with Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). It increases the performance accuracy. The SVM classification accuracy achieved by this work is 90%. The sensitivity achieved is higher and the specificity is about 80%. We can able to predict the taste disorders using this methodology.

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KAHAEI, M. H. "Detection of Bearing Faults Using Haar Wavelets." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 3 (March 1, 2006): 757–63. http://dx.doi.org/10.1093/ietfec/e89-a.3.757.

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4

KOLUMBAN, G., and T. KREBESZ. "UWB Radio: Digital Communication with Chaotic and Impulse Wavelets." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A, no. 10 (October 1, 2007): 2248–49. http://dx.doi.org/10.1093/ietfec/e90-a.10.2248.

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5

Zhang, Kun, Ling Shi, Yue Hu, Peng Chen, and Yonggang Xu. "Variable spectral segmentation empirical wavelet transform for noisy signal processing." Digital Signal Processing 117 (October 2021): 103151. http://dx.doi.org/10.1016/j.dsp.2021.103151.

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6

ADACHI, Atsuyuki, Shogo MURAMATSU, and Hisakazu KIKUCHI. "Constraints of Second-Order Vanishing Moments on Lattice Structures for Non-separable Orthogonal Symmetric Wavelets." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A, no. 3 (2009): 788–97. http://dx.doi.org/10.1587/transfun.e92.a.788.

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7

Sathish, K., Aritra Paul, Debapriya Roy, Ishmeet Kalra, and Simran Bajaj. "Brain Computer Interface for Communication and Control of Peripherals and Appliances." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1616–21. http://dx.doi.org/10.1166/jctn.2020.8411.

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The concept is designed to improve upon the recent developed system, utilizing auditory steady state response (ASSR) as a basis for the Brain Computer Interface (BCI) paradigm. It utilizes the classification of signals through a discrete wavelet transform (DWT) before the actual transmission to reduce overhead at the processing system. The electroencephalogram (EEG) obtained from the subject is through a p300 based EEG receivers. A compression algorithm is used to reduce the bandwidth usage and provide a quicker transmission of the large and continuous EEG. An Arduino board along with a proximity sensor is used to detect the presence and distance of the subject and consequently control playback of a single frequency audio signal, which as received by the user, is used for producing the EEG signals. A continuous focus of the user is required on the playback of the single frequency sound to produce a sizeable reading. At the receiving end, another Arduino board is installed with an SD card module, which contains the commands, responsible for the actual control of the devices. The concept can be utilized for various purposes from controlling IoT based systems to wheelchairs and hospital beds as well as bionic limbs, which however are limited due to the overall bulk of all the equipment currently required. The main aim of this paper is to propose an improvement in the transmission, reduction the latency of the signals and to provide a concept for utilization by the handicapped or physically impaired patients. Since the EEG is obtained through the inner ear of the subject, it completely eliminates any need for invasive surgery and provides a simplified solution. Developments have shown to be able to achieve over 95% of accuracy in the domain, currently limited by length of the EEG required in order to process the actual commands from the subject’s brain.

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8

KWON, Kil Hyun, and Dae Gwan LEE. "Oversampling Expansion in Wavelet Subspaces." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E94-A, no. 5 (2011): 1184–93. http://dx.doi.org/10.1587/transfun.e94.a.1184.

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9

CHEN, Y. L. "Progressive Image Inpainting Based on Wavelet Transform." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 10 (October 1, 2005): 2826–34. http://dx.doi.org/10.1093/ietfec/e88-a.10.2826.

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10

FUKUMA, S. "Switching Wavelet Transform for ROI Image Coding." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 7 (July 1, 2005): 1995–2006. http://dx.doi.org/10.1093/ietfec/e88-a.7.1995.

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11

DING, K. "Wavelet-Based Semi-Fragile Watermarking with Tamper Detection." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 3 (March 1, 2005): 787–90. http://dx.doi.org/10.1093/ietfec/e88-a.3.787.

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12

LEE, Y. C. "Statistical Model-Based VAD Algorithm with Wavelet Transform." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 6 (June 1, 2006): 1594–600. http://dx.doi.org/10.1093/ietfec/e89-a.6.1594.

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13

KYOCHI, Seisuke, Takafumi SHIMIZU, and Masaaki IKEHARA. "A Linear Optimization of Dual-Tree Complex Wavelet Transform." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E94.A, no. 6 (2011): 1386–93. http://dx.doi.org/10.1587/transfun.e94.a.1386.

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14

MATSUO, T. "Proposal of Shift Insensitive Wavelet Decomposition for Stable Analysis." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 8 (August 1, 2005): 2087–99. http://dx.doi.org/10.1093/ietfec/e88-a.8.2087.

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15

LEI, S. F., and Y. K. TUNG. "Wavelet-Based Speech Enhancement Using Time-Adapted Noise Estimation." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 9 (September 1, 2008): 2555–63. http://dx.doi.org/10.1093/ietfec/e91-a.9.2555.

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16

MINAMOTO, Teruya, and Mitsuaki YOSHIHARA. "Visualization of Digital Audio Watermarking Methods Using Interval Wavelet Decomposition." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A, no. 5 (2009): 1363–67. http://dx.doi.org/10.1587/transfun.e92.a.1363.

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17

YARAHMADI, Majid, Seyed-Mehdi KARBASSI, and Ahmad MIRZAEI. "Robust Wavelet Sliding-Mode Control via Time-Variant Sliding Function." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 6 (2010): 1181–89. http://dx.doi.org/10.1587/transfun.e93.a.1181.

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18

QIAO, Y. L. "Fast K Nearest Neighbors Search Algorithm Based on Wavelet Transform." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 8 (August 1, 2006): 2239–43. http://dx.doi.org/10.1093/ietfec/e89-a.8.2239.

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19

YANG, C. H., J. C. WANG, J. F. WANG, and C. W. CHANG. "A Block-Based Architecture for Lifting Scheme Discrete Wavelet Transform." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A, no. 5 (May 1, 2007): 1062–71. http://dx.doi.org/10.1093/ietfec/e90-a.5.1062.

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20

LUNG, S. Y. "Wavelet Feature Selection Using Fuzzy Approach to Text Independent Speaker Recognition." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 3 (March 1, 2005): 779–81. http://dx.doi.org/10.1093/ietfec/e88-a.3.779.

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LUNG, S. Y. "Fuzzy Training Algorithm for Wavelet Codebook Based Text-Independent Speaker Identification." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 6 (June 1, 2005): 1619–21. http://dx.doi.org/10.1093/ietfec/e88-a.6.1619.

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22

ISHIHARA, N., and K. ABE. "Parallel Architecture for 2-D Discrete Wavelet Transform with Low Energy Consumption." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 8 (August 1, 2008): 2068–75. http://dx.doi.org/10.1093/ietfec/e91-a.8.2068.

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23

HSIN, H. C., and T. Y. SUNG. "Adaptive Selection and Rearrangement of Wavelet Packets for Quad-Tree Image Coding." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 9 (September 1, 2008): 2655–62. http://dx.doi.org/10.1093/ietfec/e91-a.9.2655.

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24

CIOLINO, S. "On the Use of Wavelet Packets in Ultra Wideband Pulse Shape Modulation Systems." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 9 (September 1, 2005): 2310–17. http://dx.doi.org/10.1093/ietfec/e88-a.9.2310.

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25

KIDA, Y., and T. KIDA. "Theory of the Optimum Interpolation Approximation in a Shift-Invariant Wavelet and Scaling Subspace." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A, no. 9 (September 1, 2007): 1885–903. http://dx.doi.org/10.1093/ietfec/e90-a.9.1885.

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26

JUNG, G. C. "VLSI Implementation of Lifting Wavelet Transform of JPEG2000 with Efficient RPA(Recursive Pyramid Algorithm) Realization." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 12 (December 1, 2005): 3508–15. http://dx.doi.org/10.1093/ietfec/e88-a.12.3508.

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27

SUNG, T. Y., and H. C. HSIN. "An Efficient Rearrangement of Wavelet Packet Coefficients for Embedded Image Coding Based on SPIHT Algorithm." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A, no. 9 (September 1, 2007): 2014–20. http://dx.doi.org/10.1093/ietfec/e90-a.9.2014.

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28

TSAI, M. J., and C. W. LIN. "Wavelet Based Multipurpose Color Image Watermarking by Using Dual Watermarks with Human Vision System Models." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 6 (June 1, 2008): 1426–37. http://dx.doi.org/10.1093/ietfec/e91-a.6.1426.

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29

TSAI, M. J., and C. H. SHEN. "Differential Energy Based Watermarking Algorithm Using Wavelet Tree Group Modulation (WTGM) and Human Visual System." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 8 (August 1, 2008): 1961–73. http://dx.doi.org/10.1093/ietfec/e91-a.8.1961.

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30

CAO, Peng, Chao WANG, and Longxing SHI. "Memory-Efficient and High-Performance Two-Dimensional Discrete Wavelet Transform Architecture Based on Decomposed Lifting Algorithm." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A, no. 8 (2009): 2000–2008. http://dx.doi.org/10.1587/transfun.e92.a.2000.

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31

KIM, Sunmi, Hirokazu TANAKA, Takahiro OGAWA, and Miki HASEYAMA. "Error-Resilient 3-D Wavelet Video Coding with Duplicated Lowest Sub-Band Coefficients and Two-Step Error Concealment Method." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 11 (2010): 2173–83. http://dx.doi.org/10.1587/transfun.e93.a.2173.

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KIM, Sunmi, Hirokazu TANAKA, Takahiro OGAWA, and Miki HASEYAMA. "Erratum: Error-Resilient 3-D Wavelet Video Coding with Duplicated Lowest Sub-Band Coefficients and Two-Step Error Concealment Method [IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93.A (2010) , No. 11 pp.2173-2183]." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 12 (2010): 2763_e1. http://dx.doi.org/10.1587/transfun.e93.a.2763_e1.

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33

Lira, M. M. S., H. M. deOliveira, and R. J. deSobralCintra. "Elliptic-Cylindrical Wavelets: The Mathieu Wavelets." IEEE Signal Processing Letters 11, no. 1 (January 2004): 52–55. http://dx.doi.org/10.1109/lsp.2003.819341.

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34

Li, Yeqiu, Jianming Lu, Ling Wang, and Takakshi Yahagi. "Removal of Gaussian noise from degraded images in wavelet domain." Electronics and Communications in Japan 91, no. 1 (January 2008): 11–18. http://dx.doi.org/10.1002/ecj.10029.

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35

Karthikeyan, K., S. Ramachandra, S. Vizhian, and Satish Chandra. "Signal Processing, Wavelets and high-speed Image Interpretation of Bird Impact." Defence Science Journal 61, no. 1 (January 6, 2011): 62–71. http://dx.doi.org/10.14429/dsj.61.21.

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36

Astola, J. "Mathematics in signal processing." Signal Processing 16, no. 2 (February 1989): 184. http://dx.doi.org/10.1016/0165-1684(89)90100-x.

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37

Tay, D. B. H. "Daubechies Wavelets as Approximate Hilbert-Pairs?" IEEE Signal Processing Letters 15 (2008): 57–60. http://dx.doi.org/10.1109/lsp.2007.910318.

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38

Yu, Bo, and Xiuzhu Yang. "The Hilbert Transform of B-Spline Wavelets." IEEE Signal Processing Letters 28 (2021): 693–97. http://dx.doi.org/10.1109/lsp.2021.3069122.

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39

Badr, Iman S., Ahmed G. Radwan, El-Sayed M. EL-Rabaie, Lobna A. Said, Ghada M. El Banby, Walid El-Shafai, and Fathi E. Abd El-Samie. "Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion." Digital Signal Processing 116 (September 2021): 103103. http://dx.doi.org/10.1016/j.dsp.2021.103103.

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40

Nai-Xiang Lian, V. Zagorodnov, and Yap-Peng Tan. "Color image denoising using wavelets and minimum cut analysis." IEEE Signal Processing Letters 12, no. 11 (November 2005): 741–44. http://dx.doi.org/10.1109/lsp.2005.856865.

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41

Wakin, Michael. "Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Starck, J.-L., et al; 2010) [Book Reviews]." IEEE Signal Processing Magazine 28, no. 5 (September 2011): 144–46. http://dx.doi.org/10.1109/msp.2011.941842.

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42

Undar, Akif. "Biomedical signal processing." Control Engineering Practice 2, no. 5 (October 1994): 923. http://dx.doi.org/10.1016/0967-0661(94)90562-2.

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43

Takemura, C. M., R. M. Cesar-, R. A. T. Arantes, L. da F. Costa, E. Hingst-Zaher, V. Bonato, and S. F. dos Reis. "Morphometrical data analysis using wavelets." Real-Time Imaging 10, no. 4 (August 2004): 239–50. http://dx.doi.org/10.1016/j.rti.2004.05.006.

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44

Dumitras, A., and G. Moschytz. "From FFTs to wavelets: An interview with C. Sidney Burrus." IEEE Signal Processing Magazine 22, no. 5 (September 2005): 128–31. http://dx.doi.org/10.1109/msp.2005.1511831.

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45

Chappalli, M. B., and N. K. Bose. "Simultaneous noise filtering and super-resolution with second-generation wavelets." IEEE Signal Processing Letters 12, no. 11 (November 2005): 772–75. http://dx.doi.org/10.1109/lsp.2005.856875.

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46

Tay, D. B. H., N. G. Kingsbury, and M. Palaniswami. "Orthonormal Hilbert-Pair of Wavelets With (Almost) Maximum Vanishing Moments." IEEE Signal Processing Letters 13, no. 9 (September 2006): 533–36. http://dx.doi.org/10.1109/lsp.2006.874453.

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47

Roberts, Stephen, Evangelos Roussos, and Rizwan Choudrey. "Hierarchy, priors and wavelets: structure and signal modelling using ICA." Signal Processing 84, no. 2 (February 2004): 283–97. http://dx.doi.org/10.1016/j.sigpro.2003.10.012.

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48

Bose, N. K., S. Lertrattanapanich, and M. B. Chappalli. "Superresolution with second generation wavelets." Signal Processing: Image Communication 19, no. 5 (May 2004): 387–91. http://dx.doi.org/10.1016/j.image.2004.02.001.

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49

Lima, Miguel F. M., J. A. Tenreiro Machado, and Manuel Crisóstomo. "Experimental backlash study in mechanical manipulators." Robotica 29, no. 2 (March 4, 2010): 211–19. http://dx.doi.org/10.1017/s0263574710000056.

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SUMMARYThe behavior of mechanical manipulators with backlash is analyzed. In order to acquire and study the signals an experimental setup is implemented. The signal processing capabilities of the wavelets are used for de-noising the experimental signals and the energy of the obtained components is analyzed. To evaluate the backlash effect upon the robotic system, it is proposed an index based on the pseudo phase plane representation. Several tests are developed that demonstrate the coherence of the results.

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50

Popescu, Theodor D. "Introduction to signal processing." Control Engineering Practice 4, no. 12 (December 1996): 1771–72. http://dx.doi.org/10.1016/s0967-0661(96)90009-x.

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