Flexible architecture for the implementation of the two-dimensional discrete wavelet transform (2D-DWT) oriented to FPGA devices

Colom-Palero, Ricardo; Gironés, Rafael Gadea; Ballester-Merelo, Francisco José; Martínez-Peiró, Marcos

doi:10.1016/j.micpro.2004.05.003

Cited by 19 publications

(7 citation statements)

References 13 publications

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“…One significant advantage of using the DWT approach for signal processing is to design high and low pass filters as digital filters on programmable logic devices, such as modern FPGAs. This enables the algorithms implementation directly in hardware, which leads to the high performance needed in real time signal processing applications [13][14][15] and ultimately to embedded monitoring systems.…”

Section: Lamb Waves and Signal Processing Techniquesmentioning

confidence: 99%

An effective structural health monitoring methodology for damage isolation based on multisensor arrangements

Souza

Nóbrega

2016

J Braz. Soc. Mech. Sci. Eng.

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Section: Lamb Waves and Signal Processing Techniquesmentioning

confidence: 99%

An effective structural health monitoring methodology for damage isolation based on multisensor arrangements

Souza

Nóbrega

2016

J Braz. Soc. Mech. Sci. Eng.

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“…DWT has an advantage of being easy to use. It also, provides flexible multi-resolution analysis of an image [19][20][21]. DWT used in signal and image processing in order to obtain the useful information from the signal or image that can be used for feature extraction stage.…”

Section: Acquire Imagesmentioning

confidence: 99%

“…In this study, a novel implementation of DWT and PCA is considered. DWT, see for example [19][20][21], is normally used in signal and image processing in order to obtain the useful information from the signal or image that can be used for feature extraction stage. The Principal Component Analysis (PCA) is widely used in image processing, particularly in the area of face recognition and compression [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

Elgargni

Al‐Habaibeh

Lotfi

2014

Int J Adv Manuf Technol

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The implementation of computerised condition monitoring systems for the detection cutting tools' correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a noncontact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool's condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms.

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“…Let Sln) and Wln) be the approximation and detail , respectively , of the signal at level i . The approximation of the signal at level i+ 1 is computed using [ 5]:…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Face recognition based on DWT/DCT and SVM

Wang

Jiang

2010

2010 International Conference on Computer Application and System Modeling (ICCASM 2010)

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discrete wavelet transform has both g ood qualities in time domain and frequency domain which is an ideal tool in analyzin g unsteady si g nals. Discrete cosine transform is one of the approaches used in ima g e compressin g which is also used to extract features. This paper proposes a combined feature extraction method which is based on DWT and DCT for face reco g nition. First the ori g inal face ima g e is decomposed by 2dimentional DWT, then the 2-dimentional DCT is applied to the low frequency approximation ima g e obtained from previous step. In the end, usin g the DCT coefficient, a SVM classifier is built and face ima g e can be reco g nized. The experiment carried on ORL-DATABASE shows that the above-mentioned feature extraction method can g ain hi g her reco g nition rate than the traditional PCA al g orithm.

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Flexible architecture for the implementation of the two-dimensional discrete wavelet transform (2D-DWT) oriented to FPGA devices

Cited by 19 publications

References 13 publications

An effective structural health monitoring methodology for damage isolation based on multisensor arrangements

An effective structural health monitoring methodology for damage isolation based on multisensor arrangements

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

Face recognition based on DWT/DCT and SVM

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