Hardware Signal Processing Unit for One-Dimensional Variable-Length Discrete Wavelet Transform

Ordaz-Moreno, A.; Romero-Troncoso, René de Jesús; Vite-Frias, Jose Alberto

doi:10.1109/reconfig.2005.21

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…Considering a reference level, J , the original time series can be decomposed and reconstructed as

c^{J} = W_{d} boldq = \prod_{i = 1}^{J} {boldW}^{l}_{d} boldq and boldq = W_{r} c^{J} = \prod_{i = 1}^{J} {boldW}^{l}_{r} c^{J},

where q is ( N × 1) vector of time series; c is a wavelet coefficient, including the approximations and details; and the superscripts l and J stand for the l th and J th reference level. In Equation , W l d and W l r are called decomposition and reconstruction wavelet matrices, respectively, and these matrices can be constructed by the coefficient of decomposition and reconstruction filters …”

Section: Control Algorithmsmentioning

confidence: 99%

“…In Equation (19), W l d and W l r are called decomposition and reconstruction wavelet matrices, respectively, and these matrices can be constructed by the coefficient of decomposition and reconstruction filters. [27][28][29] In a practical implementation, an index matrix, H, can be introduced to assign which approximations or details need to be selected for reconstructing time series 30,31…”

Section: Online Wavelet-based Dsmcmentioning

confidence: 99%

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Combination of decentralized sliding mode control and online wavelet analysis for control of equipment with isolation system

Huang

Loh

2019

Struct Control Health Monit

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Summary Critical equipment and machinery, especially the one in the technology facilities, hospitals, data centers, etc., are vulnerable to strong earthquakes, and most importantly, the failure of the critical equipment will result in countless economic loss, directly and indirectly. For decades, passive control devices in the form of isolation systems between the equipment and its supporting structure have been acknowledged as an effective measure to promote the earthquake resistibility on the basis of theoretical and experimental results. Alternative to passive isolation systems, researchers found that adding a semiactive device to the isolation system can increase damping and lessen the isolator displacement. In order to mitigate the acceleration of equipment, various control algorithms have been developed to reduce the response produced by the internal forces. However, little attention has been given to the balance between the response of equipment and the structural response. In this study, an innovative control algorithm is developed based on decentralized control architecture and wavelet analysis, and this control algorithm is proposed for semiactive isolation systems to protect acceleration‐sensitive equipment against strong earthquakes. Subsequently, a full‐scale specimen is constructed; moreover, a curved surface slider (CSS) system with a magneto‐rheological (MR) damper are placed between the equipment and the floor to suppress the vibration. The experiment is conducted with a feedback control system to validate the performance and efficiency of proposed control algorithm. In the last place, the discussion and some conclusion are also drawn in this paper.

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“…Considering a reference level, J , the original time series can be decomposed and reconstructed as

c^{J} = W_{d} boldq = \prod_{i = 1}^{J} {boldW}^{l}_{d} boldq and boldq = W_{r} c^{J} = \prod_{i = 1}^{J} {boldW}^{l}_{r} c^{J},

Section: Control Algorithmsmentioning

confidence: 99%

Section: Online Wavelet-based Dsmcmentioning

confidence: 99%

Combination of decentralized sliding mode control and online wavelet analysis for control of equipment with isolation system

Huang

Loh

2019

Struct Control Health Monit

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“…The FFT processor is the FFT core described in [10]. The DWT processor is the wavelet core described in [11]. The MCVS processor takes results from FFT and DWT processors for startup transient and steady state data and automatically makes the motor condition diagnosis, based on the signature of the analyzed faults.…”

Section: Fpga-based Fft and Dwt Processormentioning

confidence: 99%

FPGA-Based Online Induction Motor Multiple-Fault Detection with Fused FFT and Wavelet Analysis

Cabal-Yépez

Osornio-Ríos

Romero-Troncoso

et al. 2009

2009 International Conference on Reconfigurable Computing and FPGAs

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Online monitoring of rotary machines, like induction motors, can effectively diagnosis electrical and mechanical faults. The origin of most recurrent faults in rotary machines is in the components: bearings, stator, rotor and others. Different methodologies based on current and vibration monitoring have been proposed using FFT and wavelet analysis for preventive monitoring of induction motors resulting in countless techniques for diagnosing specific faults, arising the necessity for a generalized technique that allows multiple fault detection. This work presents a novel methodology and its FPGA implementation for multiple fault online detection analyzing the current and vibration signals of an induction motor combining FFT and wavelet processing during its startup transient and steady state, precisely performing the identification of different faults like misalignment, unbalance, outerrace bearing defects and broken bars. The results obtained using the proposed methodology show its effectiveness providing a precise diagnosis of the induction motor condition.

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“…Nowadays, with the new technologies of Programmable Logic, the trend is to develop Intellectual Property (IP) cores that perform the needed transformation or processing. Cores have been developed to compute the Fast Fourier Transform (see Vite [3]), a Discrete Wavelet Transform (see Ordaz [4]), and some other Transforms, including the DCT, [5]-[7] however, in the DCT case, the algorithms are implemented for only for small size sequences. Of the fast algorithms, the most suitable for implementation in FPGA devices, for their regular structure, are the Filter-based algorithms [8] and [9], and those that fall into the Decimation in Frequency (DIF) category [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

VHDL Core for the Computation of the One-Dimensional Discrete Cosine Transform

Mendoza-camarena

Romero-Troncoso

2006

2006 IEEE International Conference on Reconfigurable Computing and FPGA's (ReConFig 2006)

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The Discrete Cosine Transform (DCT) is a Fourier related transform which is widely used in digital signal processing specially for signal compression due to its capability of energy compaction and because it also approaches the statistically optimal transform. Implementing the DCT in an ASIC is a design solution that meets the real-time processing requirements but it lacks flexibility. Implementation on FPGAs is a more flexible solution the also achieves the real-time requirements. This paper shows the implementation of a Recursive algorithm to compute the Fast Cosine Transform for variable-length sequences, into a Xilinx Spartan T M − 3 XC3S200 FPGA. The VHDL core was initially developed for a 1024-point DCT, however, due to the utilized structure, it was later modified to compute N -point DCT, being N a power of 2.

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Hardware Signal Processing Unit for One-Dimensional Variable-Length Discrete Wavelet Transform

Cited by 7 publications

References 1 publication

Combination of decentralized sliding mode control and online wavelet analysis for control of equipment with isolation system

Combination of decentralized sliding mode control and online wavelet analysis for control of equipment with isolation system

FPGA-Based Online Induction Motor Multiple-Fault Detection with Fused FFT and Wavelet Analysis

VHDL Core for the Computation of the One-Dimensional Discrete Cosine Transform

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