Solution to the influence of the MSSW propagating velocity on the bandwidths of the single-scale wavelet-transform processor using MSSW device

Lu, Wenke; Chen, Zhu; Kuang, Lun; Zhang, Ting; Zhang, Jingduan

doi:10.1016/j.ultras.2011.07.008

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…In addition, to suppress the side lobes of SAW oscillator, an electrode-overlap envelope is weighted to the input transducer according to the cosine square function as shown in Fig. 2 [21], [22]. The fabrication layout is designed by L-Edit8.2, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Compensated SAW Yarn Tension Sensor

Lü

Chen

2014

IEEE Trans. Instrum. Meas.

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The measurement and control of yarn tension is important in textile technology. This paper presents a two channel architecture surface acoustic wave yarn tension sensor, which is able to provide a sensitivity about two times bigger than the previous one. The design and the fabrication of the devices are also presented. A mathematical analysis shows that the output frequency of the sensor varies linearly with the yarn tension. To test the performance of the sensor, an experiment is carried out to obtain the output frequencies in different yarn tension. The results show that the sensor has a sensitivity of 8234 Hz/N (80.75 Hz/gf), and the sensor is capable of measuring a range of 0-0.196 N (20 gf) yarn tension with a relative error of 2% and a hysteresis error of 3%.Index Terms-Surface acoustic wave (SAW), yarn tension sensor.

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Section: Methodsmentioning

confidence: 99%

Compensated SAW Yarn Tension Sensor

Lü

Chen

2014

IEEE Trans. Instrum. Meas.

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“…The dyadic wavelet transform of signal

f false(t false)

is [10, 12, 14–18, 20]

\begin{matrix} right leftthickmathspace.5em \\ W T_{2^{k}} (τ) & = f (t) * ψ_{2^{k}} (t) = \int_{R} f false(t false) 2^{- false(k / 2 false)} ψ false(false(τ - t false) / 2^{k}) d t \\ = 2^{- (k / 2)} \int_{R} f false(t false) ψ ((τ - t) / 2^{k}) d t \end{matrix}

The equation of the dyadic wavelet inverse‐transform is [11, 13, 18–20]

\begin{matrix} right leftthickmathspace.5em \\ y (t) & = \frac{2}{A + B} \sum_{k \in z} \int_{R} W T_{2^{K}} false(τ false) ψ_{2^{k}, τ} false(t false) normald τ \\ = \frac{2}{A + B} \sum_{k \in z} \int_{R} W T_{2^{K}} false(τ false) 2^{- false(k / 2 false)} ψ ()(, \frac{t - τ}{2^{k}}) normald τ \end{matrix}

where

…”

Section: Principlementioning

confidence: 99%

“…For example, its algorithm is complicated and it has the low-execution efficiency. A number of wavelet transform processors and wavelet inverse-transform processors (WITPs) including very large scale integration [6,7], optical devices [8,9], magnetostatic surface wave devices [10] and surface acoustic wave (SAW) devices [11][12][13][14][15][16][17][18][19] have been used to solve the above mentioned problems. In the case of SAW devices, if the electrode envelopes of the input or output IDTs are designed based on the envelope of the wavelet function, the surface acoustic wave type (SAWT) WITP [11][12][13][14][15][16][17][18][19] can be designed and fabricated.…”

Section: Introductionmentioning

confidence: 99%

Surface acoustic wave type electrode‐area‐weighted wavelet inverse‐transform processors with phase compensation

Gao

2017

IET Circuits, Devices & Syst

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The main purpose of this research is to investigate a novel implementation method for a surface acoustic wave type (SAWT) electrode-area-weighted (EAW) wavelet inverse-transform processor (WITP). The method of EAW is that the electrode areas of the input and output interdigital transducers (IDTs) are proportional to the envelope areas of the wavelet function (i.e. the two IDTs are identical). By this method, the SAWT EAW WITP is fabricated on X-112°Y LiTaO 3 substrate material. In the study, the diffraction problem and phase difference as two key problems are presented and the solution to two problems are implemented.

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“…The wavelet transform (WT) has achieved amazing success as a very effective mathematical tool in the scientific and information industries with a wide range of applications, such as signal analysis, image processing, medical imaging and diagnosis, electronic warfare and weapons, the artificial synthesis of music and language, seismic data processing, large have developed a series of WTPs that works efficiently in boosting the application of wavelet technology [10][11][12][13]. The development of WTPs theory has been promoted by the widespread use of WTPs, but research into theoretical models and simulations of WTPs has received little attention.…”

Section: Introductionmentioning

confidence: 99%

An efficient modeling approach for wavelet transform processors using surface acoustic wave devices

Yang

Gao

2023

Meas. Sci. Technol.

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Wavelet transform processors (WTPs) using surface acoustic wave (SAW) devices provide an effective solution to utilize wavelet transform technology in practical applications. This work proposes a novel model of wavelet transform processors for predicting their performance characteristics, which might be utilized for single-electrode type and double-electrode type interdigital transducers, as there is currently no universal, quick, and accurate simulation technology for WTPs (IDTs). The admittance matrix, transfer function, and insertion loss for bothIDTs and WTPs in conjunction with a two-port electrical network are all evaluated by the model.Furthermore, some WTPs samples with a piezoelectric substrate of ST-X quartz and IDTs of Al thin film were fabricated to validate the accuracy of the simulation, with a center frequency and a scale of 60 MHz and 0.215, respectively. A comparison of the simulated and measured results found that the relative error of the frequency is only 0.67%, the maximum relative error of the bandwidth is 5.79% and the relative error of the insertion loss is only 1.24%. The experimental results show that the model is extremely accurate in effective frequency band, and its application will accelerate the design and production of WTPs, promoting the use of WTPs in signal processing systems.

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Solution to the influence of the MSSW propagating velocity on the bandwidths of the single-scale wavelet-transform processor using MSSW device

Cited by 10 publications

References 12 publications

Compensated SAW Yarn Tension Sensor

Compensated SAW Yarn Tension Sensor

Surface acoustic wave type electrode‐area‐weighted wavelet inverse‐transform processors with phase compensation

An efficient modeling approach for wavelet transform processors using surface acoustic wave devices

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