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A novel compensation method of insertion losses for wavelet inverse-transform processors using surface acoustic wave
1School of Information Science and Technology, Donghua University, Shanghai 201620, China. luwenkelu@163.com
The Review of Scientific Instruments
|December 2, 2011
Summary
This study introduces a new method to compensate for insertion losses in wavelet inverse-transform processors using surface acoustic wave (SAW) devices. By connecting amplifiers to the processor outputs, significant signal loss reduction is achieved.
Area of Science:
- Electrical Engineering
- Signal Processing
- Materials Science
Background:
- Wavelet inverse-transform processors utilizing Surface Acoustic Wave (SAW) devices often suffer from significant insertion losses.
- These insertion losses present a key challenge, limiting the efficiency and performance of SAW-based processors.
Purpose of the Study:
- To investigate and develop a method for compensating the insertion losses in wavelet inverse-transform processors that employ SAW devices.
- To address the primary limitation hindering the practical application of these processors.
Main Methods:
- A novel compensation technique was developed and implemented.
- The method involves connecting amplifiers to the output terminals of the wavelet inverse-transform processors.
- The study also details the selection criteria for amplifier bandwidths and their adjustment procedures.
Main Results:
- The proposed method effectively compensates for the insertion losses inherent in the wavelet inverse-transform processors.
- The integration of amplifiers at the output stage significantly mitigates signal attenuation.
- Specific guidance on amplifier bandwidth selection and tuning is provided.
Conclusions:
- The developed compensation strategy offers a viable solution to overcome the critical issue of insertion losses in SAW-based wavelet inverse-transform processors.
- This advancement enhances the performance and applicability of SAW devices in signal processing applications.
- The findings pave the way for more efficient and robust signal processing systems.
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