Machine Learning and Modeling of Ultrasonic Signals for High-Fidelity Data Compression
Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance, and medical diagnosis. These applications require large volumes of data to be processed, stored, and/or transmitted in real time. It is essential to compress the ultrasonic RF signal without...
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| Published in: | IEEE International Ultrasonics Symposium (Online) pp. 1 - 9 |
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| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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10.10.2022
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| ISSN: | 1948-5727 |
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| Abstract | Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance, and medical diagnosis. These applications require large volumes of data to be processed, stored, and/or transmitted in real time. It is essential to compress the ultrasonic RF signal without inadvertently degrading desirable signal features. This study explores the development of learning models for massive data compression based on wavelet packet transformation, using machine learning techniques. Furthermore, this study utilizes the fast chirplet transform algorithm to successively estimate broadband, narrowband, symmetric, skewed, nondispersive, or dispersive echoes. These parameters not only have significant physical interpretations for radar, sonar, seismic, and ultrasonic applications but also, yield a method for efficient and high-precision data compression. Signal modeling and parameter estimation of the nonstationary ultrasonic echoes are critical for image analysis, target detection, and object recognition. The objective of this study is to design computationally efficient algorithms and the implementation of 3D ultrasonic data compression. |
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| AbstractList | Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance, and medical diagnosis. These applications require large volumes of data to be processed, stored, and/or transmitted in real time. It is essential to compress the ultrasonic RF signal without inadvertently degrading desirable signal features. This study explores the development of learning models for massive data compression based on wavelet packet transformation, using machine learning techniques. Furthermore, this study utilizes the fast chirplet transform algorithm to successively estimate broadband, narrowband, symmetric, skewed, nondispersive, or dispersive echoes. These parameters not only have significant physical interpretations for radar, sonar, seismic, and ultrasonic applications but also, yield a method for efficient and high-precision data compression. Signal modeling and parameter estimation of the nonstationary ultrasonic echoes are critical for image analysis, target detection, and object recognition. The objective of this study is to design computationally efficient algorithms and the implementation of 3D ultrasonic data compression. |
| Author | Zhang, Xin Wang, Boyang Saniie, Jafar Govindan, Pramod Oruklu, Erdal Lu, Yufeng |
| Author_xml | – sequence: 1 givenname: Jafar surname: Saniie fullname: Saniie, Jafar organization: Embedded Computing and Signal Processing Research Laboratory – sequence: 2 givenname: Pramod surname: Govindan fullname: Govindan, Pramod organization: Embedded Computing and Signal Processing Research Laboratory – sequence: 3 givenname: Boyang surname: Wang fullname: Wang, Boyang organization: Embedded Computing and Signal Processing Research Laboratory – sequence: 4 givenname: Xin surname: Zhang fullname: Zhang, Xin organization: Embedded Computing and Signal Processing Research Laboratory – sequence: 5 givenname: Yufeng surname: Lu fullname: Lu, Yufeng organization: Embedded Computing and Signal Processing Research Laboratory – sequence: 6 givenname: Erdal surname: Oruklu fullname: Oruklu, Erdal organization: Embedded Computing and Signal Processing Research Laboratory |
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| Snippet | Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance, and medical diagnosis. These applications require... |
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| SubjectTerms | Analytical models Chirp Computational modeling Data compression Machine learning Machine learning algorithms Three-dimensional displays |
| Title | Machine Learning and Modeling of Ultrasonic Signals for High-Fidelity Data Compression |
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