Spectral estimation based on acoustic backscatter from a motionless stochastic medium is described for characterization of aberration in ultrasonic imaging. The underlying assumptions for the estimation are: The correlation length of the medium is short compared to the length of the transmitted acoustic pulse, an isoplanatic region of sufficient size exists around the focal point, and the backscatter can be modeled as an ergodic stochastic process. The motivation for this work is ultrasonic imaging with aberration correction. Measurements were performed using a two-dimensional array system with 80 x 80 transducer elements and an element pitch of 0.6 mm. The f number for the measurements was 1.2 and the center frequency was 3.0 MHz with a 53% bandwidth. Relative phase of aberration was extracted from estimated cross spectra using a robust least-mean-square-error method based on an orthogonal expansion of the phase differences of neighboring wave forms as a function of frequency. Estimates of cross-spectrum phase from measurements of random scattering through a tissue-mimicking aberrator have confidence bands approximately +/- 5 degrees wide. Both phase and magnitude are in good agreement with a reference characterization obtained from a point scatterer.
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http://dx.doi.org/10.1121/1.1760799 | DOI Listing |
J Phys Chem B
January 2025
Chemical, Biological and Macromolecular Sciences, S. N. Bose National Centre for Basic Sciences, J. D. Block, Sec.III, Salt Lake, Kolkata, West Bengal 700 098, India.
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Institute of Smart Systems and Services, Pforzheim University, 75175 Pforzheim, Germany.
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State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
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View Article and Find Full Text PDFPlants (Basel)
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Department of Biophysics, National Research Lobachevsky, State University of Nizhny Novgorod, 23 Gagarin Avenue, 603022 Nizhny Novgorod, Russia.
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College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Spectroscopic technology is an effective method for estimating rice chlorophyll content. However, redundant spectral information and the complex background of rice in situ challenge the accuracy and robustness of the estimation. To address this problem, this study proposed a band selection method combining spectral color characteristics and established a convolutional neural network (CNN) model based on this method to estimate chlorophyll content of rice for black (background-free), clear, muddy, and green algae-covered backgrounds.
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