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Novel Simulation Of Ultrasonic Attenuation In Fourier Transform Analog Computing
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Wave-based analog computing is a new computing paradigm heralded as a potentially superior alternative to existing digital computers. Currently, there are optical and low-frequency acoustic analog Fourier transformers. However, the former suffers from phase retrieval issues, and the latter is too physically bulky for integration into CMOS-compatible chips. This paper presents a solution to these problems: the Ultrasonic Fourier Transform Analog Computing System (UFT-ACS), a metalens-based analog computer that utilizes ultrasonic waves to perform Fourier transform calculations. Through wave propagation simulations on MATLAB, the UFT-ACS has been shown to calculate the Fourier transform of various input functions with a high degree of accuracy. Moreover, the optimal selection of parameters through sufficient zero padding and appropriate truncation and bandlimiting to minimize errors is also discussed.
In the last few years, deep learning has lead to very good performance on a
variety of problems, such as object recognition, speech recognition and natural
language processing. Among different types of deep neural networks,
convolutional neural networks have been most extensively studied. Due to the
lack of training data and computing power in early days, it is hard to train a
large high-capacity convolutional neural network without overfitting. Recently,
with the rapid growth of data size and the increasing power of graphics
processor unit, many researchers have improved the convolutional neural
networks and achieved state-of-the-art results on various tasks. In this paper,
we provide a broad survey of the recent advances in convolutional neural
networks. Besides, we also introduce some applications of convolutional neural
networks in computer vision.
The ultrasonic attenuation in fused silica was measured by the Bragg diffraction of a 6328‐Å He‐Ne laser beam; two complementary techniques were used to obtain room‐temperature data over the frequency range 200–980 MHz. The frequency dependence of the attenuation is given by f 1.97 and f 1.98, for longitudinal and transverse waves, respectively. If an exact square‐law dependence is assumed, the material loss at 25.0°C can be characterized by the constants B ( longitudinal ) = (6.1±0.2) × 10 −18 dB ⋅ sec , and B ( transverse ) = (5.7±0.2) × 10 −18 dB ⋅ sec , where A ( dB /μ sec ) = B f 2 . The acoustic‐wave reflection losses at the free surface and transducer‐bond surface of the sample were evaluated. Although in the former case the loss was negligible, in the latter large reflection losses were observed, in spite of the low conversion efficiency of the transducer. A study of the angular distribution of the acoustic intensity for the generated and reflected beams showed that this loss was mainly dissipative, although a deformation of the plane wavefront was also a contributing factor.