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Recent Advances and Challenges of 2D Fourier Transform Computational Accelerator Using GHz Ultrasonics
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.
Acoustic computing devices, including switches, logic gates, differentiator and integrator, have attracted extensive attentions in both academic research and engineering. However, no scheme of acoustic computing device with more complex functionality has been proposed, such as ordinary differential equation (ODE) solver. Here, we propose an acoustic analog computing (AAC) system based on three cascaded metasurfaces to solve the nth-order ODEs. The metasurfaces are constructed with layered labyrinthine units featuring broad amplitude and phase modulation ranges. The simulated transmitted pressure of the AAC system agrees well with the theoretical solution of ODE, demonstrating the excellent functionality. Unlike the optical ODE solver based on differentiator or integrator, whose geometry becomes more complicated for solving higher order ODE, the proposed AAC system with fixed geometry can be designed for arbitrary nth-order ODE in principle. The proposal may find applications in various scenarios such as acoustic communication, analog computing and signal processing.
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.
Correction of Transmitters' Pixel Values in an Ultrasonic Fourier Transform Analog Computing Apparatus
- X H M Tan
Non-linearity Corrections of Tapered BAW Transducers for Accurate FFT Computation Using Ultrasonic Wavefront Computing
- Z Yang