Dr. Depeng Kong Profile

Dr. Depeng Kong

at Xi'an Institute of Optics and Precision Mechanics

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Proceedings Article | 24 November 2023 Paper

Compact lensless optoelectronic convolutional neural network for image classification

Zaikun Zhang, Zhengshang Da, Depeng Kong, Ruiduo Wang, Qiyuan Mu, Shijie Wang, Yi Geng, Zhengquan He

Proceedings Volume 12935, 129350F (2023) https://doi.org/10.1117/12.3000602

KEYWORDS: Convolution, Spatial light modulators, Image classification, Digital micromirror devices, Artificial neural networks, Signal processing, Image processing, Cameras, Convolutional neural networks, Power consumption

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Recently, free-space optical neural networks (ONNs) have gained extensive interest as emerging machine learning platforms for implementing artificial intelligence tasks, such as image classification. Despite various optical implementations of electronic neural networks (ENNs), the bulky volume of optical components remains challenging to deploy edge devices, such as Internet of Things peripherals, wearable devices, and camera. To address this problem, we propose a compact lensless optoelectronic convolutional neural network (LOE-CNN) architecture with a lensless optical analog processor utilizing a single optimized diffractive phase mask (DPM) to perform convolution operations without Fourier lens. Comparing the processor with a commercially available NVIDIA A100 Tensor Core GPU in terms of speed and power, indicates the optical computing platform enables to replace the electronic processor in latency reduction and energy savings. Furthermore, we compare the LOE-CNN with two all-electronic neural networks (i.e., fully connected neural network [FC-NN] and convolutional neural network [CNN]) over the Modified National Institute of Standards and Technology (MNIST) dataset and Fashion-MNIST dataset, respectively, and demonstrate that the LOE-CNN can be functionally comparable to existing electronic counterparts in classification performance. My study not only opens up new application prospects for free-space ONNs based on compact lensless single-chip convolution processor, but also facilitates the development of ONNs-based smart devices.

Proceedings Article | 2 December 2020 Paper

High-speed focusing and scanning light through multimode fiber

Yi Geng, Zaikun Zhang, Zhengquan He, Hui Chen, Ruiduo Wang, Depeng Kong, Haitao Guo

Proceedings Volume 11717, 1171715 (2020) https://doi.org/10.1117/12.2586318

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The image distortions caused by the inherent mode dispersion and coupling of the multimode fiber (MMF) lead its output light field to be scattered and prevent it from applicating in endoscopy. Although various wavefront shaping methods have been proposed to overcome these image distortions and form the focused spots through the MMF, they a re usually time-consuming due to the multiple iterations and tedious calculation. In this paper, we present a binary amplitude-only modulation parallel coordinate algorithm for focusing and scanning light through a multimode fiber (MMF) based on the digital micro-mirror device (DMD) in a reference-free multimode fiber imaging system. In principle, our algorithm is capable of efficiently calculating the masks to be added to DMD for yielding a series of tightly focused spots; and for the same number of modulation sub-regions, our method is more than M (the number of focused spots) times faster than the amplitude iterative optimization algorithm. In the experiment, efficient light focusing and scanning at the distal end of the MMF without the iteration process are demonstrated. Furthermore, we demonstrate that the proposed method can also be extended to focus and scan light at multiple planes along the axial direction by just modifying the input wavefront accordingly. We predict the high-speed focusing method through the MMF might have the potential application for fast spot-scanning imaging.

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