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1. High-fidelity and high-speed wavefront shaping by leveraging complex media

   https://doi.org/10.1126/sciadv.adn2846  

   Yu, Li-Yu; You, Sixian (July 2024, Science Advances)

   High-precision light manipulation is crucial for delivering information through complex media. However, existing spatial light modulation devices face a fundamental speed-fidelity tradeoff. Digital micromirror devices have emerged as a promising candidate for high-speed wavefront shaping but at the cost of compromised fidelity due to the limited control degrees of freedom. Here, we leverage the sparse-to-random transformation through complex media to overcome the dimensionality limitation of spatial light modulation devices. We demonstrate that pattern compression by sparsity-constrained wavefront optimization allows sparse and robust wavefront representations in complex media, improving the projection fidelity without sacrificing frame rate, hardware complexity, or optimization time. Our method is generalizable to different pattern types and complex media, supporting consistent performance with up to 89% and 126% improvements in projection accuracy and speckle suppression, respectively. The proposed optimization framework could enable high-fidelity high-speed wavefront shaping through different scattering media and platforms without changes to the existing holographic setups, facilitating a wide range of physics and real-world applications. 

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2. Full-Speed Testing of Silicon Photonic Electro-Optic Modulators from Picowatt-level Scattered Light

   https://doi.org/10.1364/OFC.2020.Th4A.7  

   Wang, Xiaoxi; Korzh, Boris A.; Shaw, Matthew D.; Mookherjea, Shayan (March 2020, Optical Fiber Communication Conference)

   null (Ed.)

   We demonstrate a technique for measuring the full-speed performance of integrated modulators from ultraweak surface-coupled and scattered light. This can enable rapid characterization of unpackaged, high-speed wafer-scale integrated photonics without test ports or special fabrication. 

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3. High-Speed Multidimensional Optical Computing

   https://doi.org/10.1364/FIO.2023.JW4A.74  

   Fardoost, Alireza; Vanani, Fatemeh Ghaedi; Zhu, Zheyuan; Doerr, Christopher; Pang, Shuo; Li, Guifang (October 2023, Optica Publishing Group)

   A coherent multi-dimensional photonic tensor accelerator performing high-speed matrix-matrix multiplication is proposed and demonstrated. A pattern recognition experiment is demonstrated at a 25Gbps modulation speed exploiting orthogonal dimensions of light including time, wavelength, and spatial mode. 

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4. High speed light microLEDs for visible wavelength communication

   Pezeshki, B; Kalman, R; Tselikov, A; Danesh, C (March 2021, Proceedings of the Society of Photooptical Instrumentation Engineers)

   null (Ed.)

   Visible wavelength data communication is of interest for short distance chip-to-chip interconnects and free-space links such as Li-Fi, where modulated sources are incorporated in lighting systems. For the former, both high modulation bandwidth and low power consumption are critical. At Avicena, we have developed efficient high-speed light-emitting structures capable of multi-Gb/s NRZ modulation, which operate down to a few microamps of drive current. We have demonstrated a high speed and low energy optical communication links using these novel devices. 

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5. Pix2HDR - A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos

   https://doi.org/10.1109/TPAMI.2024.3410140  

   Wang, Caixin; Zhang, Jie; Wilson, Matthew A; Etienne-Cummings, Ralph (January 2024, IEEE Transactions on Pattern Analysis and Machine Intelligence)

   Abstract—Accurately capturing dynamic scenes with wideranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera’s frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these frames can still pose complications for HDR fusion algorithms, resulting in artifacts. Instead of frame-based exposures, we sample the videos using individual pixels at varying exposures and phase offsets. Implemented on a monochrome pixel-wise programmable image sensor, our sampling pattern captures fast motion at a high dynamic range. We then transform pixel-wise outputs into an HDR video using end-to-end learned weights from deep neural networks, achieving high spatiotemporal resolution with minimized motion blurring. We demonstrate aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under low-light conditions and against bright backgrounds — both challenging conditions for conventional cameras. By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system’s adaptability and performance in dynamic conditions. Index Terms—High-dynamic-range video, high-speed imaging, CMOS image sensors, programmable sensors, deep learning, convolutional neural networks. 

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