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  1. Shanghai Institute of Optics and Fine Mechanics (SIOM) · Research UpdatesOptics industryOptics and AI63

    Shanghai Institute of Optics and Mechanics Advances Multidimensional Multiplexed Optical Computing

    A team led by Peng Xie at the Shanghai Institute of Optics and Mechanics has made progress in multidimensional multiplexed optical computing, improving the parallelism of optical computing.

    Why we recommend it: The original article provides specific data and details that help assess the actual progress in this direction

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  1. Anhui Institute of Optics and Fine Mechanics (AIOFM) · Research ProgressOptics industryOptics and AI42

    AIOFM Proposes GeoAdapt and Freq-LoRA to Enhance Intelligent Sensing of Aerial Targets in Ground-Based Optical Remote Sensing

    A team at the Optoelectronics Center of the Anhui Institute of Optics and Fine Mechanics has published two results in Remote Sensing, proposing the lightweight keypoint localization framework GeoAdapt and the frequency-domain low-rank adaptation segmentation method Freq-LoRA for aerial target sensing in ground-based optical remote sensing. GeoAdapt has only 5.95M parameters and achieves PCK@0.05D of 89.3%; Freq-LoRA requires only 559K trainable parameters and reaches a mean intersection-over-union of 0.904 across five simulated weather datasets, achieving 96.1% of full fine-tuning performance.

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  1. Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) · Research ProgressOptics industryOptics and AI47

    Changchun Institute of Optics, Fine Mechanics and Physics Dual-Modal Neuromorphic Device Uses Optical Signals to Boost Machine Olfaction Gas Classification Accuracy

    A team led by researcher Li Shaojuan at the Changchun Institute of Optics, Fine Mechanics and Physics has developed a vision-olfaction dual-modal neuromorphic device based on a MoS2/SnS2 floating-gate heterostructure. The device responds to both optical and gas signals and fuses them at the device level. The results were published in Advanced Materials. The device has an active area of 148 µm² and static power consumption of 3.4 µW. With the optical modality introduced, classification accuracy for 12 gases rose from 52.24% to 98.27%, while recognition accuracy for 51 gas mixtures reached 89.32%.

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