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High-throughput biological cell classification featuring real-time optical data compression

机译:高通量生物细胞分类,具有实时光学数据压缩功能

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High throughput real-time instruments are needed to acquire large data sets for detection and classification of rare events. Enabled by the photonic time stretch digitizer, a new class of instruments with record throughputs have led to the discovery of optical rogue waves [1], detection of rare cancer cells [2], and the highest analog-to-digital conversion performance ever achieved [3]. Featuring continuous operation at 100 million frames per second and shutter speed of less than a nanosecond, the time stretch camera is ideally suited for screening of blood and other biological samples. It has enabled detection of breast cancer cells in blood with record, one-in-a-million, sensitivity [2]. Owing to their high real-time throughput, instruments produce a torrent of data - equivalent to several 4K movies per second - that overwhelm data acquisition, storage, and processing operations. This predicament calls for technologies that compress images in optical domain and in real-time. An example of this, based on warped stretch transformation and non-uniform Fourier domain sampling will be reported.
机译:需要高吞吐量的实时仪器来获取大型数据集,以检测和分类稀有事件。在光子时间拉伸数字化仪的支持下,具有创纪录吞吐量的新型仪器已导致发现光流浪[1],检测稀有癌细胞[2]以及有史以来最高的模数转换性能[3]。时间拉伸相机具有每秒1亿帧的连续操作能力和不到1纳秒的快门速度,非常适合筛查血液和其他生物样品。它能够以百万分之一的记录来检测血液中的乳腺癌细胞[2]。由于其高实时吞吐量,仪器产生的数据量激增-相当于每秒4K电影-淹没了数据采集,存储和处理操作。这种困境要求在光域中实时压缩图像的技术。一个基于翘曲拉伸变换和非均匀傅立叶域采样的示例。

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