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

机译:具有实时光学数据压缩的高通量生物细胞分类

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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:需要高吞吐量实时仪器来获取大型数据集以获取罕见事件的检测和分类。通过光子时间拉伸数字化器使得具有记录吞吐量的新类仪器导致了光学流氓波的发现[1],检测稀有癌细胞[2],以及最高的模数转换性能[3]。每秒100万帧的连续操作和少于纳秒的快门速度,时间拉伸相机非常适合筛选血液和其他生物样品。它在血液中检测到血液中的乳腺癌细胞,一次性敏感性,灵敏度[2]。由于其高实时吞吐量,仪器产生了一流的数据 - 相当于每秒几个4K电影 - 压倒数据采集,存储和处理操作。该遗迹需要在光学域中的图像和实时压缩图像的技术。将报告基于翘曲的拉伸变换和非均匀傅里叶域采样的示例。

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