摘要
We introduce for the first time the concept of anamorphic temporal imaging and its application to analog data compression in a time lens.Placed in front of a conventional spectrometer,this technique enhances the spectral resolution and the update rate.More importantly,it performs real-time optical data compression to reduce the volume of the generated digital data.Our technique introduces the concept of the warped time lens.We show that a specific class of warped time-to-frequency mapping operations combined with the nonlinear operation inherent in a photo-detector compresses the time-bandwidth product in a temporal imaging system.We employ our newly introduced Stretched Modulation Distribution to design the warp profile.Using this method,the narrow spectral features beyond the spectrometer resolution can be captured and at the same time the output bandwidth and hence the record length is minimized.The reduction in the record length results in higher update rate for the spectrometer without sacrificing the spectral resolution or bandwidth.Our method also benefits from amplitude and phase detection to recover the input complex-field spectrum.By compressing the time-bandwidth product,anamorphictemporal imaging addresses the Big Data problem in such high-throughput instruments.
We introduce for the first time the concept of anamorphic temporal imaging and its application to analog data compression in a time lens. Placed in front of a conventional spectrometer, this technique enhances the spectral resolution and the update rate. More importantly, it performs real-time optical data compression to reduce the volume of the generated digital data. Our technique introduces the concept of the warped time lens. We show that a specific class of warped time-to-frequency mapping operations combined with the nonlinear operation inherent in a photo-detector compresses the time-bandwidth product in a temporal imaging system. We employ our newly introduced Stretched Modulation Distribution to design the warp profile. Using this method, the narrow spectral features beyond the spectrometer resolution can be captured and at the same time the output bandwidth and hence the record length is minimized. The reduction in the record length results in higher update rate for the spectrometer without sacrificing the spectral resolution or bandwidth. Our method also benefits from amplitude and phase detection to recover the input complexfield spectrum. By compressing the time-bandwidth product, anamorphictemporal imaging addresses the Big Data problem in such highthroughput instruments.
