Optical Signal Processing For Data Compression In Ultrafast Measurement

Today the world is filled with continuous deluge of digital information which are ever increasing by every fraction of second. Real-time analog information such as images, RF signals needs to be sampled and quantized to represent in digital domain with help of measurement systems for information analysis, further post processing and storage. Photonics offers various advantages in terms of high bandwidth, security, immunity to electromagnetic interference, reduction in frequency dependant loss as compared to conventional electronic measurement systems. However the large bandwidth data needs to be acquired as per Nyquist principle requiring high bandwidth electronic sampler and digitizer. To address this problem, Photonic Time Stretch has been introduced to reduce the need for high speed electronic measurement equipment by significantly slowing down the speed of sampling signal. However, this generates massive data volume. Photonics-assisted methods such as Anamorphic Stretch Transform, Compressed Sensing and Fourier spectrum acquisition sensing have been addressed to achieve data compression while sampling the information. In this thesis, novel photonic implementations of each of these methods have been investigated through numerical and experimental demonstrations. The main contribution of this thesis include (1) Application of photonic implementation of compressed sensing for Optical Coherence Tomography, Fiber Bragg Grating enabled signal sensing and blind spectrum sensing applications (2) Photonic compressed sensing enabled ultra-fast imaging system (3) Fourier spectrum acquisition for RF spectrum sensing with all-optical approach (4) Adaptive non-uniform photonic time stretch methods using anamorphic stretch transform to reduce the the number of samples to be measured

Adaptive non-uniform photonic time stretch for blind RF signal detection with compressed time-bandwidth product

Photonic time stretch significantly extends the effective bandwidth of existing analog-to-digital convertors by slowing down the input high-speed RF signals. Non-uniform photonic time stretch further enables time bandwidth product reduction in RF signal detection by selectively stretching high-frequency features more. However, it requires the prior knowledge of spectral-temporal distribution of the input RF signal and has to reconfigure the time stretch filter for different RF input signals. Here we propose for the first time an adaptive non-uniform photonic time stretch method based on microwave photonics pre-stretching that achieves blind detection of high-speed RF signals with reduced time bandwidth product. Non-uniform photonic time stretch using both quadratic and cubic group delay response has been demonstrated and time bandwidth product compression ratios of 72% and 56% have been achieved respectively

Optical Signal Processing For Data Compression In Ultrafast Measurement

Today the world is filled with continuous deluge of digital information which are ever increasing by every fraction of second. Real-time analog information such as images, RF signals needs to be sampled and quantized to represent in digital domain with help of measurement systems for information analysis, further post processing and storage. Photonics offers various advantages in terms of high bandwidth, security, immunity to electromagnetic interference, reduction in frequency dependant loss as compared to conventional electronic measurement systems. However the large bandwidth data needs to be acquired as per Nyquist principle requiring high bandwidth electronic sampler and digitizer. To address this problem, Photonic Time Stretch has been introduced to reduce the need for high speed electronic measurement equipment by significantly slowing down the speed of sampling signal. However, this generates massive data volume. Photonics-assisted methods such as Anamorphic Stretch Transform, Compressed Sensing and Fourier spectrum acquisition sensing have been addressed to achieve data compression while sampling the information. In this thesis, novel photonic implementations of each of these methods have been investigated through numerical and experimental demonstrations. The main contribution of this thesis include (1) Application of photonic implementation of compressed sensing for Optical Coherence Tomography, Fiber Bragg Grating enabled signal sensing and blind spectrum sensing applications (2) Photonic compressed sensing enabled ultra-fast imaging system (3) Fourier spectrum acquisition for RF spectrum sensing with all-optical approach (4) Adaptive non-uniform photonic time stretch methods using anamorphic stretch transform to reduce the the number of samples to be measured

Edge Detection in SAR images using phase stretch transform

In this work a novel approach to edge detection on Synthetic Aperture Radar (SAR) images is introduced. The proposed method uses an optics inspired transform which emulates the diffraction of an image through a medium with nonlinear dispersive properties. The experimental results show that the output of the introduced Phase Stretch Transform (PST) in conjunction with further morphological operations can be effectively used for image edge detection

High throughput photonic time stretch optical coherence tomography with data compression

Photonic time stretch enables real time high throughput optical coherence tomography (OCT), but with massive data volume being a real challenge. In this paper, data compression in high throughput optical time stretch OCT has been explored and experimentally demonstrated. This is made possible by exploiting spectral sparsity of encoded optical pulse spectrum using compressive sensing (CS) approach. Both randomization and integration have been implemented in the optical domain avoiding an electronic bottleneck. A data compression ratio of 66% has been achieved in high throughput OCT measurements with 1.51 MHz axial scan rate using greatly reduced data sampling rate of 50 MS/s. Potential to improve compression ratio has been exploited. In addition, using a dual pulse integration method, capability of improving frequency measurement resolution in the proposed system has been demonstrated. A number of optimization algorithms for the reconstruction of the frequency-domain OCT signals have been compared in terms of reconstruction accuracy and efficiency. Our results show that the L1 Magic implementation of the primal-dual interior point method offers the best compromise between accuracy and reconstruction time of the time-stretch OCT signal tested

High throughput photonic time stretch optical coherence tomography with data compression

Photonic time stretch enables real time high throughput optical coherence tomography (OCT), but with massive data volume being a real challenge. In this paper, data compression in high throughput optical time stretch OCT has been explored and experimentally demonstrated. This is made possible by exploiting spectral sparsity of encoded optical pulse spectrum using compressive sensing (CS) approach. Both randomization and integration have been implemented in the optical domain avoiding an electronic bottleneck. A data compression ratio of 66% has been achieved in high throughput OCT measurements with 1.51 MHz axial scan rate using greatly reduced data sampling rate of 50 MS/s. Potential to improve compression ratio has been exploited. In addition, using a dual pulse integration method, capability of improving frequency measurement resolution in the proposed system has been demonstrated. A number of optimization algorithms for the reconstruction of the frequency-domain OCT signals have been compared in terms of reconstruction accuracy and efficiency. Our results show that the L1 Magic implementation of the primal-dual interior point method offers the best compromise between accuracy and reconstruction time of the time-stretch OCT signal tested

Principle and recent development in photonic time-stretch imaging

Inspiring development in optical imaging enables great applications in the science and engineering industry, especially in the medical imaging area. Photonic time-stretch imaging is one emerging innovation that attracted a wide range of attention due to its principle of one-to-one-to-one mapping among space-wavelength-time using dispersive medium both in spatial and time domains. The ultrafast imaging speed of the photonics time-stretch imaging technique achieves an ultrahigh frame rate of tens of millions of frames per second, which exceeds the traditional imaging methods in several orders of magnitudes. Additionally, regarding ultrafast optical signal processing, it can combine several other optical technologies, such as compressive sensing, nonlinear processing, and deep learning. In this paper, we review the principle and recent development of photonic time-stretch imaging and discuss the future trends