A local algorithm for the computation of image velocity via constructive interference of global Fourier components

A novel Fourier-based technique for local motion detection from image sequences is proposed. In this method, the instantaneous velocities of local image points are inferred directly from the global 3D Fourier components of the image sequence. This is done by selecting those velocities for which the superposition of the corresponding Fourier gratings leads to constructive interference at the image point. Hence, image velocities can be assigned locally even though position is computed from the phases and amplitudes of global Fourier components (spanning the whole image sequence) that have been filtered based on the motion-constraint equation, reducing certain aperture effects typically arising from windowing in other methods. Regularization is introduced for sequences having smooth flow fields. Aperture effects and their effect on optic-flow regularization are investigated in this context. The algorithm is tested on both synthetic and real image sequences and the results are compared to those of other local methods. Finally, we show that other motion features, i.e. motion direction, can be computed using the same algorithmic framework without requiring an intermediate representation of local velocity, which is an important characteristic of the proposed method.Postprint (author’s final draft

Monitoring Changes in Hemodynamics Following Optogenetic Stimulation

The brain is composed of billions of neurons, all of which connected through a vast network. After years of study and applications of different technologies and techniques, there are still more questions than answers when it comes to the fundamental functions of the brain. This project aims to provide a new tool which can be used to gain a better understanding of the fundamental mechanisms that govern neurological processes inside the brain. In order for neural networks to operate, blood has to be supplied through neighboring blood vessels. As such, the increase or decrease in the blood supply has been used as an indicator of neural activity. The neural activity and blood supply relationship is known as neural vasculature coupling. Monitoring the hemodynamics is used as an indicator of neurological activity, but the causal relationship is an area of current research. Gaining a better understanding of the coupling of neural activity and the surrounding vasculature provides a more accurate methodology to evaluate regional neural activity. The new optical technology applied in this project provides a set of tools to both stimulate and monitor this coupling relationship. Optogenetics provides the capability of stimulating neural activity using specific wavelengths of light. Essentially this tool allows for the direct stimulation of networks of neurons by simply shining one color of light onto the brain. Optical Coherence Tomography (OCT), another new optical technology applied in this project, can record volumetric images of blood vessels and flow using only infrared light. The combination of the two optical technologies is then capable of stimulating neural activity and monitoring the hemodynamic response inside the brain using only light. As a result of this project we have successfully demonstrated the capability of both stimulating and imaging the brain using new optical technologies. The optical stimulation of neural activity has evoked a direct hemodynamic effect as anticipated through neural-vasculature coupling. Changes in blood velocity, flow and dilatation were all recorded using the high resolution and high speed capabilities of the OCT system

In-situ measurement and characterization of cloud particles using digital in-line holography

Satellite measurement validations, climate models, atmospheric radiative transfer models and cloud models, all depend on accurate measurements of cloud particle size distributions, number densities, spatial distributions, and other parameters relevant to cloud microphysical processes. And many airborne instruments designed to measure size distributions and concentrations of cloud particles have large uncertainties in measuring number densities and size distributions of small ice crystals. HOLODEC (Holographic Detector for Clouds) is a new instrument that does not have many of these uncertainties and makes possible measurements that other probes have never made. The advantages of HOLODEC are inherent to the holographic method. In this dissertation, I describe HOLODEC, its in-situ measurements of cloud particles, and the results of its test flights. I present a hologram reconstruction algorithm that has a sample spacing that does not vary with reconstruction distance. This reconstruction algorithm accurately reconstructs the field to all distances inside a typical holographic measurement volume as proven by comparison with analytical solutions to the Huygens-Fresnel diffraction integral. It is fast to compute, and has diffraction limited resolution. Further, described herein is an algorithm that can find the position along the optical axis of small particles as well as large complex-shaped particles. I explain an implementation of these algorithms that is an efficient, robust, automated program that allows us to process holograms on a computer cluster in a reasonable time. I show size distributions and number densities of cloud particles, and show that they are within the uncertainty of independent measurements made with another measurement method. The feasibility of another cloud particle instrument that has advantages over new standard instruments is proven. These advantages include a unique ability to detect shattered particles using three-dimensional positions, and a sample volume size that does not vary with particle size or airspeed. It also is able to yield two-dimensional particle profiles using the same measurements

Novel linear and nonlinear optical signal processing for ultra-high bandwidth communications

The thesis is articulated around the theme of ultra-wide bandwidth single channel signals. It focuses on the two main topics of transmission and processing of information by techniques compatible with high baudrates. The processing schemes introduced combine new linear and nonlinear optical platforms such as Fourier-domain programmable optical processors and chalcogenide chip waveguides, as well as the concept of neural network. Transmission of data is considered in the context of medium distance links of Optical Time Division Multiplexed (OTDM) data subject to environmental fluctuations. We experimentally demonstrate simultaneous compensation of differential group delay and multiple orders of dispersion at symbol rates of 640 Gbaud and 1.28 Tbaud. Signal processing at high bandwidth is envisaged both in the case of elementary post-transmission analog error mitigation and in the broader field of optical computing for high level operations (“optical processor”). A key innovation is the introduction of a novel four-wave mixing scheme implementing a dot-product operation between wavelength multiplexed channels. In particular, it is demonstrated for low-latency hash-key based all-optical error detection in links encoded with advanced modulation formats. Finally, the work presents groundbreaking concepts for compact implementation of an optical neural network as a programmable multi-purpose processor. The experimental architecture can implement neural networks with several nodes on a single optical nonlinear transfer function implementing functions such as analog-to-digital conversion. The particularity of the thesis is the new approaches to optical signal processing that potentially enable high level operations using simple optical hardware and limited cascading of components

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning