Implementation of adaptive optics into a femtosecond laser chain

Optical aberrations are the main cause for reduced focusability and low peak intensity in laser beams. All the applications that rely on high peak power and/or small focus spot size, loose efficiency when optical aberrations are present. They can be reduced by using specially shaped optical elements, but all aberrations cannot be avoided completely. In this thesis we used a new approach for reducing aberrations in a femtosecond laser beam. Usually, aberrations have to be corrected by using a deformable mirror with large diameter to avoid damage to the mirror. When the beam is expanded the peak intensity is reduced and the aberrations can be corrected. Deformable optics is normally not used for femtosecond lasers with kHz repetition rate and mJ pulse energy, because the price for implementation is often too high. We used a small deformable mirror, developed for applications in microscopy, with custom made ultrafast coating in order to correct aberrations in the femtosecond laser. Prior embedding the deformable mirror into the laser chain, we investigated its properties with a HeNe laser and an uncompressed femtosecond laser beam. The aberrations and focusability of the laser beam were measured with a Shack-Hartmann wavefront sensor and a CCD camera. Finally, the deformable mirror was integrated into the laser chain to reduce aberrations. We demonstrated the measurement and correction of optical aberrations in a femtosecond laser chain. The high-order aberrations introduced by the deformable mirror itself currently prevent from increasing the focused intensity. Nevertheless, the method we proposed is promising and should be further investigated.A Mirror into a Thousand Pieces Imagine if we could shape the light in any way we want with a mirror consisting of small mirrors. The ability to do so would improve and help develop laser-based applications, thus making the technology more widespread. Today we use lasers everywhere: medicine, industrial manufacturing, micro-drilling and fundamental research etc. These applications require high intensity that can be produced by femtosecond lasers. Such lasers have very short pulses and high intensity. Femtosecond lasers are complex and consist of many optical elements (lenses, mirrors etc.), which, due to their inherent imperfections, cause distortions in the laser beam. These distortions, also called aberrations, are unwanted because they reduce the the overall quality of the beam. With passive optical elements, such as specially shaped lenses etc., we are able to reduce some of the aberrations. However to improve the quality of an unstable and distorted laser beam, we will need something smarter. A solution for that is adaptive optics. One could think of an adaptive optics system as a human visual system. There is a sensor measuring the light (aberrations) - retina in the eye, a computer calculating the shifts to produce a sharp image - the brain, and a deforming element to change the incoming light accordingly - the eye lens with muscles. When the image is not sharp the shape of the lens will change until optimum image is produced on the retina. Usually an adaptive optics system consists of a deformable mirror, a wavefront sensor and a computer. Many small segments that can be moved up and down, thus creating a wanted shape for the mirror, make a deformable mirror. Upon reflection from this kind of mirror aberrations can be corrected and the overall beam quality improved. These adaptive optics systems are usually very expensive for high intensity lasers. Usually the deformable mirror must have a large diameter to avoid damage from a high intensity laser beam. However, in this study we investigated a much cheaper option. We used a deformable mirror with a small diameter and a special coating and placed it in a femtosecond laser to correct aberrations. We learned, that with this technique, it is possible to reduce aberrations in the femtosecond laser beam and although the resulting output was not ideal, it proved that the concept of using adaptive optics to correct high intensity femtosecond laser is solid

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Action recognition using single-pixel time-of-flight detection

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject's privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network

Action Recognition Using Single-Pixel Time-of-Flight Detection

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network

Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm

A refractive lens is one of the simplest, most cost-effective and easily available imaging elements. Given a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can only be obtained by changing either the location of the lens, object or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The imaging process consists of three steps. In the first step, point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions during the final step. The results of LRRA were compared with two well-known reconstruction methods, namely the Lucy-Richardson algorithm and non-linear reconstruction

Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm

A refractive lens is one of the simplest, most cost-effective and easily available imaging elements. Given a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can only be obtained by changing either the location of the lens, object or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The imaging process consists of three steps. In the first step, point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions during the final step. The results of LRRA were compared with two well-known reconstruction methods, namely the Lucy-Richardson algorithm and non-linear reconstruction

Nonlinear Reconstruction of Images from Patterns Generated by Deterministic or Random Optical Masks—Concepts and Review of Research

Indirect-imaging methods involve at least two steps, namely optical recording and computational reconstruction. The optical-recording process uses an optical modulator that transforms the light from the object into a typical intensity distribution. This distribution is numerically processed to reconstruct the object’s image corresponding to different spatial and spectral dimensions. There have been numerous optical-modulation functions and reconstruction methods developed in the past few years for different applications. In most cases, a compatible pair of the optical-modulation function and reconstruction method gives optimal performance. A new reconstruction method, termed nonlinear reconstruction (NLR), was developed in 2017 to reconstruct the object image in the case of optical-scattering modulators. Over the years, it has been revealed that the NLR can reconstruct an object’s image modulated by an axicons, bifocal lenses and even exotic spiral diffractive elements, which generate deterministic optical fields. Apparently, NLR seems to be a universal reconstruction method for indirect imaging. In this review, the performance of NLR isinvestigated for many deterministic and stochastic optical fields. Simulation and experimental results for different cases are presented and discussed