A CMOS-based Hartmann-Shack Sensor for Real-Time Adaptive Optical Applications

Adaptive optical systems have a growing field of applications in opthalmology. In every adaptive system there is the need for a sensor and an actuator. The Hartmann-Shack wavefront sensor uses the displacement of spots in the focal plane of a lenslet array for subsequent calculation of the wavefront. The bandwidth of current sensors is mostly limited by software processing of the focal plane image to some tens of Hz, which makes it unsuitable for real-time adaptive optical systems. To overcome the current bandwidth limitations a fast Hartmann-Shack sensor based on an application specific integrated circuit has been developed and tested, that reaches a bandwidth of up to 6 kHz. The sensor includes photodetectors with 40% quantum efficiency at 680 nm wavelength and an image processing, that is especially suitable to reduce the effects of the common mismatching of process parameters in CMOS-based sensors (Complementary Metal Oxide Semiconductor). A special problem in ophthalmic applications is the low available spot power below 1 nW.Adaptive optical systems have a growing field of applications in opthalmology. In every adaptive system there is the need for a sensor and an actuator. The Hartmann-Shack wavefront sensor uses the displacement of spots in the focal plane of a lenslet array for subsequent calculation of the wavefront. The bandwidth of current sensors is mostly limited by software processing of the focal plane image to some tens of Hz, which makes it unsuitable for real-time adaptive optical systems. To overcome the current bandwidth limitations a fast Hartmann-Shack sensor based on an application specific integrated circuit has been developed and tested, that reaches a bandwidth of up to 6 kHz. The sensor includes photodetectors with 40% quantum efficiency at 680 nm wavelength and an image processing, that is especially suitable to reduce the effects of the common mismatching of process parameters in CMOS-based sensors (Complementary Metal Oxide Semiconductor). A special problem in ophthalmic applications is the low available spot power below 1 nW. The developed Hartmann-Shack sensor allowed wavefront measurements with an accuracy of 0.16 dpt defocus at 160 pW spot power. It has been possible for the first time, to measure wavefront aberrations at the living humane eye with 300 Hz repetition rate and to calculate the power spectral density of these aberrations

Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. Large datasets with high-dimensional features complicate the implementation of visual architectures in memory constrained environments. This dissertation constructs online learning replacements for the components within a multi-stage architecture and demonstrates that the proposed replacements (namely fuzzy competitive clustering, an incremental covariance estimator, and multi-layer neural network) can offer performance competitive with their offline batch counterparts while providing a reduced memory footprint. The online nature of this solution allows for the development of a method for adjusting parameters within the architecture via stochastic gradient descent. Testing over multiple datasets shows the potential benefits of this methodology when appropriate priors on the initial parameters are unknown. Alternatives to batch based decompositions for a whitening preprocessing stage which take advantage of natural image statistics and allow simple dictionary learners to work well in the problem domain are also explored. Expansions of the architecture using additional pooling statistics and multiple layers are presented and indicate that larger codebook sizes are not the only step forward to higher classification accuracies. Experimental results from these expansions further indicate the important role of sparsity and appropriate encodings within multi-stage visual feature extraction architectures

CMOS optical centroid processor for an integrated Shack-Hartmann wavefront sensor

A Shack Hartmann wavefront sensor is used to detect the distortion of light in an optical wavefront. It does this by sampling the wavefront with an array of lenslets and measuring the displacement of focused spots from reference positions. These displacements are linearly related to the local wavefront tilts from which the entire wavefront can be reconstructed. In most Shack Hartmann wavefront sensors, a CCD is used to sample the entire wavefront, typically at a rate of 25 to 60 Hz, and a whole frame of light spots is read out before their positions are processed. This results in a data bottleneck. In this design, parallel processing is achieved by incorporating local centroid processing for each focused spot, thereby requiring only reduced bandwidth data to be transferred off-chip at a high rate. To incorporate centroid processing at the sensor level requires high levels of circuit integration not possible with a CCD technology. Instead a standard 0.7J..lmCMOS technology was used but photodetector structures for this technology are not well characterised. As such characterisation of several common photodiode structures was carried out which showed good responsitivity of the order of 0.3 AIW. Prior to fabrication on-chip, a hardware emulation system using a reprogrammable FPGA was built which implemented the centroiding algorithm successfully. Subsequently, the design was implemented as a single-chip CMOS solution. The fabricated optical centroid processor successfully computed and transmitted the centroids at a rate of more than 2.4 kHz, which when integrated as an array of tilt sensors will allow a data rate that is independent of the number of tilt sensors' employed. Besides removing the data bottleneck present in current systems, the design also offers advantages in terms of power consumption, system size and cost. The design was also shown to be extremely scalable to a complete low cost real time adaptive optics system

CMOS optical centroid processor for an integrated Shack-Hartmann wavefront sensor

A Shack Hartmann wavefront sensor is used to detect the distortion of light in an optical wavefront. It does this by sampling the wavefront with an array of lenslets and measuring the displacement of focused spots from reference positions. These displacements are linearly related to the local wavefront tilts from which the entire wavefront can be reconstructed. In most Shack Hartmann wavefront sensors, a CCD is used to sample the entire wavefront, typically at a rate of 25 to 60 Hz, and a whole frame of light spots is read out before their positions are processed. This results in a data bottleneck. In this design, parallel processing is achieved by incorporating local centroid processing for each focused spot, thereby requiring only reduced bandwidth data to be transferred off-chip at a high rate. To incorporate centroid processing at the sensor level requires high levels of circuit integration not possible with a CCD technology. Instead a standard 0.7J..lmCMOS technology was used but photodetector structures for this technology are not well characterised. As such characterisation of several common photodiode structures was carried out which showed good responsitivity of the order of 0.3 AIW. Prior to fabrication on-chip, a hardware emulation system using a reprogrammable FPGA was built which implemented the centroiding algorithm successfully. Subsequently, the design was implemented as a single-chip CMOS solution. The fabricated optical centroid processor successfully computed and transmitted the centroids at a rate of more than 2.4 kHz, which when integrated as an array of tilt sensors will allow a data rate that is independent of the number of tilt sensors' employed. Besides removing the data bottleneck present in current systems, the design also offers advantages in terms of power consumption, system size and cost. The design was also shown to be extremely scalable to a complete low cost real time adaptive optics system

Machine vision applications in UAVs for autonomous aerial refueling and runway detection

This research focuses on the application of Machine Vision (MV) techniques and algorithms to the problems of Autonomous Aerial Refueling (AAR) and Runway Detection. In particular, real laboratory based hardware was used in a simulated environment to emulate real-life conditions for AAR. It was shown that the K-Means Clustering Algorithm solution to the Marker Detection problem could be executed at a frame rate of 30 Hz and it averaged a tracking error of less than one pixel while utilizing only 0.16% of the image. It was also shown that the solution to the Runway Detection problem could be executed at a frame rate of 20 Hz which is acceptable for use in an UAV performing reconnaissance work. Data from these tests suggest that both software schemes are suitable for applications in moving vehicles and that the accuracy of the measurements produced by the schemes make them suitable for UAV applications

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions