High Dimensional Modulation and MIMO Techniques for Access Networks

Exploration of advanced modulation formats and multiplexing techniques for next generation optical access networks are of interest as promising solutions for delivering multiple services to end-users. This thesis addresses this from two different angles: high dimensionality carrierless amplitudephase (CAP) and multiple-input multiple-output (MIMO) radio-over-fiber (RoF) systems. High dimensionality CAP modulation has been investigated in optical fiber systems. In this project we conducted the first experimental demonstration of 3 and 4 dimensional CAP with bit rates up to 10 Gb/s. These results indicate the potentiality of supporting multiple users with converged services. At the same time, orthogonal division multiple access (ODMA) systems for multiple possible dimensions of CAP modulation has been demonstrated for user and service allocation in wavelength division multiplexing (WDM) optical access network. 2 x 2 MIMO RoF employing orthogonal frequency division multiplexing (OFDM) with 5.6 GHz RoF signaling over all-vertical cavity surface emitting lasers (VCSEL) WDM passive optical networks (PONs). We have employed polarization division multiplexing (PDM) to further increase the capacity per wavelength of the femto-cell network. Bit rate up to 1.59 Gbps with fiber-wireless transmission over 1 m air distance is demonstrated. The results presented in this thesis demonstrate the feasibility of high dimensionality CAP in increasing the number of dimensions and their potentially to be utilized for multiple service allocation to different users. MIMO multiplexing techniques with OFDM provides the scalability in increasing spectral effciency and bit rates for RoF systems. High dimensional CAP and MIMO multiplexing techniques are two promising solutions for supporting wired and hybrid wired-wireless access networks

SenseBack - An implantable system for bidirectional neural interfacing

Chronic in-vivo neurophysiology experiments require highly miniaturized, remotely powered multi-channel neural interfaces which are currently lacking in power or flexibility post implantation. In this article, to resolve this problem we present the SenseBack system, a post-implantation reprogrammable wireless 32-channel bidirectional neural interfacing that can enable chronic peripheral electrophysiology experiments in freely behaving small animals. The large number of channels for a peripheral neural interface, coupled with fully implantable hardware and complete software flexibility enable complex in-vivo studies where the system can adapt to evolving study needs as they arise. In complementary ex-vivo and in-vivo preparations, we demonstrate that this system can record neural signals and perform high-voltage, bipolar stimulation on any channel. In addition, we demonstrate transcutaneous power delivery and Bluetooth 5 data communication with a PC. The SenseBack system is capable of stimulation on any channel with ±20 V of compliance and up to 315 μA of current, and highly configurable recording with per-channel adjustable gain and filtering with 8 sets of 10-bit ADCs to sample data at 20 kHz for each channel. To the best of our knowledge this is the first such implantable research platform offering this level of performance and flexibility post-implantation (including complete reprogramming even after encapsulation) for small animal electrophysiology. Here we present initial acute trials, demonstrations and progress towards a system that we expect to enable a wide range of electrophysiology experiments in freely behaving animals

Technical implementations of light sheet microscopy

Fluorescence-based microscopy is among the most successful methods in biological studies. It played a critical role in the visualization of subcellular structures and in the analysis of complex cellular processes, and it is nowadays commonly employed in genetic and drug screenings. Among the fluorescence-based microscopy techniques, light sheet fluorescence microscopy (LSFM) has shown a quite interesting set of benefits. The technique combines the speed of epi-fluorescence acquisition with the optical sectioning capability typical of confocal microscopes. Its unique configuration allows the excitation of only a thin plane of the sample, thus fast, high resolution imaging deep inside tissues is nowadays achievable. The low peak intensity with which the sample is illuminated diminishes phototoxic effects and decreases photobleaching of fluorophores, ensuring data collection for days with minimal adverse consequences on the sample. It is no surprise that LSFM applications have raised in just few years and the technique has been applied to study a wide variety of samples, from whole organism, to tissues, to cell clusters, and single cells. As a consequence, in recent years numerous set-ups have been developed, each one optimized for the type of sample in use and the requirements of the question at hand. Hereby, we aim to review the most advanced LSFM implementations to assist new LSFM users in the choice of the LSFM set-up that suits their needs best. We also focus on new commercial microscopes and do-it-yourself strategies; likewise we review recent designs that allow a swift integration of LSFM on existing microscopes

Light-sheet microscopy: a tutorial

This paper is intended to give a comprehensive review of light-sheet (LS) microscopy from an optics perspective. As such, emphasis is placed on the advantages that LS microscope configurations present, given the degree of freedom gained by uncoupling the excitation and detection arms. The new imaging properties are first highlighted in terms of optical parameters and how these have enabled several biomedical applications. Then, the basics are presented for understanding how a LS microscope works. This is followed by a presentation of a tutorial for LS microscope designs, each working at different resolutions and for different applications. Then, based on a numerical Fourier analysis and given the multiple possibilities for generating the LS in the microscope (using Gaussian, Bessel, and Airy beams in the linear and nonlinear regimes), a systematic comparison of their optical performance is presented. Finally, based on advances in optics and photonics, the novel optical implementations possible in a LS microscope are highlighted.Peer ReviewedPostprint (published version

Accurate Segmentation of Cerebrovasculature from TOF-MRA Images Using Appearance Descriptors

© 2013 IEEE. Analyzing cerebrovascular changes can significantly lead to not only detecting the presence of serious diseases e.g., hypertension and dementia, but also tracking their progress. Such analysis could be better performed using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images, but this requires accurate segmentation of the cerebral vasculature from the surroundings. To achieve this goal, we propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that have the ability to capture the appearance of macro and micro-vessels in ToF-MRA. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets. The appearance of the cerebral vasculature is also represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) that its parameters are estimated by using a modified Expectation-Maximization (EM) algorithm. The extracted appearance features are separable and can be classified by any classifier, as demonstrated by our segmentation results. To validate the accuracy of our algorithm, we tested the proposed approach on in-vivo data using 270 data sets, which were qualitatively validated by a neuroradiology expert. The results were quantitatively validated using the three commonly used metrics for segmentation evaluation: the Dice coefficient, the modified Hausdorff distance, and the absolute volume difference. The proposed approach showed a higher accuracy compared to two of the existing segmentation approaches

Event-Driven Technologies for Reactive Motion Planning: Neuromorphic Stereo Vision and Robot Path Planning and Their Application on Parallel Hardware

Die Robotik wird immer mehr zu einem Schlüsselfaktor des technischen Aufschwungs. Trotz beeindruckender Fortschritte in den letzten Jahrzehnten, übertreffen Gehirne von Säugetieren in den Bereichen Sehen und Bewegungsplanung noch immer selbst die leistungsfähigsten Maschinen. Industrieroboter sind sehr schnell und präzise, aber ihre Planungsalgorithmen sind in hochdynamischen Umgebungen, wie sie für die Mensch-Roboter-Kollaboration (MRK) erforderlich sind, nicht leistungsfähig genug. Ohne schnelle und adaptive Bewegungsplanung kann sichere MRK nicht garantiert werden. Neuromorphe Technologien, einschließlich visueller Sensoren und Hardware-Chips, arbeiten asynchron und verarbeiten so raum-zeitliche Informationen sehr effizient. Insbesondere ereignisbasierte visuelle Sensoren sind konventionellen, synchronen Kameras bei vielen Anwendungen bereits überlegen. Daher haben ereignisbasierte Methoden ein großes Potenzial, schnellere und energieeffizientere Algorithmen zur Bewegungssteuerung in der MRK zu ermöglichen. In dieser Arbeit wird ein Ansatz zur flexiblen reaktiven Bewegungssteuerung eines Roboterarms vorgestellt. Dabei wird die Exterozeption durch ereignisbasiertes Stereosehen erreicht und die Pfadplanung ist in einer neuronalen Repräsentation des Konfigurationsraums implementiert. Die Multiview-3D-Rekonstruktion wird durch eine qualitative Analyse in Simulation evaluiert und auf ein Stereo-System ereignisbasierter Kameras übertragen. Zur Evaluierung der reaktiven kollisionsfreien Online-Planung wird ein Demonstrator mit einem industriellen Roboter genutzt. Dieser wird auch für eine vergleichende Studie zu sample-basierten Planern verwendet. Ergänzt wird dies durch einen Benchmark von parallelen Hardwarelösungen wozu als Testszenario Bahnplanung in der Robotik gewählt wurde. Die Ergebnisse zeigen, dass die vorgeschlagenen neuronalen Lösungen einen effektiven Weg zur Realisierung einer Robotersteuerung für dynamische Szenarien darstellen. Diese Arbeit schafft eine Grundlage für neuronale Lösungen bei adaptiven Fertigungsprozesse, auch in Zusammenarbeit mit dem Menschen, ohne Einbußen bei Geschwindigkeit und Sicherheit. Damit ebnet sie den Weg für die Integration von dem Gehirn nachempfundener Hardware und Algorithmen in die Industrierobotik und MRK

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure