Novel Specialty Optical Fibers and Applications

Novel Specialty Optical Fibers and Applications focuses on the latest developments in specialty fiber technology and its applications. The aim of this reprint is to provide an overview of specialty optical fibers in terms of their technological developments and applications. Contributions include:1. Specialty fibers composed of special materials for new functionalities and applications in new spectral windows.2. Hollow-core fiber-based applications.3. Functionalized fibers.4. Structurally engineered fibers.5. Specialty fibers for distributed fiber sensors.6. Specialty fibers for communications

Discrete frequency chemical imaging with stimulated Raman scattering microscopy

Chemical imaging, the process of using chemically-specific label-free light-matter interactions as a contrast mechanism for imaging or microscopy, is a powerful set of tools for performing investigations where the distribution of chemical constituents within a specimen is of importance. This can include the locations of distinct cell types within a tissue biopsy, the distribution of oriented molecules within a polymer sample, or the concentration of a dissolved analyte in a fluidic system. Coherent Raman scattering (CRS) spectroscopies have gained increasing attention in recent years, as they represent a class of techniques which affords high-resolution, z-stack capable, not-perturbative, rapid chemical imaging. Stimulated Raman scattering (SRS) microscopy is particularly attractive because a linear response to analyte concentration allows for quantitative investigation. Unlike more traditional vibrational spectroscopic techniques such as Fourier-transform infrared (FT-IR) absorption and spontaneous Raman scattering, CRS instruments are often operated in a single-frequency or limited bandwidth fashion and investigate only one small piece of the specimen’s vibrational spectrum at any given time. This difference has implications for experimental design, imaging protocols, and subsequent data analysis. Nevertheless, the increasing interest in and apparent utility of these tools is driving many implementations of chemical imaging towards this ‘discrete-frequency’ approach. Here, we describe the construction and deployment of an SRS microscope, followed by the evaluation of this technology as a tool for the label-free classification of tissue biopsies. Additionally, we explore applications which are better-suited to the specific strengths of this imaging modality, namely those which benefit from 3D volumetric imaging or the investigation of aqueous systems, both of which are not achievable with most implementations of infrared absorption measurements

Silicon Carbide And Agile Optics Based Sensors For Power Plant Gas Turbines, Laser Beam Analysis And Biomedicine

Proposed are novel sensors for extreme environment power plants, laser beam analysis and biomedicine. A hybrid wireless-wired extreme environment temperature sensor using a thick single-crystal Silicon Carbide (SiC) chip embedded inside a sintered SiC probe design is investigated and experimentally demonstrated. The sensor probe employs the SiC chip as a Fabry-Perot (FP) interferometer to measure the change in refractive index and thickness of SiC with temperature. A novel temperature sensing method that combines wavelength-tuned signal processing for coarse measurements and classical FP etalon peak shift for fine measurements is proposed and demonstrated. This method gives direct unambiguous temperature measurements with a high temperature resolution over a wide temperature range. An alternative method using blackbody radiation from a SiC chip in a two-color pyrometer configuration for coarse temperature measurement and classical FP laser interferometry via the same chip for fine temperature measurement is also proposed and demonstrated. The sensor design is successfully deployed in an industrial test rig environment with gas temperatures exceeding 1200 C. This sensor is proposed as an alternate to all-electrical thermocouples that are susceptible to severe reliability and lifetime issues in such extreme environments. A few components non-contact thickness measurement system for optical quality semi-transparent samples such as Silicon (Si) and 6H SiC optical chips such as the one used in the design of this sensor is proposed and demonstrated. The proposed system is self-calibrating and ensures a true thickness measurement by taking into account material dispersion in the wavelength band of operation. For the first time, a 100% repeatable all-digital electronically-controlled pinhole laser beam profiling system using a Texas Instruments (TI) Digital Micro-mirror Device (DMD) commonly used in projectors is experimentally demonstrated using a unique liquid crystal image generation system with non-invasive qualities. Also proposed and demonstrated is the first motion-free electronically-controlled beam propagation analyzer system using a TI DMD and a variable focus liquid lens. The system can be used to find all the parameters of a laser beam including minimum waist size, minimum waist location and the beam propagation parameter M2. Given the all-digital nature of DMD-based profiling and all-analog motion-free nature of the Electronically Controlled Variable Focus Lens (ECVFL) beam focus control, the proposed analyzer versus prior-art promises better repeatability, speed and reliability. For the first time, Three Dimensional (3-D) imaging is demonstrated using an electronically controlled Liquid Crystal (LC) optical lens to accomplish a no-moving parts depth section scanning in a modified commercial 3-D confocal microscope. The proposed microscopy system within aberration limits has the potential to eliminate the sample or objective motion-caused mechanical forces that can distort the original sample structure and lead to imaging errors. A signal processing method for realizing high resolution three dimensional (3-D) optical imaging using diffraction limited low resolution optical signals is also proposed

A New Representation for Spectral Data Applied to Raman Spectroscopy of Brain Cancer

Par sa nature infiltrative et son confinement derrière la barrière hémo-encéphalique, le cancer primaire du cerveau est l’une des néoplasies les plus difficiles à diagnostiquer et traiter. Son traitement repose sur la résection chirurgicale maximale. La spectroscopie Raman, capable d’identifier en temps réel des régions cancéreuses qui apparaîtraient normales à l’œil nu, promet d’améliorer considérablement le guidage neurochirurgical et maximiser la résection de la masse tumorale. Cependant, le signal Raman est très complexe à interpréter : les systèmes Raman peuvent maintenant capter des signaux de grande qualité que les méthodes analytiques actuelles ne parviennent pas à interpréter de manière reproductible. Ceci constitue une barrière importante à l’acceptation de la spectroscopie Raman par les médecins et les chercheurs œuvrant sur le cancer du cerveau. L’objectif de ce travail est de développer une méthode robuste d’ingénierie des variables (« Feature engineering ») qui permettrait d’identifier les processus moléculaires exploités par les systèmes Raman pour différentier les régions cancéreuses des régions saines lors de chirurgies cérébrales. Tout d’abord, nous avons identifié les régions Raman ayant une haute spécificité à notre problématique clinique par une revue systématique de la littérature. Un algorithme d’ajustement de courbe a été développé afin d’extraire la forme des pics Raman dans les régions sélectionnées. Puis, nous avons élaboré un modèle mathématique qui tient compte de l’interactivité entre les molécules de l’échantillon interrogé, ainsi qu’entre le signal Raman et l’âge du patient opéré. Pour valider le modèle, nous avons comparé sa capacité à compresser le signal avec celle de l’analyse en composante principale (ACP), le standard en spectroscopie Raman. Finalement, nous avons appliqué la méthode d’ingénierie des variables à des spectres Raman acquis en salle d’opération afin d’identifier quels processus moléculaires indiquaient la présence de cancer. Notre méthode a démontré une meilleure rétention d’information que l’ACP. En l’appliquant aux spectres Raman in vivo, les zones denses en cellules malignes démontrent une expression augmentée d’acides nucléiques ainsi que de certaines protéines, notamment le collagène, le tryptophan et la phénylalanine. De plus, l’âge des patients semble affecter l’impact qu’ont certaines protéines, lipides et acides nucléiques sur le spectre Raman. Nos travaux révèlent l’importance d’une modélisation statistique appropriée pour l’implémentation clinique de systèmes Raman chirurgicaux.----------ABSTRACT Because of its infiltrative nature and concealment behind the blood-brain barrier, primary brain cancer remains one of the most challenging oncological condition to diagnose and treat. The mainstay of treatment is maximal surgical resection. Raman spectroscopy has shown great promise to guide surgeons intraoperatively by identifying, in real-time, dense cancer regions that appear normal to the naked eye. The Raman signal of living tissue is, however, very challenging to interpret, and while most advances in Raman systems targeted the hardware, appropriate statistical modeling techniques are lacking. As a result, there is conflicting evidence as to which molecular processes are captured by Raman probes. This limitation hinders clinical translation and usage of the technology by the cancer-research community. This work focuses on the analytical aspect of Raman-based surgical systems. Its objective is to develop a robust data processing pipeline to confidently identify which molecular phenomena allow Raman systems to differentiate healthy brain and cancer during neurosurgeries. We first selected high-yield Raman regions based on previous literature on the subject, resulting in a list of reproducible Raman bands with high likelihood of brain-specific Raman signal. We then developed a peak-fitting algorithm to extract the shape (height and width) of the Raman signal at those specific bands. We described a mathematical model that accounted for all possible interactions between the selected Raman peaks, and the interaction between the peaks’ shape and the patient’s age. To validate the model, we compared its capacity to compress the signal while maintaining high information content against a Principal Component Analysis (PCA) of the Raman spectra, the fields’ standard. As a final step, we applied the feature engineering model to a dataset of intraoperative human Raman spectra to identify which molecular processes were indicative of brain cancer. Our method showed better information retention than PCA. Our analysis of in vivo Raman measurement showed that areas with high-density of malignant cells had increased expression of nucleic acids and protein compounds, notably collagen, tryptophan and phenylalanine. Patient age seemed to affect the impact of nucleic acids, proteins and lipids on the Raman spectra. Our work demonstrates the importance of appropriate statistical modeling in the implementation of Raman-based surgical devices

Terahertz Technology and Its Applications

The Terahertz frequency range (0.1 – 10)THz has demonstrated to provide many opportunities in prominent research fields such as high-speed communications, biomedicine, sensing, and imaging. This spectral range, lying between electronics and photonics, has been historically known as “terahertz gap” because of the lack of experimental as well as fabrication technologies. However, many efforts are now being carried out worldwide in order improve technology working at this frequency range. This book represents a mechanism to highlight some of the work being done within this range of the electromagnetic spectrum. The topics covered include non-destructive testing, teraherz imaging and sensing, among others

An electromagnetic imaging system for metallic object detection and classification

PhD ThesisElectromagnetic imaging currently plays a vital role in various disciplines, from engineering to medical applications and is based upon the characteristics of electromagnetic fields and their interaction with the properties of materials. The detection and characterisation of metallic objects which pose a threat to safety is of great interest in relation to public and homeland security worldwide. Inspections are conducted under the prerequisite that is divested of all metallic objects. These inspection conditions are problematic in terms of the disruption of the movement of people and produce a soft target for terrorist attack. Thus, there is a need for a new generation of detection systems and information technologies which can provide an enhanced characterisation and discrimination capabilities. This thesis proposes an automatic metallic object detection and classification system. Two related topics have been addressed: to design and implement a new metallic object detection system; and to develop an appropriate signal processing algorithm to classify the targeted signatures. The new detection system uses an array of sensors in conjunction with pulsed excitation. The contributions of this research can be summarised as follows: (1) investigating the possibility of using magneto-resistance sensors for metallic object detection; (2) evaluating the proposed system by generating a database consisting of 12 real handguns with more than 20 objects used in daily life; (3) extracted features from the system outcomes using four feature categories referring to the objects’ shape, material composition, time-frequency signal analysis and transient pulse response; and (4) applying two classification methods to classify the objects into threats and non-threats, giving a successful classification rate of more than 92% using the feature combination and classification framework of the new system. The study concludes that novel magnetic field imaging system and their signal outputs can be used to detect, identify and classify metallic objects. In comparison with conventional induction-based walk-through metal detectors, the magneto-resistance sensor array-based system shows great potential for object identification and discrimination. This novel system design and signal processing achievement may be able to produce significant improvements in automatic threat object detection and classification applications.Iraqi Cultural Attaché, Londo

Photodetectors

In this book some recent advances in development of photodetectors and photodetection systems for specific applications are included. In the first section of the book nine different types of photodetectors and their characteristics are presented. Next, some theoretical aspects and simulations are discussed. The last eight chapters are devoted to the development of photodetection systems for imaging, particle size analysis, transfers of time, measurement of vibrations, magnetic field, polarization of light, and particle energy. The book is addressed to students, engineers, and researchers working in the field of photonics and advanced technologies

Spectral image utility for target detection applications

In a wide range of applications, images convey useful information about scenes. The “utility” of an image is defined with reference to the specific task that an observer seeks to accomplish, and differs from the “fidelity” of the image, which seeks to capture the ability of the image to represent the true nature of the scene. In remote sensing of the earth, various means of characterizing the utility of satellite and airborne imagery have evolved over the years. Recent advances in the imaging modality of spectral imaging have enabled synoptic views of the earth at many finely sampled wavelengths over a broad spectral band. These advances challenge the ability of traditional earth observation image utility metrics to describe the rich information content of spectral images. Traditional approaches to image utility that are based on overhead panchromatic image interpretability by a human observer are not applicable to spectral imagery, which requires automated processing. This research establishes the context for spectral image utility by reviewing traditional approaches and current methods for describing spectral image utility. It proposes a new approach to assessing and predicting spectral image utility for the specific application of target detection. We develop a novel approach to assessing the utility of any spectral image using the target-implant method. This method is not limited by the requirements of traditional target detection performance assessment, which need ground truth and an adequate number of target pixels in the scene. The flexibility of this approach is demonstrated by assessing the utility of a wide range of real and simulated spectral imagery over a variety ii of target detection scenarios. The assessed image utility may be summarized to any desired level of specificity based on the image analysis requirements. We also present an approach to predicting spectral image utility that derives statistical parameters directly from an image and uses them to model target detection algorithm output. The image-derived predicted utility is directly comparable to the assessed utility and the accuracy of prediction is shown to improve with statistical models that capture the non-Gaussian behavior of real spectral image target detection algorithm outputs. The sensitivity of the proposed spectral image utility metric to various image chain parameters is examined in detail, revealing characteristics, requirements, and limitations that provide insight into the relative importance of parameters in the image utility. The results of these investigations lead to a better understanding of spectral image information vis-à-vis target detection performance that will hopefully prove useful to the spectral imagery analysis community and represent a step towards quantifying the ability of a spectral image to satisfy information exploitation requirements

Guiding deep brain stimulation neurosurgery with optical spectroscopy

Savoir différiencier les différentes types de tissus représente un aspect important lors d’interventions médicales, que ce soit pour aider au diagnostic d’une maladie ou pour le guidage chirurgical. Il est généralement très difficile de distinguer les tissus sains des tissus pathologiques à l’oeil nu et la navigation chirurgicale peut parfois être difficile dans les grands organes où la structure ciblé se trouve enfouie profondément. De nouvelles méthodes susceptibles d’accroître la réussite de telles interventions médicales suscitent actuellement de l’intérêt chez les professionnels de la santé. La spectroscopie optique, en analysant les interactions lumière-tissu dans une plage spectrale définie, est un outil permettant de différencier les tissus avec une résolution et une sensibilité bien supérieures à celles de l’oeil humain. Tout au long de cette thèse, je détaillerai comment la spectroscopie optique a été utilisée pour créer et améliorer un système de guidage optique utilisé pour la stimulation cérébrale profonde en neurochirurgie, en particulier pour le traitement de la maladie de Parkinson. Pour commencer, je montrerai comment les informations spectroscopiques peuvent fournir une rétroaction peropératoire en temps réel à un neurochirurgien, au cours de la phase d’implantation de la procédure, avec une sonde qui n’induit aucune invasion supplémentaire. Je présenterai l’investigation de deux modalités spectroscopiques différentes pour la discrimination tissulaire pour le guidage, soit la spectroscopie à réflectance diffuse et la spectroscopie de diffusion Raman anti-Stokes cohérente. Les avantages et les inconvénients des deux techniques, ainsi que leurs aptitude à la traduction prometteuse pour cette application seront abordés. Par la suite, je présenterai une nouvelle technique d’analyse de données pour extraire l’oxygénation des tissus à partir de spectres de réflectance diffus dans le but d’améliorer la précision de mesure en spectroscopie rétinienne et ultimement de porter un diagnostique. Bien que conçu pour la rétine, l’algorithme peut également être utilisé pour analyser les spectres acquis lors d’une neurochirurgie afin de fournir des informations à la fois discriminantes et diagnostiques. Finalement, je montrerai des preuves de diffusion anisotrope de la lumière dans les axones myélinisés de la moelle épinière et discuterai des conséquences que cela pourrait avoir sur les simulations actuelles de la propagation des photons dans le cerveau, qui feront partie intégrante d’un guidage optique efficace.Differentiating tissue types is an important aspect of guiding medical interventions whether it be for disease diagnosis or for surgical guidance. However, diseased and healthy tissues are often hard to discriminate by human vision alone and surgical navigation can be difficult to accomplish in large organs where the target structure lies deep within the body. New methods that can increase certainty in such medical interventions are therefore of great interest to healthcare professionals. Optical spectroscopy is a tool which can be exploited to probe discriminatory information in tissue by analyzing light-tissue interactions with a spectral range, resolution and sensitivity much greater than the human eye. Throughout this thesis, I will explain how I have leveraged optical spectroscopy to create, and improve, an optical guidance system for deep brain stimulation neurosurgery, specifically for the treatment of Parkinson’s disease. I will begin by describing how spectroscopic information can provide real-time feedback to a surgeon during the procedure, in the hopes of ultimately improving treatment outcome. To this end, I will present the investigation of two different spectroscopic modalities for optical guidance: diffuse reflectance spectroscopy, and coherent anti-Stokes Raman scattering spectroscopy. The advantages and disadvantages of both techniques will be discussed along with their promising translatability for this application. Following this, I will present a novel data analysis technique for extracting the tissue oxygenation from diffuse reflectance spectra with the aim of improved diagnostic information in retinal spectroscopy. While designed for the retina, the algorithm can also be used to analyze spectra acquired during a neurosurgery to provide both discriminatory and diagnostic information. Lastly, I will show evidence of anisotropic light scattering in the myelinated axons of the spinal cord and discuss the implications this may have on current photon propagation simulations in the brain, which will be integral for effective optical guidance

Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report

This report describes the 2014 study by the Science Definition Team (SDT) of the Wide-Field Infrared Survey Telescope (WFIRST) mission. It is a space observatory that will address the most compelling scientific problems in dark energy, exoplanets and general astrophysics using a 2.4-m telescope with a wide-field infrared instrument and an optical coronagraph. The Astro2010 Decadal Survey recommended a Wide Field Infrared Survey Telescope as its top priority for a new large space mission. As conceived by the decadal survey, WFIRST would carry out a dark energy science program, a microlensing program to determine the demographics of exoplanets, and a general observing program utilizing its ultra wide field. In October 2012, NASA chartered a Science Definition Team (SDT) to produce, in collaboration with the WFIRST Study Office at GSFC and the Program Office at JPL, a Design Reference Mission (DRM) for an implementation of WFIRST using one of the 2.4-m, Hubble-quality telescope assemblies recently made available to NASA. This DRM builds on the work of the earlier WFIRST SDT, reported by Green et al. (2012) and the previous WFIRST-2.4 DRM, reported by Spergel et. (2013). The 2.4-m primary mirror enables a mission with greater sensitivity and higher angular resolution than the 1.3-m and 1.1-m designs considered previously, increasing both the science return of the primary surveys and the capabilities of WFIRST as a Guest Observer facility. The addition of an on-axis coronagraphic instrument to the baseline design enables imaging and spectroscopic studies of planets around nearby stars.Comment: This report describes the 2014 study by the Science Definition Team of the Wide-Field Infrared Survey Telescope mission. 319 pages; corrected a misspelled name in the authors list and a typo in the abstrac