647 research outputs found

    Analog Photonics Computing for Information Processing, Inference and Optimisation

    Full text link
    This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

    Autonomous Space Surveillance for Arbitrary Domains

    Get PDF
    Space is becoming increasingly congested every day and the task of accurately tracking satellites is paramount for the continued safe operation of both manned and unmanned space missions. In addition to new spacecraft launches, satellite break-up events and collisions generate large amounts of orbital debris dramatically increasing the number of orbiting objects with each such event. In order to prevent collisions and protect both life and property in orbit, accurate knowledge of the position of orbiting objects is necessary. Space Domain Awareness (SDA) used interchangeably with Space Situational Awareness (SSA), are the names given to the daunting task of tracking all orbiting objects. In addition to myriad objects in low-earth-orbit (LEO) up to Geostationary (GEO) orbit, there are a growing number of spacecraft in cislunar space expanding the task of cataloguing and tracking space objects to include the whole of the earth-moon system. This research proposes a series of algorithms to be used in autonomous SSA for earth-orbiting and cislunar objects. The algorithms are autonomous in the sense that once a set of raw measurements (images in this case) are input to the algorithms, no human in the loop input is required to produce an orbit estimate. There are two main components to this research, an image processing and satellite detection component, and a dynamics modeling component for three-body relative motion. For the image processing component, resident space objects, (commonly referred to as RSOs) which are satellites or orbiting debris are identified in optical images. Two methods of identifying RSOs in a set of images are presented. The first method autonomously builds a template image to match a constellation of satellites and proceeds to match RSOs across a set of images. The second method utilizes optical flow to use the image velocities of objects to differentiate between stars and RSOs. Once RSOs have been detected, measurements are generated from the detected RSO locations to estimate the orbit of the observed object. The orbit determination component includes multiple methods capable of handling both earth-orbiting and cislunar observations. The methods used include batch-least squares and unscented Kalman filtering for earth-orbiting objects. For cislunar objects, a novel application of a particle swarm optimizer (PSO) is used to estimate the observed satellite orbit. The PSO algorithm ingests a set of measurements and attempts to match a set of virtual particle measurements to the truth measurements. The PSO orbit determination method is tested using both MATLAB and Python implementations. The second main component of this research develops a novel linear dynamics model of relative motion for satellites in cislunar space. A set of novel linear relative equations of motion are developed with a semi-analytical matrix exponential method. The motion models are tested on various cislunar orbit geometries for both the elliptical restricted three-body problem (ER3BP) and the circular restricted three-body problem (CR3BP) through MATLAB simulations. The linear solution method\u27s accuracy is compared to the non-linear equations of relative motion and are seen to hold to meter level accuracy for deputy position for a variety of orbits and time-spans. Two applications of the linearized motion models are then developed. The first application defines a differential corrector to compute closed relative motion trajectories in a relative three-body frame. The second application uses the exponential matrix solution for the linearized equations of relative motion to develop a method of initial relative orbit determination (IROD) for the CR3BP

    A Hybrid Data-driven Model of Ship Roll

    Full text link
    A hybrid data-driven method, which combines low-fidelity physics with machine learning (ML) to model nonlinear forces and moments at a reduced computational cost, is applied to predict the roll motions of an appended ONR Tumblehome (ONRT) hull in waves. The method is trained using CFD data of unforced roll decay time series--a common data set used in parameter identification for ship roll damping and restoring moments. The trained model is then used to predict wave excited roll responses in a range of wave frequencies and the results are compared to CFD validation data. The predictions show that the method improves predictions of roll responses, especially near the natural frequency

    2023- The Twenty-seventh Annual Symposium of Student Scholars

    Get PDF
    The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp

    2008 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Second Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1002/thumbnail.jp

    Molecular Dynamics

    Full text link
    While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. We know that many proteins have functional motions, and in Chapter "Structure Determination" we already introduced the famous example of the allosteric cooperative binding of oxygen to the haem group in hemoglobin. However, experimentally, such motions are hard to observe. Here, we will introduce MD simulations to investigate the dynamic behaviour of proteins. In a simulation the forces and interactions between particles are used to numerically derive the resulting three-dimensional movement of these particles over a certain time-scale. We will also highlight some applications, and will see how simulation results may be interpreted.Comment: editorial responsability: Halima Mouhib, Sanne Abeln, K. Anton Feenstra. This chapter is part of the book "Introduction to Protein Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to all the (published) chapters. The update adds available arxiv hyperlinks for the chapter

    2022 Review of Data-Driven Plasma Science

    Get PDF
    Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

    Applications

    Get PDF
    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
    • …
    corecore