Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Computational geometry

コンピュータ・サイエンス : ACM computing surveys

.............................................................................................................................................................. 6 1 Introduction ............................................................................................................................................. 7 1.1 Motivation .......................................................................................................................................................... 7 1.2 Previous Surveys ............................................................................................................................................. 8 1.3 Overview of Technologies ........................................................................................................................... 9 1.4 Indoor Positioning Applications ............................................................................................................ 11 1.5 Structure of this Work ............................................................................................................................... 14 2 User Requirements ............................................................................................................................. 15 2.1 Requirements Parameters Overview .................................................................................................. 15 2.2 Positioning Requirements Parameters Definition ......................................................................... 17 2.3 Man Machine Interface Requirements ................................................................................................ 19 2.4 Security and Privacy Requirements ..................................................................................................... 20 2.5 Costs .................................................................................................................................................................. 20 2.6 Generic Derivation of User Requirements......................................................................................... 20 2.7 Requirements for Selected Indoor Applications ............................................................................. 21 3 Definition of Terms ............................................................................................................................. 25 3.1 Disambiguation of Terms for Positioning .......................................................................................... 25 3.2 Definition of Technical Terms ................................................................................................................ 27 3.3 The Basic Measuring Principles ............................................................................................................. 29 3.4 Positioning Methods ................................................................................................................................... 31 4 Cameras .................................................................................................................................................. 34 4.1 Reference from 3D Building Models .................................................................................................... 35 4.2 Reference from Images .............................................................................................................................. 36 4.3 Reference from Deployed Coded Targets .......................................................................................... 37 4.4 Reference from Projected Targets ........................................................................................................ 38 4.5 Systems without Reference ..................................................................................................................... 39 4.6 Reference from Other Sensors ............................................................................................................... 40 4.7 Summary on Camera Based Indoor Positioning Systems ........................................................... 40 5 Infrared ................................................................................................................................................... 42 5.1 Active Beacons .............................................................................................................................................. 42 5.2 Imaging of Natural Infrared Radiation ............................................................................................... 43 5.3 Imaging of Artificial Infrared Light ....................................................................................................... 43 5.4 Summary on Infrared Indoor Positioning Systems ....................................................................... 44

Indoor Positioning Technologies

The Role of Additive Manufacturing in the Era of Industry 4.0

An apparatus and method for creating a 3D model of a scene. A plurality of light beams are projected onto the scene and their imaged projections are analyzed to capture surface information of the scene at a plurality of dots where the light beams intersect the scene. Surface information is used to register consecutive video frames and to construct a 3D model of the scene.

System and method for three dimensional modeling

Computational kinematics

The authors discuss the development of intelligent techniques appropriate for the automatic preparation, execution, and control of numerical experiments.

/pdf/intelligence-in-scientific-computing-2dngsvii1v.pdf

Intelligence in scientific computing

We present an algorithm for functional kinematic tolerance analysis of general planar mechanical systems with parametric tolerances. The algorithm performs worst-case analysis of systems of curved parts with contact changes, including open and closed kinematic chains. It computes quantitative variations and helps designers detect qualitative variations, such as blocking and under-cutting. The algorithm constructs a variation model for each interacting pair of parts: a mapping from the part tolerances and configurations to the kinematic variation of the pair. These models generalize the configuration space representation of nominal kinematics to toleranced parts. They are composed via sensitivity analysis and linear programming to derive the system variation at a given configuration. The variation relative to the nominal system function is computed by sampling the system variation. We demonstrate the algorithm on detailed parametric models of a movie camera film advance and of a micro-mechanical gear discriminator.

/pdf/parametric-kinematic-tolerance-analysis-of-general-planar-jcll2dl996.pdf

Elisha Sacks

Papers

System and method for three dimensional modeling

Computational kinematics

Intelligence in scientific computing

Intelligence in scientific computing

Parametric kinematic tolerance analysis of general planar systems