The Kinematics of Multi-Fingered Manipulation
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Interaction of the Past of Parallel Universes
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CERN Document Server Interaction of the Past of parallel universes Alexander K. Guts Department of Mathematics, Omsk State University 644077 Omsk-77 RUSSIA E-mail: [email protected] October 26, 1999 ABSTRACT We constructed a model of five-dimensional Lorentz manifold with foliation of codimension 1 the leaves of which are four-dimensional space-times. The Past of these space-times can interact in macroscopic scale by means of large quantum fluctuations. Hence, it is possible that our Human History consists of ”somebody else’s” (alien) events. In this article the possibility of interaction of the Past (or Future) in macroscopic scales of space and time of two different universes is analysed. Each universe is considered as four-dimensional space-time V 4, moreover they are imbedded in five- dimensional Lorentz manifold V 5, which shall below name Hyperspace. The space- time V 4 is absolute world of events. Consequently, from formal standpoints any point-event of this manifold V 4, regardless of that we refer it to Past, Present or Future of some observer, is equally available to operate with her.Inotherwords, modern theory of space-time, rising to Minkowsky, postulates absolute eternity of the World of events in the sense that all events exist always. Hence, it is possible interaction of Present with Past and Future as well as Past can interact with Future. Question is only in that as this is realized. The numerous articles about time machine show that our statement on the interaction of Present with Past is not fantasy, but is subject of the scientific study. -
On the Configurations of Closed Kinematic Chains in Three
On the Configurations of Closed Kinematic Chains in three-dimensional Space Gerhard Zangerl Department of Mathematics, University of Innsbruck Technikestraße 13, 6020 Innsbruck, Austria E-mail: [email protected] Alexander Steinicke Department of Applied Mathematics and Information Technology, Montanuniversitaet Leoben Peter Tunner-Straße 25/I, 8700 Leoben, Austria E-mail: [email protected] Abstract A kinematic chain in three-dimensional Euclidean space consists of n links that are connected by spherical joints. Such a chain is said to be within a closed configuration when its link lengths form a closed polygonal chain in three dimensions. We investigate the space of configurations, described in terms of joint angles of its spherical joints, that satisfy the the loop closure constraint, meaning that the kinematic chain is closed. In special cases, we can find a new set of parameters that describe the diagonal lengths (the distance of the joints from the origin) of the configuration space by a simple domain, namely a cube of dimension n − 3. We expect that the new findings can be applied to various problems such as motion planning for closed kinematic chains or singularity analysis of their configuration spaces. To demonstrate the practical feasibility of the new method, we present numerical examples. 1 Introduction This study is the natural further development of [32] in which closed configurations of a two-dimensional kine- matic chain (KC) in terms of its joint angles were considered. As a generalization, we study the configuration spaces of a three-dimensional closed kinematic chain (CKC) with n links in terms of the joint angles of its spherical joints. -
Projective Geometry: a Short Introduction
Projective Geometry: A Short Introduction Lecture Notes Edmond Boyer Master MOSIG Introduction to Projective Geometry Contents 1 Introduction 2 1.1 Objective . .2 1.2 Historical Background . .3 1.3 Bibliography . .4 2 Projective Spaces 5 2.1 Definitions . .5 2.2 Properties . .8 2.3 The hyperplane at infinity . 12 3 The projective line 13 3.1 Introduction . 13 3.2 Projective transformation of P1 ................... 14 3.3 The cross-ratio . 14 4 The projective plane 17 4.1 Points and lines . 17 4.2 Line at infinity . 18 4.3 Homographies . 19 4.4 Conics . 20 4.5 Affine transformations . 22 4.6 Euclidean transformations . 22 4.7 Particular transformations . 24 4.8 Transformation hierarchy . 25 Grenoble Universities 1 Master MOSIG Introduction to Projective Geometry Chapter 1 Introduction 1.1 Objective The objective of this course is to give basic notions and intuitions on projective geometry. The interest of projective geometry arises in several visual comput- ing domains, in particular computer vision modelling and computer graphics. It provides a mathematical formalism to describe the geometry of cameras and the associated transformations, hence enabling the design of computational ap- proaches that manipulates 2D projections of 3D objects. In that respect, a fundamental aspect is the fact that objects at infinity can be represented and manipulated with projective geometry and this in contrast to the Euclidean geometry. This allows perspective deformations to be represented as projective transformations. Figure 1.1: Example of perspective deformation or 2D projective transforma- tion. Another argument is that Euclidean geometry is sometimes difficult to use in algorithms, with particular cases arising from non-generic situations (e.g. -
Arxiv:1910.10745V1 [Cond-Mat.Str-El] 23 Oct 2019 2.2 Symmetry-Protected Time Crystals
A Brief History of Time Crystals Vedika Khemania,b,∗, Roderich Moessnerc, S. L. Sondhid aDepartment of Physics, Harvard University, Cambridge, Massachusetts 02138, USA bDepartment of Physics, Stanford University, Stanford, California 94305, USA cMax-Planck-Institut f¨urPhysik komplexer Systeme, 01187 Dresden, Germany dDepartment of Physics, Princeton University, Princeton, New Jersey 08544, USA Abstract The idea of breaking time-translation symmetry has fascinated humanity at least since ancient proposals of the per- petuum mobile. Unlike the breaking of other symmetries, such as spatial translation in a crystal or spin rotation in a magnet, time translation symmetry breaking (TTSB) has been tantalisingly elusive. We review this history up to recent developments which have shown that discrete TTSB does takes place in periodically driven (Floquet) systems in the presence of many-body localization (MBL). Such Floquet time-crystals represent a new paradigm in quantum statistical mechanics — that of an intrinsically out-of-equilibrium many-body phase of matter with no equilibrium counterpart. We include a compendium of the necessary background on the statistical mechanics of phase structure in many- body systems, before specializing to a detailed discussion of the nature, and diagnostics, of TTSB. In particular, we provide precise definitions that formalize the notion of a time-crystal as a stable, macroscopic, conservative clock — explaining both the need for a many-body system in the infinite volume limit, and for a lack of net energy absorption or dissipation. Our discussion emphasizes that TTSB in a time-crystal is accompanied by the breaking of a spatial symmetry — so that time-crystals exhibit a novel form of spatiotemporal order. -
The Fractal Dimension of Product Sets
THE FRACTAL DIMENSION OF PRODUCT SETS A PREPRINT Clayton Moore Williams Brigham Young University [email protected] Machiel van Frankenhuijsen Utah Valley University [email protected] February 26, 2021 ABSTRACT Using methods from nonstandard analysis, we define a nonstandard Minkowski dimension which exists for all bounded sets and which has the property that dim(A × B) = dim(A) + dim(B). That is, our new dimension is “product-summable”. To illustrate our theorem we generalize an example of Falconer’s1 to show that the standard upper Minkowski dimension, as well as the Hausdorff di- mension, are not product-summable. We also include a method for creating sets of arbitrary rational dimension. Introduction There are several notions of dimension used in fractal geometry, which coincide for many sets but have important, distinct properties. Indeed, determining the most proper notion of dimension has been a major problem in geometric measure theory from its inception, and debate over what it means for a set to be fractal has often reduced to debate over the propernotion of dimension. A classical example is the “Devil’s Staircase” set, which is intuitively fractal but which has integer Hausdorff dimension2. As Falconer notes in The Geometry of Fractal Sets3, the Hausdorff dimension is “undoubtedly, the most widely investigated and most widely used” notion of dimension. It can, however, be difficult to compute or even bound (from below) for many sets. For this reason one might want to work with the Minkowski dimension, for which one can often obtain explicit formulas. Moreover, the Minkowski dimension is computed using finite covers and hence has a series of useful identities which can be used in analysis. -
Near-Death Experiences and the Theory of the Extraneuronal Hyperspace
Near-Death Experiences and the Theory of the Extraneuronal Hyperspace Linz Audain, J.D., Ph.D., M.D. George Washington University The Mandate Corporation, Washington, DC ABSTRACT: It is possible and desirable to supplement the traditional neu rological and metaphysical explanatory models of the near-death experience (NDE) with yet a third type of explanatory model that links the neurological and the metaphysical. I set forth the rudiments of this model, the Theory of the Extraneuronal Hyperspace, with six propositions. I then use this theory to explain three of the pressing issues within NDE scholarship: the veridicality, precognition and "fear-death experience" phenomena. Many scholars who write about near-death experiences (NDEs) are of the opinion that explanatory models of the NDE can be classified into one of two types (Blackmore, 1993; Moody, 1975). One type of explana tory model is the metaphysical or supernatural one. In that model, the events that occur within the NDE, such as the presence of a tunnel, are real events that occur beyond the confines of time and space. In a sec ond type of explanatory model, the traditional model, the events that occur within the NDE are not at all real. Those events are merely the product of neurobiochemical activity that can be explained within the confines of current neurological and psychological theory, for example, as hallucination. In this article, I supplement this dichotomous view of explanatory models of the NDE by proposing yet a third type of explanatory model: the Theory of the Extraneuronal Hyperspace. This theory represents a Linz Audain, J.D., Ph.D., M.D., is a Resident in Internal Medicine at George Washington University, and Chief Executive Officer of The Mandate Corporation. -
Arxiv:1902.02232V1 [Cond-Mat.Mtrl-Sci] 6 Feb 2019
Hyperspatial optimisation of structures Chris J. Pickard∗ Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom and Advanced Institute for Materials Research, Tohoku University 2-1-1 Katahira, Aoba, Sendai, 980-8577, Japan (Dated: February 7, 2019) Anticipating the low energy arrangements of atoms in space is an indispensable scientific task. Modern stochastic approaches to searching for these configurations depend on the optimisation of structures to nearby local minima in the energy landscape. In many cases these local minima are relatively high in energy, and inspection reveals that they are trapped, tangled, or otherwise frustrated in their descent to a lower energy configuration. Strategies have been developed which attempt to overcome these traps, such as classical and quantum annealing, basin/minima hopping, evolutionary algorithms and swarm based methods. Random structure search makes no attempt to avoid the local minima, and benefits from a broad and uncorrelated sampling of configuration space. It has been particularly successful in the first principles prediction of unexpected new phases of dense matter. Here it is demonstrated that by starting the structural optimisations in a higher dimensional space, or hyperspace, many of the traps can be avoided, and that the probability of reaching low energy configurations is much enhanced. Excursions into the extra dimensions are progressively eliminated through the application of a growing energetic penalty. This approach is tested on hard cases for random search { clusters, compounds, and covalently bonded networks. The improvements observed are most dramatic for the most difficult ones. Random structure search is shown to be typically accelerated by two orders of magnitude, and more for particularly challenging systems. -
Department of Mechanical Engineering ME 8492 – Kinematics of Machinery Unit I – Introduction to Mechanism - MCQ Bank 1
ChettinadTech Dept. of MECH Department of Mechanical Engineering ME 8492 – Kinematics of Machinery Unit I – Introduction to Mechanism - MCQ Bank 1. In a reciprocating steam engine, which of the following forms a kinematic link ? (a) cylinder and piston (b) piston rod and connecting rod (c) crank shaft and flywheel (d) flywheel and engine frame Answer: (c) 2. The motion of a piston in the cylinder of a steam engine is an example of (a) completely constrained motion (b) incompletely constrained motion (c) successfully constrained motion (d) none of these Answer: (a) 3. The motion transmitted between the teeth of gears in mesh is (a) sliding (b) rolling (c) may be rolling or sliding depending upon the shape of teeth (d) partly sliding and partly rolling Answer: (d) 4. The cam and follower without a spring forms a (a) lower pair (b) higher pair (c) self closed pair (d) force closed pair Answer: (c) 5. A ball and a socket joint forms a (a) turning pair (b) rolling pair (c) sliding pair (d) spherical pair Answer: (d) 6. The lead screw of a lathe with nut forms a (a) sliding pair (b) rolling pair (c) screw pair (d) turning pair ME 8692 – Finite Element Analysis Page 1 ChettinadTech Dept. of MECH Answer: (c) 7. When the elements of the pair are kept in contact by the action of external forces, the pair is said to be a (a) lower pair (b) higher pair (c) self closed pair (d) force closed pair Answer: (d) 8. Which of the following is a turning pair ? (a) Piston and cylinder of a reciprocating steam engine (b) Shaft with collars at both ends fitted in a circular hole (c) Lead screw of a lathe with nut (d) Ball and socket joint Answer: (b) 9. -
General N-Dimensional Rotations
General n-Dimensional Rotations Antonio Aguilera Ricardo Pérez-Aguila Centro de Investigación en Tecnologías de Información y Automatización (CENTIA) Universidad de las Américas, Puebla (UDLAP) Ex-Hacienda Santa Catarina Mártir México 72820, Cholula, Puebla [email protected] [email protected] ABSTRACT This paper presents a generalized approach for performing general rotations in the n-Dimensional Euclidean space around any arbitrary (n-2)-Dimensional subspace. It first shows the general matrix representation for the principal n-D rotations. Then, for any desired general n-D rotation, a set of principal n-D rotations is systematically provided, whose composition provides the original desired rotation. We show that this coincides with the well-known 2D and 3D cases, and provide some 4D applications. Keywords Geometric Reasoning, Topological and Geometrical Interrogations, 4D Visualization and Animation. 1. BACKGROUND arctany x x 0 arctany x x 0 The n-Dimensional Translation arctan2(y, x) 2 x 0, y 0 In this work, we will use a simple nD generalization of the 2D and 3D translations. A point 2 x 0, y 0 x (x1 , x2 ,..., xn ) can be translated by a distance vec- In this work we will only use arctan2. tor d (d ,d ,...,d ) and results x (x, x ,..., x ) , 1 2 n 1 2 n 2. INTRODUCTION which, can be computed as x x T (d) , or in its expanded matrix form in homogeneous coordinates, The rotation plane as shown in Eq.1. [Ban92] and [Hol91] have identified that if in the 2D space a rotation is given around a point, and in the x1 x2 xn 1 3D space it is given around a line, then in the 4D space, in analogous way, it must be given around a 1 0 0 0 plane. -
Fractal-Based Methods As a Technique for Estimating the Intrinsic Dimensionality of High-Dimensional Data: a Survey
INFORMATICA, 2016, Vol. 27, No. 2, 257–281 257 2016 Vilnius University DOI: http://dx.doi.org/10.15388/Informatica.2016.84 Fractal-Based Methods as a Technique for Estimating the Intrinsic Dimensionality of High-Dimensional Data: A Survey Rasa KARBAUSKAITE˙ ∗, Gintautas DZEMYDA Institute of Mathematics and Informatics, Vilnius University Akademijos 4, LT-08663, Vilnius, Lithuania e-mail: [email protected], [email protected] Received: December 2015; accepted: April 2016 Abstract. The estimation of intrinsic dimensionality of high-dimensional data still remains a chal- lenging issue. Various approaches to interpret and estimate the intrinsic dimensionality are deve- loped. Referring to the following two classifications of estimators of the intrinsic dimensionality – local/global estimators and projection techniques/geometric approaches – we focus on the fractal- based methods that are assigned to the global estimators and geometric approaches. The compu- tational aspects of estimating the intrinsic dimensionality of high-dimensional data are the core issue in this paper. The advantages and disadvantages of the fractal-based methods are disclosed and applications of these methods are presented briefly. Key words: high-dimensional data, intrinsic dimensionality, topological dimension, fractal dimension, fractal-based methods, box-counting dimension, information dimension, correlation dimension, packing dimension. 1. Introduction In real applications, we confront with data that are of a very high dimensionality. For ex- ample, in image analysis, each image is described by a large number of pixels of different colour. The analysis of DNA microarray data (Kriukien˙e et al., 2013) deals with a high dimensionality, too. The analysis of high-dimensional data is usually challenging. -
Kinematic Singularities of Mechanisms Revisited
IMA Conference on Mathematics of Robotics 9 – 11 September 2015, St Anne’s College, University of Oxford 1 Kinematic Singularities of Mechanisms Revisited By Andreas M¨uller1, Dimiter Zlatanov2 1Johannes Kepler University, Linz, Austria; 2University of Genoa, Genoa, Italy Abstract The paper revisits the definition and the identification of the singularities of kinematic chains and mechanisms. The degeneracy of the kinematics of an articulated system of rigid bodies cannot always be identified with the singularities of the configuration space. Local analysis can help identify kinematic chain singularities and better understand the way the motion characteristics change at such configurations. An example is shown that exhibits a kinematic singularity although its configuration space is a smooth manifold. 1. Introduction Kinematic singularities of a mechanism are critical configurations that can lead to a loss of structural stability or controllability. This has been a central topic in mechanism theory and still is a field of active research. A systematic approach to the study of singular configurations involves a mathematical model for the kinematic chain and its interaction with the environment via inputs and outputs. Thereupon critical configurations can be identified for the kinematic chain itself and for the input and output relations. A kinematic chain is a system of rigid bodies (links), some pairs of which are connected with joints. It is defined by specifying exactly which links are jointed (by a connectiv- ity graph [Wittenburg (1994)]), the type of each joint, and the joint's locations in the adjacent links. Mathematically, a kinematic chain is modeled by specifying its possible motions as a subset of the smooth curves on an ambient manifold, usually assumed to have a global parametrization, Vn. -
Euclidean Versus Projective Geometry
Projective Geometry Projective Geometry Euclidean versus Projective Geometry n Euclidean geometry describes shapes “as they are” – Properties of objects that are unchanged by rigid motions » Lengths » Angles » Parallelism n Projective geometry describes objects “as they appear” – Lengths, angles, parallelism become “distorted” when we look at objects – Mathematical model for how images of the 3D world are formed. Projective Geometry Overview n Tools of algebraic geometry n Informal description of projective geometry in a plane n Descriptions of lines and points n Points at infinity and line at infinity n Projective transformations, projectivity matrix n Example of application n Special projectivities: affine transforms, similarities, Euclidean transforms n Cross-ratio invariance for points, lines, planes Projective Geometry Tools of Algebraic Geometry 1 n Plane passing through origin and perpendicular to vector n = (a,b,c) is locus of points x = ( x 1 , x 2 , x 3 ) such that n · x = 0 => a x1 + b x2 + c x3 = 0 n Plane through origin is completely defined by (a,b,c) x3 x = (x1, x2 , x3 ) x2 O x1 n = (a,b,c) Projective Geometry Tools of Algebraic Geometry 2 n A vector parallel to intersection of 2 planes ( a , b , c ) and (a',b',c') is obtained by cross-product (a'',b'',c'') = (a,b,c)´(a',b',c') (a'',b'',c'') O (a,b,c) (a',b',c') Projective Geometry Tools of Algebraic Geometry 3 n Plane passing through two points x and x’ is defined by (a,b,c) = x´ x' x = (x1, x2 , x3 ) x'= (x1 ', x2 ', x3 ') O (a,b,c) Projective Geometry Projective Geometry