...Laser heads can produce dot sizes of 60μm whereas Oki Digital LED technology can produce dots as small as 34μm ... But have been pioneering the use of Digital LED technology in printing devices for many years! ... Do you like what you see?Laser heads can produce dot sizes of 60μm whereas Oki Digital LED technology can produce dots as small as 34μm ... But have been pioneering the use of Digital LED technology in printing devices for many years! ... Do you like what you see?Laser heads can produce dot sizes of 60μm whereas Oki Digital LED technology can produce dots as small as 34μm ... But have been pioneering the use of Digital LED technology in printing devices for many years! ... Do you like what you see?Laser heads can produce dot sizes of 60μm whereas Oki Digital LED technology can produce dots as small as 34μm ... But have been pioneering the use of Digital LED technology in printing devices for many years! ... Do you like what you see?Laser heads can produce dot sizes of 60μm whereas Oki Digital LED technology can produce dots as small as 34μm ... But have been pioneering the use of Digital LED technology in printing devices for many years! ... Do you like what you see?Laser heads can produce dot sizes of 60μm whereas Oki Digital LED technology can produce dots as small as 34μm ... But have been pioneering the use of Digital LED technology in printing devices for many years! ... Do you like what you see?Laser heads can produce dot sizes of 60μm whereas Oki Digital LED technology...
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...Nanotechnology (first used the term nanotechnology by Richard Feynman, in 1959) can be defined as the manipulation of atoms and molecules at nano (one billionth) scale (1–100 nm) to produce devices, structures or systems having at least one novel or superior property. The materials having at least one dimension in the nano scale are called nanomaterials. 10-9 meter (1 nanometer) to 10-7 meter (100 nanometer) Human eye can visualize up to 20μm only 1μm = 10-6 m DNA= 2.5nm- 3nm Protein= ̴ 5 nm Virus= ̴ 150 nm Human hair= ̴ 5000 nm Properties of Nanomaterials 1. The surface area to volume ratio of the nanomaterials is relatively larger than that of bulk materials of the same mass. This increases the chemical reactivity and affects strength and electrical properties of the material. 2. The quantum confinement is observed at nanometer sizes that changes the optical, electronic and magnetic properties of the material. The band gap increases as the size of the material is reduced to nanometer range. I II III IV Reduction in particle size increase in its Surface area Now, material is NANO so surface area will big… HOW? Let us consider a sphere of radius ‘r’ Surface Area = 4 x π x r2 Volume = (4/3) x π x r3 ratio of SA to Vol = 3/r Thus, radius of sphere decreases, Surface area will increase Let us consider a cube of sides 1 m Area= 6 x side2 = 6 x 1m2 = 6m2 Now, cut the same cube into 8 pieces, then the SA will increases Area= 6 x (1/2)2 x 8 = 12m2 Similarly, the...
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...Teaching Notes – Dippin’ Dots Ice Cream, as of October, 2004 Case Uses & Objectives This case can be used to augment discussions of strategic analysis, specifically both internal and external environmental analysis (Chapters 2 & 3 in Dess, Lumpkin & Eisner); and strategic formulation, specifically business level strategy (Chapter 5), with an additional focus on strategic implementation, specifically entrepreneurial development (Chapters 12 & 13). The case is written in a style that overviews the situation but intentionally avoids guiding students through any analytical framework or specific application question. In so doing, it provides the instructor with the latitude to adjust class discussion and thereby accommodate the abilities of a wide-range of students. Specifically, the instructor can invite students to reason through a situation where uncertainty exists and speculation may be required. In terms of environmental analysis, this case connects a discussion of external environmental forces and Porter’s five-force model, and how such forces affect the opportunities for growth in an industry (referencing concepts covered in Chapter 2). In terms of internal analysis of the firm, (referencing Chapter 3), the value-chain and resource-based VRIN analysis provides a case for how distribution challenges across the value-chain activities could affect value. The stakeholder perspective can also be analyzed using the balanced scorecard. As a business-level strategy...
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...Teaching Notes – Dippin’ Dots Ice Cream, as of October, 2004 Case Uses & Objectives This case can be used to augment discussions of strategic analysis, specifically both internal and external environmental analysis (Chapters 2 & 3 in Dess, Lumpkin & Eisner); and strategic formulation, specifically business level strategy (Chapter 5), with an additional focus on strategic implementation, specifically entrepreneurial development (Chapters 12 & 13). The case is written in a style that overviews the situation but intentionally avoids guiding students through any analytical framework or specific application question. In so doing, it provides the instructor with the latitude to adjust class discussion and thereby accommodate the abilities of a wide-range of students. Specifically, the instructor can invite students to reason through a situation where uncertainty exists and speculation may be required. In terms of environmental analysis, this case connects a discussion of external environmental forces and Porter’s five-force model, and how such forces affect the opportunities for growth in an industry (referencing concepts covered in Chapter 2). In terms of internal analysis of the firm, (referencing Chapter 3), the value-chain and resource-based VRIN analysis provides a case for how distribution challenges across the value-chain activities could affect value. The stakeholder perspective can also be analyzed using the balanced scorecard. As a business-level strategy case, (referencing...
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...This page intentionally left blank Physical Constants Quantity Electron charge Electron mass Permittivity of free space Permeability of free space Velocity of light Value e = (1.602 177 33 ± 0.000 000 46) × 10−19 C m = (9.109 389 7 ± 0.000 005 4) × 10−31 kg �0 = 8.854 187 817 × 10−12 F/m µ0 = 4π10−7 H/m c = 2.997 924 58 × 108 m/s Dielectric Constant (�r� ) and Loss Tangent (� �� /� � ) Material Air Alcohol, ethyl Aluminum oxide Amber Bakelite Barium titanate Carbon dioxide Ferrite (NiZn) Germanium Glass Ice Mica Neoprene Nylon Paper Plexiglas Polyethylene Polypropylene Polystyrene Porcelain (dry process) Pyranol Pyrex glass Quartz (fused) Rubber Silica or SiO2 (fused) Silicon Snow Sodium chloride Soil (dry) Steatite Styrofoam Teflon Titanium dioxide Water (distilled) Water (sea) Water (dehydrated) Wood (dry) � r �� / � 1.0005 25 8.8 2.7 4.74 1200 1.001 12.4 16 4–7 4.2 5.4 6.6 3.5 3 3.45 2.26 2.25 2.56 6 4.4 4 3.8 2.5–3 3.8 11.8 3.3 5.9 2.8 5.8 1.03 2.1 100 80 1 1.5–4 0.1 0.000 6 0.002 0.022 0.013 0.000 25 0.002 0.05 0.000 6 0.011 0.02 0.008 0.03 0.000 2 0.000 3 0.000 05 0.014 0.000 5 0.000 6 0.000 75 0.002 0.000 75 0.5 0.000 1 0.05 0.003 0.000 1 0.000 3 0.001 5 0.04 4 0 0.01 Conductivity (� ) Material Silver Copper Gold Aluminum Tungsten Zinc Brass Nickel Iron Phosphor bronze Solder Carbon steel German silver Manganin Constantan Germanium Stainless steel , S/m 6.17 × 107 4.10 × 107 3.82 × 107 1.82 × 107 1.67 × 107 1.5 × 107 1.45 × 107 1.03...
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...decide if I should sit Alfred Zingale and Matthias Arndt next to each other. It wasn’t that I was worried about conflicting views, actually it was quiet the contrary, and I didn’t want them to be able to double team the other guests. Finally I decided that just because they have essentially the same opinions, I wouldn’t separate them. In my mind they come as a unit because they had co-authored a book. The place cards had been set and I made up my mind that I would do no more rearranging. I bent over the table in my grey sleeveless dress and lit the deep red candles that were extending upward out of the floral arrangement. The guests would be arriving soon and I began to think over the whole situation. Each person has written a book about the dot com industry, how they can be successful as well as how to invest wisely in one. I was hoping to learn a lot of information so I could make a good decision on whether my company would benefit from being online. These thoughts drifted through my head until the doorbell rang. I opened the door to a short plump woman with reddish brown hair in her late 40’s was standing on my stoop. She wore a pale green dress suit, but looked quite attractive. She extended her arm, shook my hand and introduced herself as Anita Rosen. As the only woman who was attending the dinner party that night, it was a given who she was, but all the same she was quite pleasant. John Cassidy was next to arrive. He looked like the typical “guy next door” type. I bet...
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...SENIOR SECONDARY COURSE PHYSICS 1 (CORE MODULES) Coordinators Dr. Oum Prakash Sharma Sh. R.S. Dass NATIONAL INSTITUTE OF OPEN SCHOOLING A-25, INSTITUTIONAL AREA, SECTOR-62, NOIDA-201301 (UP) COURSE DESIGN COMMITTEE CHAIRMAN Prof. S.C. Garg Former Pro-Vice Chancellor IGNOU, Maidan Garhi, Delhi MEMBERS Prof. A.R. Verma Former Director, National Physical Laboratory, Delhi, 160, Deepali Enclave Pitampura, Delhi-34 Dr. Naresh Kumar Reader (Rtd.) Deptt. of Physics Hindu College, D.U. Dr. Oum Prakash Sharma Asstt. Director (Academic) NIOS, Delhi Prof. L.S. Kothari Prof. of Physics (Retd.) Delhi University 71, Vaishali, Delhi-11008 Dr. Vajayshree Prof. of Physics IGNOU, Maidan Garhi Delhi Sh. R.S. Dass Vice Principal (Rtd.) BRMVB, Sr. Sec. School Lajpat Nagar, New Delhi-110024 Dr. G.S. Singh Prof. of Physics IIT Roorkee Sh. K.S. Upadhyaya Principal Jawahar Navodaya Vidyalaya Rohilla Mohammadabad (U.P.) Dr. V.B. Bhatia Prof. of Physics (Retd.) Delhi University 215, Sector-21, Faridabad COURSE DEVELOPMENT TEAM CHAIRMAN Prof. S.C. Garg Former Pro-Vice Chancellor IGNOU, Delhi MEMBERS Prof. V.B. Bhatia 215, Sector-21, Faridabad Prof. B.B. Tripathi Prof. of Physics (Retd.), IIT Delhi 9-A, Awadhpuri, Sarvodaya Nagar Lucknow-226016 Sh. K.S. Upadhyaya Principal Navodaya Vidyalaya Rohilla Mohammadabad, (U.P.) Dr. V.P. Shrivastava Reader (Physics) D.E.S.M., NCERT, Delhi EDITORS TEAM CHAIRMAN Prof. S.C. Garg Former Pro-Vice Chancellor IGNOU, Delhi MEMBERS Prof. B.B. Tripathi Prof...
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...For the last fifty years computers have grown faster, smaller, and more powerful, transforming and benefiting our society in ways too much to count. But like any exponential explosion of resources, this growth known as Moore's law must soon come to an end. Research has already begun on what comes after our current computing revolution. This research has discovered the possibility for an entirely new type of computer, one that operates according to the laws of quantum physics, a quantum computer. A quantum computer would not just be a traditional computer built out of different parts, but a machine that would exploit the laws of quantum physics to perform certain information processing tasks in a better and more efficient manner. One demonstration of this potential is that quantum computers would break the codes that protect our modern computing infrastructure the security of every Internet transaction would be broken if a quantum computer were to be built. This potential has made quantum computing a national security concern. Yet at the same time, quantum computers will also revolutionize large parts of science in a more benevolent way. Simulating large quantum systems, something a quantum computer can easily do, is not practically possible on a traditional computer. A technology of quantum computers is also very different. For operation, quantum computer uses quantum bits (qubits). Qubit has a quaternary nature. Quantum mechanic’s laws are completely different from the laws...
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...CHAPTER 0 Contents Preface v vii Problems Solved in Student Solutions Manual 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Matrices, Vectors, and Vector Calculus Newtonian Mechanics—Single Particle Oscillations 79 127 1 29 Nonlinear Oscillations and Chaos Gravitation 149 Some Methods in The Calculus of Variations 165 181 Hamilton’s Principle—Lagrangian and Hamiltonian Dynamics Central-Force Motion 233 277 333 Dynamics of a System of Particles Motion in a Noninertial Reference Frame Dynamics of Rigid Bodies Coupled Oscillations 397 435 461 353 Continuous Systems; Waves Special Theory of Relativity iii iv CONTENTS CHAPTER 0 Preface This Instructor’s Manual contains the solutions to all the end-of-chapter problems (but not the appendices) from Classical Dynamics of Particles and Systems, Fifth Edition, by Stephen T. Thornton and Jerry B. Marion. It is intended for use only by instructors using Classical Dynamics as a textbook, and it is not available to students in any form. A Student Solutions Manual containing solutions to about 25% of the end-of-chapter problems is available for sale to students. The problem numbers of those solutions in the Student Solutions Manual are listed on the next page. As a result of surveys received from users, I continue to add more worked out examples in the text and add additional problems. There are now 509 problems, a significant number over the 4th edition. The instructor will find a large...
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...Results and Discussion After assembling the Force Table apparatus by adjusting the load and angle through trial and error to reach balance, two trials were done for the this experiment to further analyze the condition and implications of equilibrium. On this experiment, mass obtained are converted to Force and the angle for each vectors(represented by F1, F2, F3 and F4 ) as well to acquire the Resultant Vector. Table 1: Actual Values for Trial 1 and Trial 2 |Actual Values |Trial 1 |Trial 2 | |F1 |0.294 or 0.29 N |0.490 or 0.49 N | |F2 |0.539 or 0.54 N |0.588 or 0.59 N | |F3 |0.735 or 0.74 N |0.931 or 0.93 N | |F4 |0.539 or 054 N |0.686 or 0.69 N | |θ1 |0° |0° | |θ2 |67° |70° | |θ3 |178° |173° | |θ4 ...
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...polar equation to an equation in rectangular coordinates. Then identify and graph the equation. 7) r = 2 cos θ_ Write the complex number in polar form. Express the argument in degrees, rounded to the nearest tenth, if necessary. 8) 2 + 2i Plot the complex number in the complex plane. 9) -4 + i Solve the problem. Leave your answer in polar form. 10) z = 10(cos 45 + i sin 45°) w = 5(cos 15° + i sin 15°) Find . Write the expression in the standard form a + bi. 11) Find all the complex roots. Leave your answers in polar form with the argument in degrees. 12) The complex fourth roots of -16 Use the figure below. Determine whether the given statement is true or false. 13) A + H = F Find the dot product v ∙_ w. 14) v = 7i + 9j,w = -5i - 6j Find the angle between v and w. Round your answer to one decimal place, if necessary. 15) v = 6i - 5j,w = 9i + 2j State whether the vectors are parallel, orthogonal, or neither. 16) v = 4i + 2j,w = 2i - 4j Decompose v into two vectors and ,...
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...IEEE T R A N S A C T I O N S O N I N F O R M A T I O N T H E O R Y . VOL. 37, N O . I , J A N U A R Y 1991 43 A Class of Least-Squares Filtering and Identification Algorithms with Systolic Array Architectures Seth Z. Kalson, Member, IEEE, and Kung Yao, Member, IEEE Abstract -A unified approach is presented for deriving a large class of new and previously known time and order recursive least-squares algorithms with systolic array architectures, suitable for high throughput rate and VLSl implementations of space-time filtering and system identification problems. The geometrical derivation given here is unique in that no assumption is made concerning the rank of the sample data correlation matrix. Our method utilizes and extends the concept of oblique projections, as used previously in the derivations of the leastsquares lattice algorithms. Both the growing and sliding memory, exponentially weighted least-squares criteria are considered. Index Terms-Least-squares systolic arrays. tions of the least-squares estimation problem: 1) the filtering problem is to find the filtered output y , , ( t ) , where n . Y,!(t)S Cgl'(t)xi(t), i=l 1ItIT; (1.2) 2) the identification problem is to find the filter weights g ; ( t ) , i = 1;. ., n, for any t I. T This generalization of the least-squares estimation problem is important whenever practical space-time or multichannel filtering arises, such as in adaptive antenna arrays, I. INTRODUCTION decision feedback and...
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...Tutorial 1 – Introduction to MATLAB When you start MATLAB, the desktop appears in its default layout. The desktop includes these panels: • Current Folder — Access your files. • Command Window — Enter commands at the command line, indicated by the prompt (>>). • Workspace — Explore data that you create or import from files. • Command History — View or rerun commands that you entered at the command line. Help - To obtain information or description of particular function. >> help cos Exit - Leave Matlab session >> exit Clear - Removes all variables from the workspace. >> clear KMLIM TCI2261 2012/2013 Scripts New script Debugging Tools % These are comments ` Variables As you work in MATLAB, you issue commands that create variables and call functions. For example, create a variable named a by typing this statement at the command line: a=1 MATLAB adds variable a to the workspace and displays the result in the Command Window. a= 1 When you do not specify an output variable, MATLAB uses the variable ans, short for answer, to store the results of your calculation. sin(a) ans = 0.8415 If you end a statement with a semicolon, MATLAB performs the computation, but suppresses the display of output in the Command Window. sin(a); At any time you want to know the active variables you can use: KMLIM TCI2261 2012/2013 Arrays MATLAB is an abbreviation for "matrix laboratory."While other programming languages mostly work with...
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... =1 (here, xi is the i-th example) 11 . . 1 ⋮ ⋮ = ⋮ ⋮ 1 1 ⋮ ⋮ 1 ⋮ ⋮ Substituting w = in = we get = − + = = + − 1 − , We can compute as: = ( + )−1 where K = i.e. = , 11 . . ⋮ ⋮ ⋮ 1 1 ⋮ ⋮ ⋮ 11 ⋮ ⋮ ..... 1 1 ⋮ ⋮ =(xi.xj) (dot product) K: matrix of inner products of N vectors (Gram Matrix) K: matrix of similarities of example pairs (since dot product gives similarity between vectors) (1 , 1 ) . . . . . ⋮ K= ⋮ ( , 1 ) (1 , ) ⋮ ⋮ ( , ) Now, = = = , = =1 =1 (since w = ) , So in the dual form: Compute = ( + )−1 where K = , i.e. = , Evaluate on a new data point xnew as y = f = =1 , Both of these require inner products between data points Substitute the inner product with a kernel function K(x,z) = Then, we obtain a procedure for ridge regression over a new feature space F given by ɸ: x -> ɸ(x) ϵ F (kernel ridge regression) K: R2 -> R3 We can calculate the similarity (dot product) between data points in the new feature...
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...VECTORS (I) Introduction Quantities with magnitude but without direction are called scalars. Examples: distance, speed, and mass. Quantities with both magnitude and direction are called vectors. Examples: displacement, velocity, and weight. Geometrical Representation of Vectors A vector can be represented with a line segment with an arrowhead indicating its specific → direction. Thus the displacement from the point A to the point B is the vector AB , represented by the straight line AB in the direction from A to B. B A Magnitude of a vector The magnitude of the vector AB , denoted by AB , is the length (or distance) AB. Negative Vectors The negative vector – a has the same magnitude as a but is in opposite direction of a . Thus → → → → – BA = AB . Zero Vectors A zero vector is any vector of zero magnitude, denoted by 0 . [Note: 0 ≠ 0] Unit Vector ˆ A unit vector is a vector with magnitude 1. A unit vector in the direction of a is denoted by a ˆ where a = a . a Page 1 Position Vector Position vectors give the location of points with respect to a fixed point of reference (commonly known called the origin). Usually it is equivalent to the coordinates of a point. Equal Vectors → → Two vectors are equal if they have the same magnitude and direction. Thus if AB = PQ , then AB = PQ and AB // PQ . → → → → Example: The position vectors of A, B and C are a = 2i + 3j – 4k, b = 5i – j + 2k and c = 11i + j + 14k. Find the position...
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