The Proof of Innocence
Dmitri Krioukov 1
1
Cooperative Association for Internet Data Analysis (CAIDA),
University of California, San Diego (UCSD), La Jolla, CA 92093, USA
This document provides a mathematical proof of innocence for a driver accused of not stopping at a stop sign. The summary is:
1. An observer measuring the angular speed rather than linear speed of an object can perceive it as not stopping if the object decelerates and accelerates quickly near the observer and their view is briefly obstructed.
2. The document analyzes the relationship between linear and angular speed and shows that a car decelerating and accelerating rapidly near an observer could appear to not stop.
3. It applies this analysis to a scenario where a driver's view of an accused car was briefly obstructed when the car was near a stop sign, which could have caused the observer to incorrectly perceive
This document proposes a method for continuous time series alignment in human action recognition. It defines continuous versions of time series, warping paths, and the dynamic time warping (DTW) distance. The method finds the optimal continuous warping path by approximating solutions to a cost minimization problem. An experiment applies the continuous DTW to classify human activities from accelerometer data, achieving classification accuracy close to the discrete DTW method. The continuous approach solves issues with resampling data and has potential for improved approximations and optimization methods.
This chapter discusses simple harmonic motion (SHM). SHM is defined as periodic motion where the acceleration is directly proportional to and opposite of the displacement from equilibrium. The key equations of SHM are introduced, including the displacement equation x = A sin(ωt + φ) and equations for velocity, acceleration, kinetic energy, and potential energy using angular frequency ω. Examples of SHM include a simple pendulum and spring oscillations. Exercises are provided to apply the kinematic equations of SHM.
Equation of motion of a variable mass system2Solo Hermelin
This is the second of three presentations (self content) for derivation of equations of motions of a variable mass system containing moving solids (rotors, pistons,..) and elastic parts. It uses the Reynolds' Transport Theorem. It is recommended to see the first presentation before this one. Each presentation uses a different method of derivation.
The presentation is at undergraduate (physics, engineering) level.
Please sent comments for improvements to solo.hermelin@gmail.com. Thanks!
For more presentations on different subjects please visit my website at http://www.solohermelin.com
This document describes circuit analysis using Laplace transforms. It discusses analyzing both linear and nonlinear circuits in the Laplace domain. Key steps include taking the Laplace transform of circuit elements and sources, setting up equations for elements like resistors, inductors and capacitors in the s-domain, and using circuit analysis techniques like KVL, KCL to solve for output variables. It also addresses analyzing circuits with both zero and non-zero initial conditions. Examples are provided to illustrate the process.
This document describes research on modeling and optimizing the dynamics and gait of multi-link swimming robots using a "perfect fluid" model. The researchers formulated the dynamics of an articulated multi-link swimming robot moving in a planar environment. They reduced the system to first-order equations and developed simulations to examine performance for harmonic inputs and optimize displacement through inputs. Experiments were also planned using prototype robotic swimmers to compare with the theoretical model.
This document discusses carrier phase tracking in digital communication receivers. It proposes minimizing mean squared error to estimate and track the carrier phase. The carrier phase is modeled as the product of carrier frequency and time plus an unknown phase offset. An expression is derived for the derivative of mean squared error with respect to phase offset. This derivative indicates the phase error between the received signal and ideal constellation points, and can be used in a phase-locked loop to iteratively adjust the phase offset and align the signals. A second-order carrier tracking loop is presented that computes phase error, increments predicted phase by a fraction of the error, and accumulates another fraction to track frequency offsets.
The document describes and analyzes two algorithms for finding the convex hull of a set of points: a brute force algorithm and a divide and conquer algorithm.
The brute force algorithm iterates through all points three times, checking all possible line combinations, resulting in O(n3) time complexity.
The divide and conquer algorithm recursively divides the point set into halves at each step by finding the furthest point from the current left-right boundary line. It has O(n log n) time complexity.
An experiment comparing runtimes on sample point sets showed the divide and conquer approach was significantly faster than the brute force approach.
This document provides a mathematical proof of innocence for a driver accused of not stopping at a stop sign. The summary is:
1. An observer measuring the angular speed rather than linear speed of an object can perceive it as not stopping if the object decelerates and accelerates quickly near the observer and their view is briefly obstructed.
2. The document analyzes the relationship between linear and angular speed and shows that a car decelerating and accelerating rapidly near an observer could appear to not stop.
3. It applies this analysis to a scenario where a driver's view of an accused car was briefly obstructed when the car was near a stop sign, which could have caused the observer to incorrectly perceive
This document proposes a method for continuous time series alignment in human action recognition. It defines continuous versions of time series, warping paths, and the dynamic time warping (DTW) distance. The method finds the optimal continuous warping path by approximating solutions to a cost minimization problem. An experiment applies the continuous DTW to classify human activities from accelerometer data, achieving classification accuracy close to the discrete DTW method. The continuous approach solves issues with resampling data and has potential for improved approximations and optimization methods.
This chapter discusses simple harmonic motion (SHM). SHM is defined as periodic motion where the acceleration is directly proportional to and opposite of the displacement from equilibrium. The key equations of SHM are introduced, including the displacement equation x = A sin(ωt + φ) and equations for velocity, acceleration, kinetic energy, and potential energy using angular frequency ω. Examples of SHM include a simple pendulum and spring oscillations. Exercises are provided to apply the kinematic equations of SHM.
Equation of motion of a variable mass system2Solo Hermelin
This is the second of three presentations (self content) for derivation of equations of motions of a variable mass system containing moving solids (rotors, pistons,..) and elastic parts. It uses the Reynolds' Transport Theorem. It is recommended to see the first presentation before this one. Each presentation uses a different method of derivation.
The presentation is at undergraduate (physics, engineering) level.
Please sent comments for improvements to solo.hermelin@gmail.com. Thanks!
For more presentations on different subjects please visit my website at http://www.solohermelin.com
This document describes circuit analysis using Laplace transforms. It discusses analyzing both linear and nonlinear circuits in the Laplace domain. Key steps include taking the Laplace transform of circuit elements and sources, setting up equations for elements like resistors, inductors and capacitors in the s-domain, and using circuit analysis techniques like KVL, KCL to solve for output variables. It also addresses analyzing circuits with both zero and non-zero initial conditions. Examples are provided to illustrate the process.
This document describes research on modeling and optimizing the dynamics and gait of multi-link swimming robots using a "perfect fluid" model. The researchers formulated the dynamics of an articulated multi-link swimming robot moving in a planar environment. They reduced the system to first-order equations and developed simulations to examine performance for harmonic inputs and optimize displacement through inputs. Experiments were also planned using prototype robotic swimmers to compare with the theoretical model.
This document discusses carrier phase tracking in digital communication receivers. It proposes minimizing mean squared error to estimate and track the carrier phase. The carrier phase is modeled as the product of carrier frequency and time plus an unknown phase offset. An expression is derived for the derivative of mean squared error with respect to phase offset. This derivative indicates the phase error between the received signal and ideal constellation points, and can be used in a phase-locked loop to iteratively adjust the phase offset and align the signals. A second-order carrier tracking loop is presented that computes phase error, increments predicted phase by a fraction of the error, and accumulates another fraction to track frequency offsets.
The document describes and analyzes two algorithms for finding the convex hull of a set of points: a brute force algorithm and a divide and conquer algorithm.
The brute force algorithm iterates through all points three times, checking all possible line combinations, resulting in O(n3) time complexity.
The divide and conquer algorithm recursively divides the point set into halves at each step by finding the furthest point from the current left-right boundary line. It has O(n log n) time complexity.
An experiment comparing runtimes on sample point sets showed the divide and conquer approach was significantly faster than the brute force approach.
The document discusses different methods for analyzing laminar and turbulent boundary layers on airfoils, including:
1) Thwaites' method for computing laminar boundary layers numerically using momentum thickness.
2) Michel's criterion for predicting transition from laminar to turbulent flow.
3) Head's method for solving the turbulent boundary layer equations using empirical closure relations.
4) The Squire-Young formula for predicting drag from boundary layer properties.
This document summarizes Chan's optimal output sensitive algorithms for computing convex hulls in 2D and 3D. It begins with basic notions of convex hulls, then describes Jarvis's march and Graham's scan algorithms for 2D convex hulls. It introduces Chan's 2D algorithm, which combines Jarvis's march and Graham's scan to achieve optimal O(n log h) time complexity, where h is the size of the hull. The document then explains how Chan extended this approach to 3D convex hulls by replacing the components with their 3D analogues, resulting in an optimal output sensitive 3D convex hull algorithm.
Convex Hull - Chan's Algorithm O(n log h) - Presentation by Yitian Huang and ...Amrinder Arora
Chan's Algorithm for Convex Hull Problem. Output Sensitive Algorithm. Takes O(n log h) time. Presentation for the final project in CS 6212/Spring/Arora.
- Carlos Ragone fell 500 feet into snow after his anchor gave way while mountain climbing. The snow broke his fall, creating a 4 foot deep hole.
- Assuming constant acceleration during his impact with the snow, the estimated average acceleration was about 125g as he slowed to a stop within the 4 foot deep hole.
- A runner ran 2.5 km in 9 minutes, then walked back to the starting point over 30 minutes. Her average velocity was 5.2 km/hr for running, -0.83 km/hr for walking, and 0 km/hr for the total trip. Her average speed for the entire trip was 1.3 km/hr.
Presents the Tracking methods of moving targets by sensors (radar, electro optics,..).
For comments please contact me at solo.hermelin@gmail.com.
For more presentations visit my website at http://www.solohermelin.com.
I recommend to view this presentation on my website at RADAR folder, Tracking Systems subfolder.
1. The document discusses causality and relativity of simultaneity using spacetime diagrams. It explains that nothing can travel faster than the speed of light based on analyses of pole-barn paradox scenarios and Lorentz transformations.
2. Key concepts from special relativity are reviewed, including Lorentz transformations, 4-vectors like 4-displacement and 4-velocity, proper time, rest mass, 4-momentum, and conservation of 4-momentum. Collisions are also discussed in relation to conservation of 4-momentum.
3. Causality is maintained in special relativity because the order of events cannot be reversed between reference frames if they are causally connected within or on each other's light cones. Faster-
This document contains solutions to problems from Chapter 2 on kinematics.
1) It calculates average velocities for objects moving with constant acceleration based on displacement and time intervals.
2) It uses kinematic equations to solve for quantities like displacement, velocity, and acceleration from graphs of position vs. time.
3) It finds the slope of tangent lines on x-t graphs to determine instantaneous velocity.
The document discusses linear transformations and their properties. It defines key concepts such as the kernel, range, rank, and nullity of a linear transformation. The kernel is the set of all vectors that map to the zero vector, and is a subspace of the domain. The range is the set of images of all vectors under the transformation. The rank is the dimension of the range, and the nullity is the dimension of the kernel. A linear transformation is one-to-one if different vectors always map to different outputs, and onto if its range is equal to the codomain. An isomorphism is a linear transformation that is both one-to-one and onto, and maps spaces to spaces of the same dimension.
The Laplace transform is used to solve differential equations by transforming them into algebraic equations that are easier to solve. It was developed in the late 18th century building on prior work by Euler and Lagrange. The transform switches a function of time f(t) to a function of a complex variable F(s). It can be applied to ordinary and partial differential equations to reduce their dimension by one. Real-world applications of the Laplace transform include modeling semiconductor mobility, call completion in wireless networks, vehicle vibrations, and electromagnetic field behavior.
The document describes the Floyd-Warshall algorithm for finding shortest paths in a weighted graph. It discusses the all-pairs shortest path problem, previous solutions using Dijkstra's algorithm and dynamic programming, and then presents the Floyd-Warshall algorithm as another dynamic programming solution. The algorithm works by computing the shortest path between all pairs of vertices where intermediate vertices are restricted to a given set. It does this using a bottom-up computation in O(V^3) time and O(V^2) space.
The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
Application of Laplace transforms for solving transient equations of electrical circuits. Initial and final value theorems. Unit step, impulse and ramp inputs. Laplace transform for shifted and singular functions.
This document summarizes key concepts about transfer functions and convolution:
1. Convolution represents linear time-invariant (LTI) systems, where the output is the impulse response convolved with the input. In the frequency domain, the transfer function is the frequency response.
2. Properties of convolution systems are that they are linear, causal, and time-invariant. Composition of convolution systems corresponds to multiplication of transfer functions.
3. Examples of convolution systems and calculating their transfer functions are presented for circuits, mechanical systems, and communication channels. Interpreting the impulse response provides insight into how past inputs affect current system outputs.
This document contains partial solutions to homework problems from dynamics courses taught between 2002-2003. It was compiled by the author from PDF files to help recipients with their studies. It includes solutions for chapters 13-17 and past exams, but is not a complete set of answers. The mass, pulley, and rod problems solved here provide example solutions that could aid readers in learning concepts in engineering dynamics.
1) A car traveling at 60 m/s takes 4.0 seconds to react and decelerate at -5.0 m/s^2 until stopping. The normal stopping distance is 80 m.
2) For a drunk driver reacting in 6.0 seconds and decelerating at -5.0 m/s^2, the stopping distance is 120 m.
3) Kinematic equations are used to determine the stopping distance from the initial velocity, deceleration, and reaction time.
The document discusses several algorithms for computing the convex hull of a set of points, including brute force, quick hull, divide and conquer, Graham's scan, and Jarvis march. It provides details on the time complexity of each algorithm, ranging from O(n^2) for brute force to O(n log n) for quick hull, divide and conquer, and Graham's scan. Jarvis march runs in O(nh) time where h is the number of points on the convex hull.
1. The document provides solutions to physics problems involving charged spheres and cubes, rotating wheels, friction on motorcycles, chasing foxes and rabbits, growing raindrops, and springs wrapped around poles.
2. It derives equations and ratios for potentials, field strengths, minimum distances, meeting points, accelerations, temperatures, spring constants, and more using principles of electrostatics, kinematics, dynamics, and thermodynamics.
3. The solutions are detailed and rigorous, employing techniques like Gauss' law, dimensional analysis, conservation of energy, and modeling circular and conical motions.
The document presents Aabid Shah's presentation on the divide-and-conquer algorithm and Graham's scan for computing the convex hull of a set of points. It introduces divide-and-conquer as a technique that divides a problem into smaller subproblems, solves the subproblems recursively, and combines the solutions. Graham's scan is described as a divide-and-conquer algorithm that uses a stack to find the convex hull of a set of points in O(n log n) time by sorting points by polar angle and checking for non-left turns. The key steps of Graham's scan and properties of the convex hull are outlined.
This document introduces the concept of a limit, which is the foundation of calculus. It provides an intuitive approach to understanding limits by examining how the instantaneous velocity of an object can be approximated by calculating average velocities over increasingly small time intervals. The key idea is that as the time intervals approach zero, the average velocities approach a limiting value that represents the instantaneous velocity. More generally, a limit describes how a function's values approach a specific number L as the input values approach a particular value a. The process of determining a limit involves first conjecturing the limit based on sampling function values, and then verifying it with a more rigorous analysis.
This document discusses limits and continuity in calculus. It begins by explaining how limits were used to define instantaneous rates of change in velocity and acceleration, which were fundamental to the development of calculus. The chapter then aims to develop the concept of the limit intuitively before providing precise mathematical definitions. Limits are introduced as the value a function approaches as the input gets arbitrarily close to a given value, without actually reaching it. Several examples are provided to illustrate how to determine limits through sampling inputs and making conjectures.
The document discusses different methods for analyzing laminar and turbulent boundary layers on airfoils, including:
1) Thwaites' method for computing laminar boundary layers numerically using momentum thickness.
2) Michel's criterion for predicting transition from laminar to turbulent flow.
3) Head's method for solving the turbulent boundary layer equations using empirical closure relations.
4) The Squire-Young formula for predicting drag from boundary layer properties.
This document summarizes Chan's optimal output sensitive algorithms for computing convex hulls in 2D and 3D. It begins with basic notions of convex hulls, then describes Jarvis's march and Graham's scan algorithms for 2D convex hulls. It introduces Chan's 2D algorithm, which combines Jarvis's march and Graham's scan to achieve optimal O(n log h) time complexity, where h is the size of the hull. The document then explains how Chan extended this approach to 3D convex hulls by replacing the components with their 3D analogues, resulting in an optimal output sensitive 3D convex hull algorithm.
Convex Hull - Chan's Algorithm O(n log h) - Presentation by Yitian Huang and ...Amrinder Arora
Chan's Algorithm for Convex Hull Problem. Output Sensitive Algorithm. Takes O(n log h) time. Presentation for the final project in CS 6212/Spring/Arora.
- Carlos Ragone fell 500 feet into snow after his anchor gave way while mountain climbing. The snow broke his fall, creating a 4 foot deep hole.
- Assuming constant acceleration during his impact with the snow, the estimated average acceleration was about 125g as he slowed to a stop within the 4 foot deep hole.
- A runner ran 2.5 km in 9 minutes, then walked back to the starting point over 30 minutes. Her average velocity was 5.2 km/hr for running, -0.83 km/hr for walking, and 0 km/hr for the total trip. Her average speed for the entire trip was 1.3 km/hr.
Presents the Tracking methods of moving targets by sensors (radar, electro optics,..).
For comments please contact me at solo.hermelin@gmail.com.
For more presentations visit my website at http://www.solohermelin.com.
I recommend to view this presentation on my website at RADAR folder, Tracking Systems subfolder.
1. The document discusses causality and relativity of simultaneity using spacetime diagrams. It explains that nothing can travel faster than the speed of light based on analyses of pole-barn paradox scenarios and Lorentz transformations.
2. Key concepts from special relativity are reviewed, including Lorentz transformations, 4-vectors like 4-displacement and 4-velocity, proper time, rest mass, 4-momentum, and conservation of 4-momentum. Collisions are also discussed in relation to conservation of 4-momentum.
3. Causality is maintained in special relativity because the order of events cannot be reversed between reference frames if they are causally connected within or on each other's light cones. Faster-
This document contains solutions to problems from Chapter 2 on kinematics.
1) It calculates average velocities for objects moving with constant acceleration based on displacement and time intervals.
2) It uses kinematic equations to solve for quantities like displacement, velocity, and acceleration from graphs of position vs. time.
3) It finds the slope of tangent lines on x-t graphs to determine instantaneous velocity.
The document discusses linear transformations and their properties. It defines key concepts such as the kernel, range, rank, and nullity of a linear transformation. The kernel is the set of all vectors that map to the zero vector, and is a subspace of the domain. The range is the set of images of all vectors under the transformation. The rank is the dimension of the range, and the nullity is the dimension of the kernel. A linear transformation is one-to-one if different vectors always map to different outputs, and onto if its range is equal to the codomain. An isomorphism is a linear transformation that is both one-to-one and onto, and maps spaces to spaces of the same dimension.
The Laplace transform is used to solve differential equations by transforming them into algebraic equations that are easier to solve. It was developed in the late 18th century building on prior work by Euler and Lagrange. The transform switches a function of time f(t) to a function of a complex variable F(s). It can be applied to ordinary and partial differential equations to reduce their dimension by one. Real-world applications of the Laplace transform include modeling semiconductor mobility, call completion in wireless networks, vehicle vibrations, and electromagnetic field behavior.
The document describes the Floyd-Warshall algorithm for finding shortest paths in a weighted graph. It discusses the all-pairs shortest path problem, previous solutions using Dijkstra's algorithm and dynamic programming, and then presents the Floyd-Warshall algorithm as another dynamic programming solution. The algorithm works by computing the shortest path between all pairs of vertices where intermediate vertices are restricted to a given set. It does this using a bottom-up computation in O(V^3) time and O(V^2) space.
The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
Application of Laplace transforms for solving transient equations of electrical circuits. Initial and final value theorems. Unit step, impulse and ramp inputs. Laplace transform for shifted and singular functions.
This document summarizes key concepts about transfer functions and convolution:
1. Convolution represents linear time-invariant (LTI) systems, where the output is the impulse response convolved with the input. In the frequency domain, the transfer function is the frequency response.
2. Properties of convolution systems are that they are linear, causal, and time-invariant. Composition of convolution systems corresponds to multiplication of transfer functions.
3. Examples of convolution systems and calculating their transfer functions are presented for circuits, mechanical systems, and communication channels. Interpreting the impulse response provides insight into how past inputs affect current system outputs.
This document contains partial solutions to homework problems from dynamics courses taught between 2002-2003. It was compiled by the author from PDF files to help recipients with their studies. It includes solutions for chapters 13-17 and past exams, but is not a complete set of answers. The mass, pulley, and rod problems solved here provide example solutions that could aid readers in learning concepts in engineering dynamics.
1) A car traveling at 60 m/s takes 4.0 seconds to react and decelerate at -5.0 m/s^2 until stopping. The normal stopping distance is 80 m.
2) For a drunk driver reacting in 6.0 seconds and decelerating at -5.0 m/s^2, the stopping distance is 120 m.
3) Kinematic equations are used to determine the stopping distance from the initial velocity, deceleration, and reaction time.
The document discusses several algorithms for computing the convex hull of a set of points, including brute force, quick hull, divide and conquer, Graham's scan, and Jarvis march. It provides details on the time complexity of each algorithm, ranging from O(n^2) for brute force to O(n log n) for quick hull, divide and conquer, and Graham's scan. Jarvis march runs in O(nh) time where h is the number of points on the convex hull.
1. The document provides solutions to physics problems involving charged spheres and cubes, rotating wheels, friction on motorcycles, chasing foxes and rabbits, growing raindrops, and springs wrapped around poles.
2. It derives equations and ratios for potentials, field strengths, minimum distances, meeting points, accelerations, temperatures, spring constants, and more using principles of electrostatics, kinematics, dynamics, and thermodynamics.
3. The solutions are detailed and rigorous, employing techniques like Gauss' law, dimensional analysis, conservation of energy, and modeling circular and conical motions.
The document presents Aabid Shah's presentation on the divide-and-conquer algorithm and Graham's scan for computing the convex hull of a set of points. It introduces divide-and-conquer as a technique that divides a problem into smaller subproblems, solves the subproblems recursively, and combines the solutions. Graham's scan is described as a divide-and-conquer algorithm that uses a stack to find the convex hull of a set of points in O(n log n) time by sorting points by polar angle and checking for non-left turns. The key steps of Graham's scan and properties of the convex hull are outlined.
This document introduces the concept of a limit, which is the foundation of calculus. It provides an intuitive approach to understanding limits by examining how the instantaneous velocity of an object can be approximated by calculating average velocities over increasingly small time intervals. The key idea is that as the time intervals approach zero, the average velocities approach a limiting value that represents the instantaneous velocity. More generally, a limit describes how a function's values approach a specific number L as the input values approach a particular value a. The process of determining a limit involves first conjecturing the limit based on sampling function values, and then verifying it with a more rigorous analysis.
This document discusses limits and continuity in calculus. It begins by explaining how limits were used to define instantaneous rates of change in velocity and acceleration, which were fundamental to the development of calculus. The chapter then aims to develop the concept of the limit intuitively before providing precise mathematical definitions. Limits are introduced as the value a function approaches as the input gets arbitrarily close to a given value, without actually reaching it. Several examples are provided to illustrate how to determine limits through sampling inputs and making conjectures.
Transient analysis examines how circuit voltages and currents change during a transition between steady state conditions. When a change occurs, such as a switch opening or closing, the circuit moves from one steady state to another.
The summary discusses transient analysis of RC circuits. In a source-free RC circuit, the capacitor voltage decays exponentially with a time constant of RC after a switch change. In a driven RC circuit with a voltage source, the capacitor voltage reaches a final value that is the combination of an exponentially decaying complementary solution and a steady-state particular solution.
Introduction to differential calculus. This slide deals with the concept of differential calculus. An explanation is given that how derivative of a function can be calculated graphically and algebraically
The document analyzes the motion of an inverted pendulum with an oscillating support. It uses Lagrangian mechanics to derive an equation of motion for the pendulum's angle θ. For small oscillations of θ, the equation is approximated as θ̈ + θaω2cos(ωt) - ω02 = 0. This shows the pendulum's motion depends on the frequency and amplitude of the support oscillations. If the support frequency ω is high enough, the pendulum undergoes stable oscillations instead of falling over. An analytical approximation of θ as a small oscillation correlated with the support motion is derived, explaining this stabilizing effect.
The document analyzes the motion of an inverted pendulum with an oscillating support. It uses Lagrangian mechanics to derive an equation of motion for the pendulum's angle θ. For small oscillations of θ, the equation is approximated as θ̈ + θaω2cos(ωt) - ω02 = 0, where a and ω are constants related to the support oscillation. If the support oscillation frequency ω is high enough, θ undergoes small oscillations correlated with cos(ωt), preventing the pendulum from falling over on average. The full motion of θ consists of these small oscillations modulated by an overall oscillation with frequency Ω related to ω and a.
The document discusses rectilinear kinematics and concepts like instantaneous speed, constant speed, velocity, and acceleration. It provides examples and tables showing the distance, time, speed, and acceleration of a car. It defines velocity as involving both speed and direction through displacement, and defines acceleration as a change in velocity or direction over time. An example problem is also included to determine time to reach a velocity, acceleration at a given speed, net displacement over an interval, and to plot the corresponding diagrams.
GEN PHYSICS 1 WEEK 2 KINEMATICS IN ONE DIMENSION.pptxAshmontefalco4
This document provides an overview of kinematics in one dimension, including graphical representations of motion, motion along a straight line, and motion with constant acceleration. It discusses topics like displacement, velocity, acceleration, and their relationships. Examples are provided to demonstrate calculating variables like position, velocity and acceleration from graphs or equations of motion. Kinematic formulas are also introduced for problems involving constant acceleration.
The document summarizes key concepts from Chapter 2 of a Physics textbook on kinematics of linear motion. It discusses the following in 3 sentences:
Linear motion can be one-dimensional or two-dimensional projectile motion. Equations of motion include relationships between displacement, velocity, acceleration, and time. Uniformly accelerated motion follows equations that relate the initial and final velocity, acceleration, and time to determine displacement and distance traveled.
This document provides a preview of key concepts in calculus including:
1) The area problem which examines how to compute the area under a curve using limits of approximating polygons and rectangles.
2) The tangent problem which explores how to determine the slope and tangent line to a curve using limits of secant lines.
3) How velocity can be defined as the limit of the change in distance over the change in time between intervals approaching zero.
4) Limits of sequences and how real numbers like π and infinite decimals can be represented as the limit of a sequence of approximations.
5) Infinite series and how they can be used to represent some real numbers as an infinite sum.
College Physics 1st Edition Etkina Solutions ManualHowardRichsa
This document contains a chapter from the textbook "College Physics" by Etkina, Gentile, and Van Heuvelen. It includes multiple choice and conceptual questions about kinematics concepts like displacement, velocity, acceleration. It also includes practice problems asking students to draw motion diagrams and choose reference frames. The key concepts covered are scalar and vector quantities, relationships between displacement, velocity and acceleration, and using graphs to represent motion.
The document discusses particle kinematics and concepts such as displacement, velocity, acceleration, and their relationships for rectilinear and curvilinear motion. Key concepts covered include definitions of displacement, average and instantaneous velocity, acceleration, graphical representations of position, velocity, and acceleration over time, and analytical methods for solving kinematic equations involving constant or variable acceleration. Several sample problems are provided to illustrate applying these kinematic concepts and relationships to solve for variables like time, velocity, acceleration, and displacement given relevant conditions.
Three events occur:
1) An airplane carrying an atomic clock flies back and forth for 15 hours at an average speed of 140 m/s.
2) The time on the clock is compared to an atomic clock kept on the ground.
3) Using time dilation, the elapsed time on the ground clock is calculated to be 9.5 nanoseconds longer than the time on the airplane clock.
GRAPHICAL REPRESENTATION OF MOTION💖.pptxssusere853b3
Graphical representations like distance-time graphs and velocity-time graphs can be used to describe motion. Distance-time graphs show the dependence of distance on time, with distance on the y-axis and time on the x-axis. The slope of a distance-time graph gives the object's speed. Velocity-time graphs show the dependence of velocity on time, with velocity on the y-axis and time on the x-axis. The area under a velocity-time graph gives the object's displacement. These graphs can indicate whether motion is uniform or non-uniform and can be used to calculate values like speed, velocity, distance, and acceleration.
Research on The Control of Joint Robot TrajectoryIJRESJOURNAL
ABSTRACT: This paper relates to a Robot that belongs to the category of Joint Robot.In the article,we analyze the path planning and control system of the robot,specifically speaking,it involves the interpolation of the robot trajectory, the analysis of the inverse kinematics, the introduction of the method to reduce the trajectory error, the optimization of the trajectory and in the end, the corresponding control system is designed according to the relevant parameters. This research project first introduces the importance of the robot, and then analyzes the whole process of the robot from the grasping pin, the screw to they are delivered to the designated position,finally, the process is introduced in detail, and the simulation result is displayed.
Amazon.comThe three main activities that e.docxnettletondevon
Amazon.com
The three main activities that exist within Amazon’s logistics sector are listed below:
Order Processing
Inventory Management
Freight Transportation
Logistics (IT)Management System
Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2002). Supply chain logistics management (Vol. 2). New York, NY: McGraw-Hill
2
The fourth functional area of Amazon logistics place take within.
Warehousing, Material Handling and Packaging
Facility Networks
Logistics (IT) Management System cont.
Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2002). Supply chain logistics management (Vol. 2). New York, NY: McGraw-Hill
3
Amazon Web Services
Amazon Simple Storage Service
Information System
Customer Relationship Management
Amazon IT Management Systems
Chaffey, D. et al (2004) Business Information Management: Improving Performance using Information Systems (1st Edition) Prentice Hall Pearson Education. England
4
Rail
Truck
Air
Intermodal Transportation
Cost of transport
Total system cost
Speed of transportation
Outsourcing
Third-party logistics service provider (3PL)
Transportation
Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2002). Supply chain logistics management (Vol. 2). New York, NY: McGraw-Hill.
5
Distribution Management Operations
Yet Another Massive Market on the ... - media.cygnus.com. (n.d.). Retrieved April 5, 2016, from http://media.cygnus.com/files/base/FL/document/2015/10/RW_Baird_AMZN2015-10-19_report.pdf
6
Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2002). Supply chain logistics management (Vol. 2). New York, NY: McGraw-Hill.
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1. The Proof of Innocence
Dmitri Krioukov1
1
Cooperative Association for Internet Data Analysis (CAIDA),
University of California, San Diego (UCSD), La Jolla, CA 92093, USA
We show that if a car stops at a stop sign, an observer, e.g., a police officer, located at a certain
distance perpendicular to the car trajectory, must have an illusion that the car does not stop, if the
following three conditions are satisfied: (1) the observer measures not the linear but angular speed
of the car; (2) the car decelerates and subsequently accelerates relatively fast; and (3) there is a
short-time obstruction of the observer’s view of the car by an external object, e.g., another car, at
the moment when both cars are near the stop sign.
I. INTRODUCTION
It is widely known that an observer measuring the
speed of an object passing by, measures not its actual
linear velocity by the angular one. For example, if we
stay not far away from a railroad, watching a train ap-
proaching us from far away at a constant speed, we first
perceive the train not moving at all, when it is really far,
but when the train comes closer, it appears to us mov-
ing faster and faster, and when it actually passes us, its
visual speed is maximized.
This observation is the first building block of our proof
of innocence. To make this proof rigorous, we first con-
sider the relationship between the linear and angular
speeds of an object in the toy example where the ob-
ject moves at a constant linear speed. We then proceed
to analyzing a picture reflecting what really happened
in the considered case, that is, the case where the linear
speed of an object is not constant, but what is constant
instead is the deceleration and subsequent acceleration of
the object coming to a complete stop at a point located
closest to the observer on the object’s linear trajectory.
Finally, in the last section, we consider what happens
if at that critical moment the observer’s view is briefly
obstructed by another external object.
II. CONSTANT LINEAR SPEED
Consider Fig. 1 schematically showing the geometry of
the considered case, and assume for a moment that C’s
linear velocity is constant in time t,
v(t) ≡ v0. (1)
Without loss of generality we can choose time units t such
that t = 0 corresponds to the moment when C is at S.
Then distance x is simply
x(t) = v0t. (2)
Observer O visually measures not the linear speed of C
but its angular speed given by the first derivative of an-
gle α with respect to time t,
ω(t) =
dα
dt
. (3)
O (police officer)
S (stop sign)C (car)
r0
L (lane)
α
v x
FIG. 1: The diagram showing schematically the geometry of
the considered case. Car C moves along line L. Its current
linear speed is v, and the current distance from stop sign S
is x, |CS| = x. Another road connects to L perpendicularly
at S. Police officer O is located on that road at distance r0
from the intersection, |OS| = r0. The angle between OC and
OS is α.
To express α(t) in terms of r0 and x(t) we observe from
triangle OCS that
tan α(t) =
x(t)
r0
, (4)
leading to
α(t) = arctan
x(t)
r0
. (5)
Substituting the last expression into Eq. (3) and using
the standard differentiation rules there, i.e., specifically
the fact that
d
dt
arctan f(t) =
1
1 + f2
df
dt
, (6)
where f(t) is any function of t, but it is f(t) = v0t/r0
here, we find that the angular speed of C that O observes
arXiv:1204.0162v1[physics.pop-ph]1Apr2012
2. 2
−10 −5 0 5 10
0
0.2
0.4
0.6
0.8
1
t (seconds)
ω(t)(radianspersecond)
FIG. 2: The angular velocity ω of C observed by O as a
function of time t if C moves at constant linear speed v0. The
data is shown for v0 = 10 m/s = 22.36 mph and r0 = 10 m =
32.81 ft.
as a function of time t is
ω(t) =
v0/r0
1 + v0
r0
2
t2
. (7)
This function is shown in Fig. 2. It confirms and quan-
tifies the observation discussed in the previous section,
that at O, the visual angular speed of C moving at a
constant linear speed is not constant. It is the higher,
the closer C to O, and it goes over a sharp maximum at
t = 0 when C is at the closest point S to O on its linear
trajectory L.
III. CONSTANT LINEAR DECELERATION
AND ACCELERATION
In this section we consider the situation closely mim-
icking what actually happened in the considered case.
Specifically, C, instead of moving at constant linear
speed v0, first decelerates at constant deceleration a0,
then comes to a complete stop at S, and finally acceler-
ates with the same constant acceleration a0.
In this case, distance x(t) is no longer given by Eq. (2).
It is instead
x(t) =
1
2
a0t2
. (8)
If this expression does not look familiar, it can be easily
derived. Indeed, with constant deceleration/acceleration,
the velocity is
v(t) = a0t, (9)
−10 −5 0 5 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
t (seconds)
ω(t)(radianspersecond)
a
0
= 1 m/s2
a0
= 3 m/s
2
a0
= 10 m/s2
FIG. 3: The angular velocity ω of C observed by O as a func-
tion of time t if C moves with constant linear deceleration a0,
comes to a complete stop at S at time t = 0, and then moves
with the same constant linear acceleration a0. The data are
shown for r0 = 10 m.
but by the definition of velocity,
v(t) =
dx
dt
, (10)
so that
dx = v(t) dt. (11)
Integrating this equation we obtain
x(t) =
x
0
dx =
t
0
v(t) dt = a0
t
0
t dt =
1
2
a0t2
. (12)
Substituting the last expression into Eq. (5) and then dif-
ferentiating according to Eq. (3) using the rule in Eq. (6)
with f(t) = a0t2
/(2r0), we obtain the angular velocity of
C that O observes
ω(t) =
a0
r0
t
1 + 1
4
a0
r0
2
t4
. (13)
This function is shown in Fig. 3 for different values
of a0. In contrast to Fig. 2, we observe that the angular
velocity of C drops to zero at t = 0, which is expected
because C comes to a complete stop at S at this time.
However, we also observe that the higher the decelera-
tion/acceleration a0, the more similar the curves in Fig. 3
become to the curve in Fig. 2. In fact, the blue curve in
Fig. 3 is quite similar to the one in Fig. 2, except the nar-
row region between the two peaks in Fig. 3, where the
angular velocity quickly drops down to zero, and then
quickly rises up again to the second maximum.
3. 3
O (police officer)
S (stop sign)C1 (car #1)
r0
L1 (lane #1)
L2 (lane #2)C2 (car #2)
l1
C1 = Toyota Yaris
l1 = 150 in
l2
C2 ≈ Subaru Outback
l2 = 189 in
l2 − l1 = 39 in ≈ 1 m
FIG. 4: The diagram showing schematically the brief obstruc-
tion of view that happened in the considered case. The O’s
observations of car C1 moving in lane L1 are briefly obstructed
by another car C2 moving in lane L2 when both cars are near
stop sign S. The region shaded by the grey color is the area
of poor visibility for O.
IV. BRIEF OBSTRUCTION OF VIEW AROUND
t = 0.
Finally, we consider what happens if the O’s observa-
tions are briefly obstructed by an external object, i.e.,
another car, see Fig. 4 for the diagram depicting the
considered situation. The author/defendant (D.K.) was
driving Toyota Yaris (car C1 in the diagram), which is
one of the shortest cars avaialable on the market. Its
lengths is l1 = 150 in. (Perhaps only the Smart Cars are
shorter?) The exact model of the other car (C2) is un-
known, but it was similar in length to Subaru Outback,
whose exact length is l2 = 189 in.
To estimate times tp and tf at which the partial and,
respectively, full obstructions of view of C1 by C2 be-
gan and ended, we must use Eq. (8) substituting there
xp = l2 + l1 = 8.16 m, and xf = l2 − l1 = 0.99 m, re-
spectively. To use Eq. (8) we have to know C1’s decelera-
tion/acceleration a0. Unfortunately, it is difficult to mea-
sure deceleration or acceleration without special tools,
but we can roughly estimate it as follows. D.K. was badly
sick with cold on that day. In fact, he was sneezing while
approaching the stop sign. As a result he involuntary
pushed the brakes very hard. Therefore we can assume
that the deceleration was close to maximum possible for
a car, which is of the order of 10 m/s2
= 22.36 mph/s.
We will thus use a0 = 10 m/s2
. Substituting these values
of a0, xp, and xf into Eq. (8) inverted for t,
t =
2x
a0
, (14)
we obtain
tp = 1.31 s, (15)
tf = 0.45 s. (16)
The full durations of the partial and full obstructions are
then just double these times.
Next, we are interested in time t at which the angular
speed of C1 observed by O without any obstructions goes
over its maxima, as in Fig. 3. The easiest way to find t
is to recall that the value of the first derivative of the
angular speed at t is zero,
dω
dt
= ˙ω(t ) = 0. (17)
To find ˙ω(t) we just differentiate Eq. (13) using the stan-
dard differentiation rules, which yield
˙ω(t) = 4
a0
r0
1 − 3
4
a0
r0
2
t4
1 + 1
4
a0
r0
2
t4
2 . (18)
This function is zero only when the numerator is zero, so
that the root of Eq. (17) is
t =
4 4
3
r0
a0
. (19)
Substituting the values of a0 = 10 m/s2
and r0 = 10 m
in this expression, we obtain
t = 1.07 s. (20)
We thus conclude that time t lies between tf and tp,
tf < t < tp, (21)
and that differences between all these times is actually
quite small, compare Eqs. (15,16,20).
These findings mean that the angular speed of C1 as
observed by O went over its maxima when the O’s view
of C1 was partially obstructed by C2, and very close in
time to the full obstruction. In lack of complete informa-
tion, O interpolated the available data, i.e., the data for
times t > t ≈ tf ≈ tp, using the simplest and physiologi-
cally explainable linear interpolation, i.e., by connecting
the boundaries of available data by a linear function. The
result of this interpolation is shown by the dashed curve
in Fig. 5. It is remarkably similar to the curve show-
ing the angular speed of a hypothetical object moving at
constant speed v0 = 8 m/s ≈ 18 mph.
V. CONCLUSION
In summary, police officer O made a mistake, confusing
the real spacetime trajectory of car C1—which moved
at approximately constant linear deceleration, came to a
4. 4
−10 −8 −6 −4 −2 0 2 4 6 8 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
t (seconds)
ω(t)(radianspersecond)
C
1
’s speed
O’s perception
v
0
= 8 m/s
t
f
t′
tp
FIG. 5: The real angular speed of C1 is shown by the blue solid
curve. The O’s interpolation is the dashed red curve. This
curve is remarkably similar to the red solid curve, showing
the angular speed of a hypothetical object moving at constant
linear speed v0 = 8 m/s = 17.90 mph.
complete stop at the stop sign, and then started moving
again with the same acceleration, the blue solid line in
Fig. 5—for a trajectory of a hypothetical object moving
at approximately constant linear speed without stopping
at the stop sign, the red solid line in the same figure.
However, this mistake is fully justified, and it was made
possible by a combination of the following three factors:
1. O was not measuring the linear speed of C1 by any
special devices; instead, he was estimating the vi-
sual angular speed of C1;
2. the linear deceleration and acceleration of C1 were
relatively high; and
3. the O’s view of C1 was briefly obstructed by an-
other car C2 around time t = 0.
As a result of this unfortunate coincidence, the O’s per-
ception of reality did not properly reflect reality.