COURSE WORK 1 FORWARD AND INVERSE KINEMATICS OF THE FANUC 120iB/10L • Introduction The Fanuc ARC Mate 120iB/10L is one of the two models of robot manipulators belonging to the series ARC Mate 120iB. Both robots in the family are 6R manipulators with decoupled architecture. The ARC Mate 120iB series is a modular construction, electric […]
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COURSE WORK 1 FORWARD AND INVERSE KINEMATICS OF THE FANUC 120iB/10L • Introduction The Fanuc ARC Mate 120iB/10L is one of the two models of robot manipulators belonging to the series ARC Mate 120iB. Both robots in the family are 6R manipulators with decoupled architecture. The ARC Mate 120iB series is a modular construction, electric
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Adaptive Signal Processing and Machine Intelligence Coursework Prof. Danilo P. Mandic TAs: Giuseppe Calvi, Ilia Kisil, Harry Davies, Shengxi Li, Takashi Nakamura February 5, 2019 1 Contents Guidelines 3 1 Classical and Modern Spectrum Estimation 4 1.1 PropertiesofPowerSpectralDensity(PSD) …………………………… 4 1.2 Periodogram-basedMethodsAppliedtoReal–WorldData ……………………. 5 1.3 CorrelationEstimation …………………………………….. 5 1.4 SpectrumofAutoregressiveProcesses ……………………………… 7 1.5 RealWorldSignals:RespiratorySinusArrhythmiafromRR-Intervals .
ECS130 Homework Assignment 4 Due: 4:00pm, February 20, 2019 1. Ex 6.2 2. Ex 6.3 3. Ex 6.4 4. Ex 6.6 5. Ex 6.9 6. Consider the matrix 2 −1 −1 2 … … A= …… .. . −1 −1 2 Taking A to be a 10 by 10
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Assignment 3C – New MATLAB (55 points) Part 1. (5 points) Create a single MATLAB script that: • Tells the user that this is a program which will determine the surface area and volume of three different types of solid objects: a right circular cone, a cylinder, and a rectangular prism (assume the rectangular prism
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COMP532 Assignment 1 – Reinforcement Learning Problem 1 (12 marks) Re-implement (e.g. in Matlab or Python) the results presented in Figure 2.2 of the Sutton & Barto book comparing a greedy method with two -greedy methods (𝜀 = 0.01 and 𝜀 = 0.1), on the 10-armed testbed, and present your code and results. Include a
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Homework #2 Instructions: EECE 5550 Mobile Robotics Spring 2019 1. This homework covers topics from Lectures 1-8. 2. Due Date: Wednesday, February 13, 23:59. Please upload your work to your Dropbox folder. 3. No late homework will be accepted. Part A: Reading 1. Read this paper and be ready to answer questions during an in
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Optimization Methods in Finance ASSIGNMENT 1: Portfolio Optimization: Mean Variance and MAD DEADLINE: Thursday, 14 February 2019 @ 10pm (22:00) electronically via Learn January 31, 2019 This assignment follows on from the last lab session (week 3). Download the comma-separated value file all.csv from Learn. Have a look at it (for example with Excel): it
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Optimization Methods in Finance ASSIGNMENT 1: Portfolio Optimization: Mean Variance and MAD DEADLINE: Thursday, 14 February 2019 @ 10pm (22:00) electronically via Learn January 31, 2019 This assignment follows on from the last lab session (week 3). Download the comma-separated value file all.csv from Learn. Have a look at it (for example with Excel): it
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1 Basic Requirements 1. Programming language: Matlab (recommended and template codes provided), or other languages (without template codes) 2. Implement your code under Code folder, and read data from Data folder (one neg folder contains all the negative reviews and one pos folders contains all the positive ones). 3. Final project report under name report.pdf
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