suryakiranmg/EE483-Digital-Signal-Processing

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Created over 6 years ago
Updated over 6 years ago

suryakiranmg/EE483-Digital-Signal-Processing

suryakiranmg

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Dynamic-Movement-Primitives-and-Imitation-Learning-Robotics

Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DMPs and previously proposed building blocks is that each DMP is a nonlinear dynamical system. The basic idea is that you take a dynamical system with well specified, stable behavior and add another term that makes it follow some interesting trajectory as it goes about its business. The DMP differential equations (Transformation System, Canonical System, Non-linear Function) realize a general way of generating point-to-point movements. Imitation learning using linear regression is performed to compute the weight factor W from a demonstrated trajectory dataset, given by a teacher. The quality of the imitation is evaluated by comparing the training data with the data generated by the DMP.

Stochastic-Simulations-Proj7-Expectation-Maximization

Multivariate Gaussian distribution, Mixture Distribution, Expectation- Maximisation(EM) Algorithm on a 2D Gaussian Mixture Model(GMM), Comparison of quality and speed of GMM-EM estimates for different GMM distributionsn, K-means clustering routine, GMM-EM algorithm for pdf fitting

Stochastic-Simulations-Proj8-Markov-Chain-Monte-Carlo-Optimisation-Minimum-path

Stratification and importance sampling in Monte Carlo Estimation Simulation, Gibbs Sampling, Optimisation using Schwefel fuction - global minimum using simulated annealing, Simulation using exponential, polynomial and logarithmic colling schedule, Simulated annealing simulation to determine a minimal path between 48 US state capitals by road beginning from Sacramento, California.

Robotics-Project

Simulation-of-Image-Rejection-Filters-in-RF

Hartley and Weaver Image Rejection Filter Simulation

Kalman-vs-Butterworth-Filters

Comparison of a 2nd Order Butterworth Filter with a Kalman Filter in Data Filtering

Team-Project-in-Robotics--Balance-Step-Walk-tasks-on-NAO-Robot

Robotics Final Project : Balancing, Stepping and Walking Tasks on Nao Robot

RF--Harmonics-Intermodulation-Products-Calculator

The purpose of this mini project is to design a “calculator” of Harmonic and Intermodulation components that are generated due to non-linearities of the RF receiver (Particularly Amplifiers). It will accept 2 OR 3 input frequencies (in MHz) and generate the Harmonic and IM components separately as outputs. The repeated results both within and across orders are eliminated. Everything is summarized in a way that allows one to determine which transmitters are the most likely offenders. Phantom results are also eliminated.

Stochastic-Simulations-Proj4-Monte-Carlo-simulations-Chi-Square-Test-Interval-Estimation

Integral Approx using Monte-Carlo simulations, Chi-squared test on Empirical Distribution, Bootstrap sampling & Interval Estimate of 'Old faithful' geyser's geothermal discharge using data from 272 eruptions since 2000.