Suryakiran's repositories
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-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.
awesome-control-theory
Awesome resources for learning control theory
ml-cheatsheet
Machine learning cheatsheet
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.
Team-Project-in-Robotics--Balance-Step-Walk-tasks-on-NAO-Robot
Robotics Final Project : Balancing, Stepping and Walking Tasks on Nao Robot
4WD-Smart-Car-for-RaspberryPi
Project 1 : ADAS Features on 4-Wheel Drive Smart Car for Raspberry Pi Project 2 : LiDAR data classification using Pretrained Neural Net
Deep-Learning-Transfer-Learning-OpenCV
Project1 : Deep Learning - Flower Recognition using Transfer Learning Project2 : OpenCV - Highway Lane Detection on Camera Images & Video Project3 : Notes on DL & ML libraries, PyTorch
My-Projects-DoctoralCoursework
Tools : CUDA C, Multicore Programming, Batch Scripting, MATLAB
Coding_Prep
Coding Interview Prep Stuff
ece6460_project_ideas
Demos of some project ideas for ECE 6460
Hello-Python
Encounters with Python
Makeathon-SparkSC-Intelligent-Indoor-Hydroponic-Farm
Makeathon - SparkSC
MasteringRTOS
Running FreeRTOS on Arduino, STM32F4x and Cortex M based MCUs
modern-cpp-features
A cheatsheet of modern C++ language and library features.
Stochastic-Simulations-Proj1-Uniform-Distribution
Bernoulli Trials- Biased & Unbiased Coin Flips - Uniform Distribution
Stochastic-Simulations-Proj3-Poisson-Sample-with-Bernoulli-Approximation
Solutions to problems using Beroulli Trials, Poisson Distribution, Inverse transform method, Accept Reject Sampling and some Comparisons
tensorflow
An Open Source Machine Learning Framework for Everyone