ricmagno Goto Github PK

cvat

Powerful and efficient Computer Vision Annotation Tool (CVAT)

esp-homekit-sdk

git-github.com-ricmagno-carpark

gitignore

A collection of useful .gitignore templates

kb_lets_split

Let's split keyboard configuration and firmware

kb_olkb

Custom layout of my Massdrop OLKB keyboard (Preonic Rev3)

kinesis

lotto_nz

AI approach to NZ lotto

mobile_app_webscap

These collect app information from IOS App Store (through fnd.io) and Google Play Store from an list of apps.

models

Models and examples built with TensorFlow

nqptp

Not Quite PTP

opc-ua_unity

Unity Project of an OPC UA Sample Client. Designed to be used with a Microsoft HoloLens

pdf_info_read

Reads text from pdf.

pi-hole_blacklist

My personal blacklist of annoying ads

project_management

Project Management org templates

replace-colorthemes

Replace color-theme with Emacs theme framework

safety_equipment

Safety equipment on construction

shairport-sync

AirPlay and AirPlay 2 audio player

spectacle

Spectacle allows you to organize your windows without using a mouse.

tikz

tikz images

transfer-function-estimation-using-time-frequency-analysis

Given limited and noisy data, identifying the transfer function of a complex aerospace system may prove difficult. In order to obtain a clean transfer function estimate despite noisy data, a time-frequency analysis approach to system identification has been developed. The method is based on the observation that for a linear system, an input at a given frequency should result in a response at the same frequency, and a time localized frequency input should result in a response that is nearby in time to the input. Using these principles, the noise in the response can be separated from the physical dynamics. In addition, the impulse response of the system can be restricted to be causal and of limited duration, thereby reducing the number of degrees of freedom in the estimation problem. The estimation method consists of finding a rough estimate of the impulse response from the sampled input and output data. The impulse response estimate is then transformed to a two dimensional time-frequency mapping. The mapping provides a clear graphical method for distinguishing the noise from the system dynamics. The information believed to correspond to noise is discarded and a cleaner estimate of the impulse response is obtained from the remaining information. The new impulse response estimate is then used to obtain the transfer function estimate. The results indicate that the time-frequency transfer function estimation method can provide estimates that are often less noisy than those obtained from other methods such as the Empirical Transfer Function Estimate and Welch's Averaged Periodogram Method.

what-if-tool

Source code/webpage/demos for the What-If Tool