Results for random-sensor-values

A simple module that generates random sensor values (temperature, humidity) and random resources (iron ore, gold, etc.) for simulation purposes.

Use the random function in CSS

A module for generating random sensor values

A small implementation of `crypto.getRandomValues` for React Native. This is useful to polyfill for libraries like [uuid](https://www.npmjs.com/package/uuid) that depend on it.

`window.crypto.getRandomValues` with fallback to Node.js crypto

A custom module for generating random sensor values and simulating resource categories for spacecraft control.

DOM element size sensor which will callback when size changed.

A simple module to generate random sensor values for spacecraft

Measures patterns of attribute values associated with features. Reveals whether similar values tend to occur near each other, or whether high or low values are interspersed

Statistical routines and probability distributions.

TypeScript definitions for react-native-get-random-values

Generate a random integer

Sensor component for React that notifies you when it goes in or out of the window viewport.

A functional typescript implementation of the PCG family random number generators

A function to parse floating point hexadecimal strings as defined by the WebAssembly specification

Generate random numbers from various distributions.

TypeScript definitions for d3-random

Fastest random ID and random string generation for Node.js

RFC9562 UUIDs

A mathematically correct random number generator library for JavaScript.

Universal Module for Secure Random Generator in JavaScript

A React Native wrapper to check whether a component is in the view port to track impressions and clicks

URL and cookie safe UIDs

[](https://npm.im/@dnd-kit/abstract)

Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for all place limits on how well it is possible to determine the system's state. The Kalman filter deals effectively with the uncertainty due to noisy sensor data and to some extent also with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average. The purpose of the weights is that values with better (i.e., smaller) estimated uncertainty are "trusted" more. The weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system's state. The result of the weighted average is a new state estimate that lies between the predicted and measured state, and has a better estimated uncertainty than either alone. This process is repeated at every time step, with the new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only the last "best guess", rather than the entire history, of a system's state to calculate a new state.