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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

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Related Experiment Video

Updated: Jun 19, 2026

VacuSIP, an Improved InEx Method for In Situ Measurement of Particulate and Dissolved Compounds Processed by Active Suspension Feeders
08:57

VacuSIP, an Improved InEx Method for In Situ Measurement of Particulate and Dissolved Compounds Processed by Active Suspension Feeders

Published on: August 3, 2016

Implicit sampling for particle filters.

Alexandre J Chorin1, Xuemin Tu

  • 1Department of Mathematics, University of California and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. chorin@math.berkeley.edu

Proceedings of the National Academy of Sciences of the United States of America
|October 7, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient particle-based nonlinear filtering method using Gaussian variables and resampling. The novel approach sharpens particle paths, significantly reducing the number of particles needed for accurate probability density function estimation.

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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Last Updated: Jun 19, 2026

VacuSIP, an Improved InEx Method for In Situ Measurement of Particulate and Dissolved Compounds Processed by Active Suspension Feeders
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VacuSIP, an Improved InEx Method for In Situ Measurement of Particulate and Dissolved Compounds Processed by Active Suspension Feeders

Published on: August 3, 2016

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

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Area of Science:

  • Computational statistics
  • Nonlinear filtering
  • Particle methods

Background:

  • Particle-based nonlinear filtering is crucial for estimating probability density functions in complex systems.
  • Existing methods often require a large number of particles, increasing computational cost.
  • Chainless Monte Carlo methods offer alternative approaches to particle filtering.

Purpose of the Study:

  • To develop a novel particle-based nonlinear filtering scheme.
  • To improve the efficiency of particle filtering by reducing particle requirements.
  • To enhance the accuracy of probability density function estimation.

Main Methods:

  • A particle-based nonlinear filtering scheme is proposed.
  • Each probability density function is represented by a set of Gaussian variable functions.
  • Resampling is performed using normalization factors and Jacobians.

Main Results:

  • The proposed scheme focuses particle paths sharply.
  • Fewer particles are required for accurate estimation.
  • The method is demonstrated on an ill-conditioned test problem.

Conclusions:

  • The novel filtering scheme offers improved efficiency and accuracy.
  • The method effectively reduces particle count in nonlinear filtering.
  • This approach shows promise for complex estimation problems.