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Related Concept Videos

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...
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...
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
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...

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Related Experiment Video

Updated: May 30, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Spatiotemporal sampling of dynamic environment sequences.

Liang Wan1, Shue-Kwan Mak, Tien-Tsin Wong

  • 1School of Computer Software, Tianjin University, Weijin Road 92, Nankai District, Tianjin, PR China. lwan@tju.edu.cn

IEEE Transactions on Visualization and Computer Graphics
|August 6, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces spatiotemporal sampling to improve animation consistency with dynamic environment illumination. The novel method ensures smoother, temporally coherent animations by adaptively sampling environment sequences.

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Area of Science:

  • Computer Graphics
  • Image Synthesis
  • Rendering Techniques

Background:

  • Environment sampling is crucial for realistic scene illumination from distant sources.
  • Temporal consistency in animations under dynamic environment sequences remains a significant challenge.
  • Existing methods lack comprehensive exploitation of both spatial and temporal coherence in environment sequences.

Purpose of the Study:

  • To address the lack of temporal consistency in animations rendered with dynamic environment sequences.
  • To propose a novel spatiotemporal sampling method that leverages both temporal and spatial coherence.
  • To enhance the quality and consistency of synthesized animations.

Main Methods:

  • Treating environment sequences as spatiotemporal volumes for adaptive stratification.
  • Developing a new metric to evaluate the importance of stratified volume elements.
  • Implementing a stratification algorithm to minimize abrupt temporal and spatial changes in sampling patterns.

Main Results:

  • The proposed method adaptively adjusts sample counts per environment frame.
  • Generated sampling patterns exhibit enhanced temporal coherence.
  • Comparative experiments confirm the production of smooth and consistent animations.

Conclusions:

  • Spatiotemporal sampling effectively exploits temporal and spatial coherence in environment sequences.
  • The method offers automatic adjustment of sampling for consistent animation.
  • This approach significantly improves the temporal consistency of rendered animations.