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Related Concept Videos

Upsampling01:22

Upsampling

277
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
277
Sampling Methods: Overview01:06

Sampling Methods: Overview

397
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...
397
Downsampling01:20

Downsampling

215
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
215
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

320
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...
320
Sampling Plans01:23

Sampling Plans

232
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...
232
Aliasing01:18

Aliasing

181
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
181

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Towards real-time STEM simulations through targeted subsampling strategies.

Alex W Robinson1, Jack Wells2, Daniel Nicholls1

  • 1Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.

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Summary
This summary is machine-generated.

This study introduces an efficient method for scanning transmission electron microscopy (STEM) simulations, significantly reducing computation time. The approach enhances simulation speed for materials like SrTiO3 and MoS2, paving the way for self-driving STEM.

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Area of Science:

  • Materials Science
  • Computational Physics
  • Electron Microscopy

Background:

  • Scanning transmission electron microscopy (STEM) image interpretation is challenging due to contrast sensitivity to various factors.
  • STEM simulations are crucial for image validation but are computationally intensive, requiring significant time or specialized hardware.
  • Compressive sensing techniques have shown promise in reducing data acquisition in experimental STEM.

Purpose of the Study:

  • To develop a more efficient method for STEM simulations.
  • To explore the application of compressive sensing principles to STEM simulations.
  • To accelerate the interpretation and validation of atomic-scale STEM data.

Main Methods:

  • Implemented a targeted sampling strategy for STEM simulations.
  • Introduced independent subsampling for frozen phonon layers.
  • Applied the method to simulate SrTiO3 grain boundaries and MoS2 monolayers using abTEM software.

Main Results:

  • Demonstrated a significant increase in STEM simulation efficiency.
  • Successfully simulated complex material structures, including defects like sulfur vacancies in MoS2.
  • Showed applicability to both traditional multislice and PRISM simulation methods.

Conclusions:

  • The proposed method substantially enhances STEM simulation speed and efficiency.
  • This approach can be applied to various materials and simulation techniques.
  • Future work may involve using simulations to guide real-time STEM data acquisition for self-correcting microscopy.