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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

336
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...
336
Sampling Methods: Overview01:06

Sampling Methods: Overview

477
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...
477
Aliasing01:18

Aliasing

200
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...
200
Sampling Theorem01:15

Sampling Theorem

726
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.
726
Upsampling01:22

Upsampling

296
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...
296
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

358
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...
358

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Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning.

Fabian Böhm1, Diego Alonso-Urquijo2, Guy Verschaffelt2

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Analog Ising machines can now perform ultrafast statistical sampling for machine learning by injecting noise. This breakthrough enables efficient neural network training and optimization, surpassing traditional digital computers in speed.

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Area of Science:

  • Physics
  • Computer Science
  • Machine Learning

Background:

  • Ising machines offer a novel non-von-Neumann approach for computation, particularly in neural network training and combinatorial optimization.
  • Current analog Ising machines struggle with efficient statistical sampling, limiting their effectiveness for training neural networks compared to digital methods.

Purpose of the Study:

  • To introduce a universal method for achieving ultrafast statistical sampling in analog Ising machines.
  • To demonstrate the application of this method for accurate Boltzmann distribution sampling and unsupervised neural network training.

Main Methods:

  • Development of a noise injection technique for analog Ising machines.
  • Experimental implementation using an opto-electronic Ising machine.
  • Comparative analysis with software-based training methods and simulations.

Main Results:

  • Demonstrated accurate sampling of Boltzmann distributions and unsupervised neural network training with an opto-electronic Ising machine.
  • Simulations indicate that Ising machines can achieve statistical sampling speeds orders of magnitude faster than software methods.
  • Achieved comparable accuracy to software-based training for neural networks.

Conclusions:

  • Noise injection enables ultrafast statistical sampling in analog Ising machines, overcoming previous limitations.
  • Ising machines are now viable and efficient tools for machine learning applications beyond combinatorial optimization.
  • This advancement broadens the applicability of Ising machines in fields requiring rapid statistical sampling.