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Upsampling

Upsampling

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...

Bandpass Sampling

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...

Aliasing

Aliasing

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 signal...

Sampling Theorem

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.

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

Sampling Methods: Overview

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.
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Related Experiment Video

Updated: Jun 16, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Improving the performance of the signal space separation method by comprehensive spatial sampling.

J Nurminen¹, S Taulu, Y Okada

¹BioMag laboratory, Hospital District of Helsinki and Uusimaa HUSLAB, Helsinki University Central Hospital, PL 340, FI-00029 HUS, Finland. jnu@iki.fi

Physics in Medicine and Biology

|February 17, 2010

Summary

Novel sensor array designs using vector sensors can significantly improve biomagnetic instrument performance. These new configurations enhance signal space separation (SSS) for better interference shielding and reduced noise.

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Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

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Last Updated: Jun 16, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Area of Science:

Biophysics
Biomagnetism
Sensor Technology

Background:

Traditional biomagnetic instruments use radially oriented sensors on a near-spherical surface.
Current signal space separation (SSS) methods face limitations with these symmetric sensor designs.
Excessive symmetry hinders the separation of internal and external magnetic field components.

Purpose of the Study:

To investigate novel sensor array designs for improved biomagnetic measurements.
To overcome limitations of traditional sensor arrays in signal space separation (SSS).
To enhance shielding against external interference and reduce noise in reconstructed signals.

Main Methods:

Simulated evaluation of sensor arrays employing vector sensors in one or two layers.
Analysis of sensor arrays providing information on multiple field components and radial dependence.
Comparison with traditional reference array geometries.

Main Results:

Novel two-layer vector sensor arrays significantly improve SSS performance.
The best evaluated two-layer array achieved a shielding factor of 60 dB (1000x) with ~400 sensors.
Traditional sensor arrays showed limited improvement due to restricted spatial coverage.

Conclusions:

Vector sensor arrays offer a pathway to superior biomagnetic measurement capabilities.
Enhanced SSS performance leads to improved noise reduction and interference shielding.
Advanced sensor designs increase the information obtainable from magnetic field measurements.