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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...

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Related Experiment Video

Updated: May 18, 2026

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

Optimized compressive sampling for passive millimeter-wave imaging.

Leonidas Spinoulas¹, Jin Qi, Aggelos K Katsaggelos

¹Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois 60208, USA. leonidasspinoulas2015@u.northwestern.edu

|September 13, 2012

Summary

This summary is machine-generated.

This study presents a single-detector passive millimeter-wave imaging system using a cyclic sensing matrix. An adaptive sampling strategy improves image reconstruction, demonstrating enhanced system performance for millimeter-wave imaging.

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Clinical Imaging of Microwave Mammography

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Published on: November 14, 2025

Area of Science:

Electromagnetic Wave Imaging
Signal Processing
Computational Imaging

Background:

Passive millimeter-wave (PMMW) imaging offers all-weather, non-ionizing capabilities.
Previous systems often require complex mechanical components for mask switching.
Bayesian reconstruction methods are effective but sensitive to sampling strategies.

Purpose of the Study:

To introduce an improved sampling scheme for a single-detector PMMW imaging system.
To enhance image reconstruction quality by adaptively selecting submasks.
To validate the feasibility and performance gains of the proposed method.

Main Methods:

Utilized a previously presented single-detector PMMW imaging system with a cyclic sensing matrix.
Acquired incoherent measurements of the scene using a unified, compact mask.
Implemented an optimal adaptive selection of sampling submasks from the full cyclic mask.
Reconstructed images using a Bayesian approach.

Main Results:

Demonstrated the feasibility of the single-detector PMMW imaging system.
Achieved improved image reconstruction results through the adaptive sampling scheme.
The unified mask design eliminated the need for mechanical mask exchange.

Conclusions:

The proposed adaptive sampling strategy significantly enhances the performance of the PMMW imaging system.
The cyclic sensing matrix and adaptive submask selection offer a compact and efficient imaging solution.
This approach advances the practical application of single-detector PMMW imaging technology.