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Standing Waves in a Cavity

Standing Waves in a Cavity

A household microwave and lasers are examples of standing electromagnetic waves in a cavity. When two conducting metal plates are placed parallel at the nodal planes, it creates a cavity where standing waves are formed. The cavity between the two planes is analogous to a stretched string held at the points x = 0 and x = L. Here, the distance 'L' between the two planes must be an integer multiple of half of the wavelength. The wavelengths that satisfy this condition are given by:

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Related Experiment Video

Updated: Jan 10, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Breast density classification using frequency-based features in microwave imaging.

Mehran Taghipour-Gorjikolaie¹, Banafsheh Khalesi², Bilal Khalid³

¹School of Engineering, London South Bank University, London, UK. mehran.taghipour-gorjikolaie@lsbu.ac.uk.

Scientific Reports

|November 27, 2025

Summary

This summary is machine-generated.

Microwave imaging accurately classifies breast density, improving early breast cancer detection. This safe, non-ionizing radiation technology enhances diagnostic accuracy for all women, especially younger ones.

Keywords:

Breast Cancer Breast Density Classification Frequency-based Features Machine Learning Microwave Imaging

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Last Updated: Jan 10, 2026

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Published on: December 15, 2014

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Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

Published on: April 5, 2020

Area of Science:

Biomedical Engineering
Medical Imaging
Radiology

Background:

Breast density is a critical factor in breast cancer detection accuracy.
High breast density can obscure tumors and increase diagnostic errors.
Current screening methods pose challenges for younger women due to radiation concerns.

Purpose of the Study:

To evaluate microwave imaging for accurate breast density classification.
To assess the potential of microwave imaging in improving breast cancer detection.
To offer a safe, non-ionizing radiation alternative for breast screening.

Main Methods:

Utilized a microwave imaging prototype (MammoWave) scanning breasts with low-power electromagnetic signals.
Extracted frequency-based features from signal magnitude and phase using Fast Fourier Transform (FFT).
Employed a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel for classification.

Main Results:

Achieved ~70% accuracy in breast density classification using fused magnitude and phase features.
Improved accuracy to ~73% by selecting specific microwave frequency sub-bands (1-6 GHz).
Demonstrated that incorporating breast density classification significantly enhances machine learning model performance for cancer detection.

Conclusions:

Microwave imaging is a promising technology for safe and accurate breast density classification.
Personalized screening based on breast density can improve breast cancer detection rates.
This technology offers a viable alternative for women of all ages, including younger demographics.