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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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Updated: Feb 24, 2026

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Microwave breast cancer detection using time-frequency representations.

Hongchao Song¹, Yunpeng Li², Aidong Men³

¹School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China. shch@bupt.edu.cn.

Medical & Biological Engineering & Computing

|August 25, 2017

Summary

This summary is machine-generated.

This study introduces advanced microwave breast cancer detection using time-frequency analysis, improving accuracy over traditional methods. New feature extraction techniques enhance tumor detection robustness, especially with signal misalignment.

Keywords:

Empirical mode decomposition Feature extraction Microwave breast cancer detection Wavelet transform

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Last Updated: Feb 24, 2026

Clinical Imaging of Microwave Mammography

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Published on: November 14, 2025

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

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Published on: April 5, 2020

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

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Published on: December 15, 2014

Area of Science:

Biomedical Engineering
Medical Imaging
Machine Learning

Background:

Microwave-based breast cancer detection offers a complementary approach to existing techniques.
Machine learning algorithms are increasingly popular for detecting breast tumors, focusing on existence rather than precise localization.
Principle Component Analysis (PCA) is a common feature extraction method but is sensitive to signal misalignment.

Purpose of the Study:

To propose novel feature extraction methods for microwave breast cancer detection.
To enhance the robustness of detection against signal misalignment.
To improve the performance of machine learning-based breast cancer detection systems.

Main Methods:

Feature extraction using time-frequency representations: Wavelet Transform (WT) and Empirical Mode Decomposition (EMD).
Generation of time-invariant statistics from decomposition results.
Validation using clinical datasets and simulated tumor responses.
Ensemble selection-based classifier integration.

Main Results:

Features extracted from WT and EMD decomposition demonstrate improved detection performance.
The proposed methods show increased robustness to data misalignment compared to PCA.
Enhanced detection accuracy when combined with an ensemble selection classifier.

Conclusions:

Time-frequency representations combined with time-invariant statistics offer robust feature extraction for microwave breast cancer detection.
The proposed methods significantly improve detection performance, particularly in the presence of signal misalignment.
This approach enhances the reliability and accuracy of machine learning-based breast cancer detection systems.