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When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...

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Updated: May 1, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

MSigSeg: An R package for multiple signals segmentation.

Xuanyu Liu¹, Junbo Duan², Dian Gong³

¹Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Computer Methods and Programs in Biomedicine

|April 8, 2025

Summary

This summary is machine-generated.

A new R package, MSigSeg, efficiently detects common breakpoints in multiple biomedical signals. This tool addresses the challenge of signal heterogeneity, offering a practical solution for complex data analysis.

Keywords:

Breakpoints detection Fast optimization algorithm R Segmentation ℓ-0 norm

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Area of Science:

Biomedical data analysis
Signal processing
Computational biology

Background:

Identifying signal breakpoints is key to scientific discovery.
Biomedical signals are often heterogeneous, complicating breakpoint detection.
Existing methods primarily focus on single signals, not common breakpoints across multiple signals.

Purpose of the Study:

To develop a fast and optimal method for detecting common breakpoints in multiple signals.
To implement this method in a user-friendly R package (MSigSeg).

Main Methods:

Utilized an optimization approach with an ℓ-0 norm penalty for accurate common breakpoint detection.
Developed a fast optimization algorithm implemented within the MSigSeg R package.
Provided detailed mathematical descriptions and usage examples for core functions.

Main Results:

Simulation studies demonstrated the method's performance compared to existing segmentation approaches.
Real-world biomedical problems were analyzed, showcasing the practical utility of MSigSeg.
Significant efficiency gains were observed in the detection of common breakpoints.

Conclusions:

The MSigSeg R package offers an efficient and sensitive method for identifying common breakpoints in multiple signals.
It serves as a valuable resource for analyzing complex biomedical data.
The package is publicly available on CRAN.