| Type: | Package |
| Date: | 2022-09-10 |
| Title: | A Self-Adaptive Approach for Demodulating Multi-Component Signal |
| Version: | 1.0.0 |
| Maintainer: | Shubhra Prakash <shubhraprakash279@gmail.com> |
| Description: | Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the 'Python' library 'PyLMD'. |
| License: | Apache License (≥ 2) |
| Depends: | R (≥ 3.6.0) |
| BugReports: | https://github.com/shubhra-opensource/LMD/issues |
| URL: | https://github.com/shubhra-opensource/LMD |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| Suggests: | knitr, rmarkdown, ggformula, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| Imports: | EMD, ggplot2, patchwork |
| NeedsCompilation: | no |
| Packaged: | 2022-09-20 02:27:01 UTC; shubh |
| Author: | Shubhra Prakash [trl, aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2022-09-20 09:56:07 UTC |
Extract Product Function
Description
Method for extracting product functions
Usage
extract_product_function(
signal,
max_envelope_iteration = 200,
envelope_epsilon = 0.01,
convergence_epsilon = 0.01
)
Arguments
signal |
Signal values (Numeric | vector) |
max_envelope_iteration |
Maximum number of iterations when separating local envelope signals (Integer) |
envelope_epsilon |
Terminate processing when obtaining pure FM signal (Double) |
convergence_epsilon |
Terminate processing when modulation signal converges (Double) |
Value
Product Function
Author(s)
Shubhra Prakash, shubhraprakash279@gmail.com
References
https://pypi.org/project/PyLMD/
Examples
x=1:100
y = (2 / 3 )* sin(x * 30) + (2 / 3) * sin(x * 17.5) + (4 / 5) *cos(x * 2)
plot(y,type="l")
pf=extract_product_function(y)
Find Extreme Points
Description
Method for finding Extreme Points
Usage
find_extrema(signal, include_endpoints = TRUE)
Arguments
signal |
Signal values (Numeric | vector) |
include_endpoints |
whether to include end points or not (Boolean) |
Details
A local extrema is the point at which a maximum or minimum value of the function in some open interval containing the point is obtained.
Value
Indexes of all extrema values (including starting and ending points)
Author(s)
Shubhra Prakash, shubhraprakash279@gmail.com
Examples
signal=c( 0.841471 ,0.9092974,0.14112,-0.7568025,-0.9589243)
find_extrema(signal)
Monotonicity Check
Description
Method for checking if signal is increasing or decreasing monotonously
Usage
is_monotonous(signal)
Arguments
signal |
Signal values (Numeric | vector) |
Details
A monotonic signal is a function that keeps increasing or decreasing as its domain variable proceeds.#'
Value
Boolean
Author(s)
Shubhra Prakash, shubhraprakash279@gmail.com
References
https://pypi.org/project/PyLMD/
Examples
x=1:100
is_monotonous(x)
Local Mean Decomposition
Description
Method for finding Product Functions (PFs)
Usage
lmd(
signal,
include_endpoints = TRUE,
max_smooth_iteration = 12,
max_envelope_iteration = 200,
envelope_epsilon = 0.01,
convergence_epsilon = 0.01,
max_num_pf = 8
)
Arguments
signal |
Signal values (Numeric | vector) |
include_endpoints |
Whether to treat the endpoint of the signal as a pseudo-extreme point (Boolean) |
max_smooth_iteration |
Maximum number of iterations of moving average algorithm (Integer) |
max_envelope_iteration |
Maximum number of iterations when separating local envelope signals (Integer) |
envelope_epsilon |
Terminate processing when obtaining pure FM signal (Double) |
convergence_epsilon |
Terminate processing when modulation signal converges (Double) |
max_num_pf |
The maximum number of PFs generated(Integer) |
Details
LMD is a method of decomposing signal into Product Functions (PFs) based on algorithm presented in Jonathan S. Smith. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5):443-454
Value
list(pf,residue) | PFs:The decompose functions arranged from high frequency to low frequency | residue:residual component
Author(s)
Shubhra Prakash, shubhraprakash279@gmail.com
References
https://pypi.org/project/PyLMD/
Examples
x=1:100
y = (2 / 3 )* sin(x * 30) + (2 / 3) * sin(x * 17.5) + (4 / 5) *cos(x * 2)
plot(y,type="l")
lmd(y)
Local Mean and Envelope
Description
Method for finding Local Mean and Envelope
Usage
local_mean_and_envelope(signal, extrema)
Arguments
signal |
Signal values (Numeric | vector) |
extrema |
indexes for extreme values |
Value
mean, envelope and smoothed mean and envelope values
Author(s)
Shubhra Prakash, shubhraprakash279@gmail.com
References
https://pypi.org/project/PyLMD/
Examples
signal = sin(1:10)
extrema = c(1 , 2, 5, 8, 10)
local_mean_and_envelope(signal, extrema)
Weighted Moving Average
Description
Weighted Moving Average Smoothing
Usage
moving_average_smooth(signal, window, max_smooth_iteration = 12)
Arguments
signal |
Signal values (Numeric | vector) |
window |
filter weights for smoothing (Numeric | vector) |
max_smooth_iteration |
Maximum number of iterations of moving average algorithm (Integer) |
Details
Weighted Moving Average Smoothing is used to smooth en the mean and envelope signal
Value
smooth signal
Author(s)
Shubhra Prakash, shubhraprakash279@gmail.com
References
https://pypi.org/project/PyLMD/
Examples
x=0:100
y = (2 / 3 )* sin(x * 30) + (2 / 3) * sin(x * 17.5) + (4 / 5) *cos(x * 2)
plot(y,type="l")
wma=moving_average_smooth(y,5)
plot(wma,type="l")
LMD Plot
Description
Method for plotting Product Functions (PFs) and Residue
Usage
plot_lmd(
lmd_obj,
max_pf = length(lmd_obj[["pf"]]),
show_residue = TRUE,
pricolor_plot = "midnightblue",
line_size_plot = 1
)
Arguments
lmd_obj |
LMD object created from LMD function |
max_pf |
Number of PFs to Plot |
show_residue |
Whether to plot residue or not |
pricolor_plot |
color of plots |
line_size_plot |
Size of line in ggplot |
Value
ggplot plot for Product Functions (PFs) and Residue
Author(s)
Shubhra Prakash, shubhraprakash279@gmail.com
Examples
x=1:100
y = (2 / 3 )* sin(x * 30) + (2 / 3) * sin(x * 17.5) + (4 / 5) *cos(x * 2)
plot_lmd(lmd(y))