# Signal Analysis: Time Segments & Characteristics

• Hzaqa
In summary, the problem is to analyze a signal in terms of time segments to understand its characteristics or patterns. The equations used are RMS, Wavlet, and FFT, and the question is which one of them can provide a better understanding of the system. The suggested solutions are creating a spectrogram for frequency analysis and experimenting with moving average plots for amplitude analysis or using the Lomb-Scargle Periodogram method to identify frequency components. The author also shares a MATLAB code for frequency and amplitude spectrum analysis, but is unsure which method is best.
Hzaqa

## Homework Statement

Hi ,
The problem is how to analysis this signal in terms of time segments to understnd the characteristics/pattern of this signal

## Homework Equations

There is no relevant equtions

## The Attempt at a Solution

I have tried to use :
RMS
Wavlet
FFT
But which one of them can give us a better understing of the system,

Thanks,

Your problem statement is fairly unconstrained so--

For frequency analysis, consider making a spectrogram, a 3 dimensional graph (frequency vs amplitude vs time).

For amplitude analysis, experiment with various moving average (RMS) plots.

Presumably you have a data set that corresponds to the graph, and not just the graph alone? You might want to investigate the use of the Lomb-Scargle Periodogram method to tease out the underlying frequency components of the signal.

Thanks for the response,

Well, I have applied frequency domain

Matlab code:

m=fft(x)
n=length(x)
power=abs(m(1:floor(n/2))).^2
nyquist=1/2
freq=((1:n/2)/(n/2)*nyquist)*5000
figure
plot(freq,power)
xlabel('Frequency (Hz)')
ylabel('Power')
title('Power Spectrum of Signal')
grid on

amp=abs(m(1:floor(n/2)))
figure
plot(freq,amp)
xlabel('Frequency (Hz)')
ylabel('Amplitude')
title('Amplitude Spectrum of Signal')
grid on

But I am not quite sure which one of them is best/correct,any suggestion,
Thanks

## 1. What is signal analysis?

Signal analysis is the process of extracting meaningful information from a signal. This can involve breaking down a signal into smaller time segments and analyzing its characteristics to better understand its behavior.

## 2. What are time segments in signal analysis?

Time segments refer to dividing a signal into smaller, discrete parts for analysis. This can provide insights into the behavior of the signal over time and help identify patterns or anomalies that may not be apparent when looking at the signal as a whole.

## 3. What are some of the characteristics that can be analyzed in a signal?

Some common characteristics that can be analyzed in a signal include amplitude, frequency, and phase. Other characteristics may also be relevant depending on the specific application and type of signal being analyzed.

## 4. How is signal analysis used in scientific research?

Signal analysis is used in a wide range of scientific research fields, such as neuroscience, engineering, and physics. It can help researchers understand the behavior of complex systems and identify patterns or correlations that may not be visible to the naked eye.

## 5. What techniques are commonly used for signal analysis?

There are many techniques that can be used for signal analysis, including Fourier analysis, wavelet analysis, and time-frequency analysis. Each technique has its own strengths and limitations, and the choice of technique will depend on the specific characteristics and goals of the signal analysis.

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