Experiments with Homeopathic medicine Sulphur 200c
The experiment was conducted with 6 subjects,Left side graphs are of sulphur 200c given subjects,
Right are of placebo given subjects.
In brief : per day two subjects were selected. 9 pills of sulphur 200c was given to one subject and 9 pills of placebo given to another subject. Same procedure was followed for all the four days but with different subjects.
Readings were taken from 8am to 8.10am, except on the day 2nd it was taken from 08.00.16 am to 08.10 am. It can observe the shift in peak in medium (yellow) frequency in the picture (2nd day 1st graph).Click on the picture to maximize
The readings were taken at an interval of 1sec in contrary to the Gelsemium 200c readings 2sec interval. (For more details on experiment particulars please contact me at muntadev2in@yahoo.co.in here all the details are not published because of space limitations)
Kumar,
ReplyDeleteYour graphs on this make no sense at all. They show "Frequency Hz" on one axis and some other measure on the other axis. However what you you measured was temperature versus time. So the axes of the graphs should at least be "Temperature" and "Time" in some way.
In reality you have just produced a mass of totally meaningless numbers, probably out of a lab instrument you have no idea how to use. Consequently, your results simply do not prove what you claim they do.
Perhaps you might like to show us your base data, and we can see if there is any REAL pattern to it?
Do you know what is ment by AR Spectrum and FFT, how to obtain ar and fft?, I think you came from james randi forum, most of the people in JREF are unscientific, they are just mazicians.
ReplyDeleteYes, I do know about Fast Fourier Transforms, etc. Your graphs still make no sense at all w.r.t FFT. They do not show what you think they show. They are quite meaningless numbers right now.
ReplyDeletePerhaps you might like to explain here why you chose to use esoteric Fourier transforms for analyzing really the basic measuring of temperatures over time intervals.
And no, I am not from the James Randi forum. I'm discussing real science with you, not "mazic".
Please stick to your own topic and answer the questions.
do you like to suggest any other statistical measure other than spectral analysis to study variability data?
ReplyDeleteI am not your schoolmaster to correct your analysis work or suggest alternatives. If you wish to run in the science game and make these hypotheses, it is YOUR responsibility alone to explain and justify clearly your own protocol and data analysis. So far you have not done so.
ReplyDeleteThe only "information" we have learned from you so far is that you seem unable to explain and justify your own protocol, and you don't appear to understand what you are doing with your own data analysis. But you can certainly dispel that impression by giving us fuller explanations of these topics.
So I repeat: Please explain here why YOU chose to use esoteric Fourier transforms for analyzing what is a really basic measurement of temperatures over time intervals.
Please let me know your background(accodomic) so I will be known what are the basic explanations you need. also please come out of anonymous postings.
ReplyDeleteYou can take it as given I have FAR more than sufficient academic qualifications to understand anything you can provide. Because so far you have explained nothing at all, which even a child is qualified to understand.
ReplyDeletePlease stop dodging the issue, sir, and explain here why YOU chose to use esoteric Fourier transforms for analyzing what is a really basic measurement of temperatures over time intervals.
There will be more questions, but they will depend on your answers to this first question.
Please provide an answer soon, or I will begin to assume you don't actually understand your own research.
Thank you
Well, it's been 4 days now, and you have not responded in any way. Why is this? I am waiting patiently for you to provide your reasons why you chose to use FFT's on your data, when it was absolutely meaningless to do so from a research angle.
ReplyDeleteSo if you can't provide a response for that, perhaps you would like an easier request? OK, here's a simpler one to be going on with: If you are measuring *temperatures*, why are you posting oscilloscope displays showing nice regular sine waves of fixed frequencies? Human temperature is not a frequency-based sinusoidal measurement, by and large.
Is that any easier for you to explain? Add it to the list of requests, please.
do you want original readings? let me know please.
ReplyDeleteI have converted time domain variability into frequency domain with AR.
parametric reconstruction based on AR model is well known mothod in stats to predict rythmnic behaviour in time series varibility, is'nt it?
ReplyDeleteTime domain frequency variation analysis only makes sense and produces useful information if the base data is actually cyclic in some way and it relates to the base data.
ReplyDeleteYour first problem is that your base data is "temperature" and "time". But your FFT graphs do not relate to these measurements in any way at all. So again, why did you choose to do these FFT analyses? You have not made a sensible case for doing so.
Your second problem is that if we look at the scope displays showing your base measurements, they show a cycle of some dozens per second, and that there is a second signal that cycles hundreds per second. For temperature measurements in humans, do you think those are sensible input?
So let me introduce a third issue for your list: Why are the temperatures you measure consistently lower than normal human temperatures? Humans will maintain a constant core temperature until extreme cold for long periods starts to drag it down. Such as sitting in snow for some hours. Your data points suggest this was indeed happening to your test subjects. Would you care to explain why this might be?
1.I have measured variability in human skin temperature not the oral so there will be 5-6^0 variation in value.
ReplyDelete2.The Parametric Interpolation and Prediction is a powerful composite algorithm that generates a parametric (sinusoids or damped sinusoids) model of the signal.
The algorithm has three stages. In the first stage, an AR, Prony, Eigenanalysis, or Fourier procedure is used to estimate the frequencies and component count.
In the second stage a linear fit is made to determine the amplitudes and phases. These are the starting estimates for the third stage, the non-linear optimization.
While it is possible to accomplish these same steps from any of the spectral procedures, this option combines all of the steps into a single integrated procedure designed specifically for interpolation and prediction.
This presents a dual graph with a frequency domain bar plot in the upper graph. The non-linear optimization is shown in the lower graph.
look@
http://homeoresearch.blogspot.com/2009/11/hoarseness-voice-case.html
Frequency-Amplitude Bar Plot
The upper graph is a parametric amplitude spectrum that uses a bar plot. There is a single bar for each component fitted.
Non-Linear Optimization Plot
The following is the non-linear optimization graph for data consisting of three sinusoids and noise. The three component functions are shown in the Y-axis plot. The Y2 plot contains the fitted curve and the data that were fitted
The non-linear optimization graph offers the option of displaying Confidence and Prediction Intervals about the fitted curve.
Algorithm
The estimation of component count and frequencies can be done by
•AR,Data FB - Autoregressive modeling. The Data Svd FB algorithm in the AR(AutoRegressive) Spectrum.
Energy spectral density:
ReplyDeleteThe energy spectral density describes how the energy (or variance) of a signal or a time series is distributed with frequency. If f(t) is a finite-energy (square integrable) signal, the spectral density Φ(ω) of the signal is the square of the magnitude of the continuous Fourier transform of the signal (here energy is taken as the integral of the square of a signal, which is the same as physical energy if the signal is a voltage applied to a 1-ohm load).
where ω is the angular frequency (2π times the ordinary frequency) and F(ω) is the continuous Fourier transform of f(t), and F * (ω) is its complex conjugate.
If the signal is discrete with values fn, over an infinite number of elements, we still have an energy spectral density:
where F(ω) is the discrete-time Fourier transform of fn.
If the number of defined values is finite, the sequence does not have an energy spectral density per se, but the sequence can be treated as periodic, using a discrete Fourier transform to make a discrete spectrum, or it can be extended with zeros and a spectral density can be computed as in the infinite-sequence case.
The continuous and discrete spectral densities are often denoted with the same symbols, as above, though their dimensions and units differ; the continuous case has a time-squared factor that the discrete case does not have. They can be made to have equal dimensions and units by measuring time in units of sample intervals or by scaling the discrete case to the desired time units.
As is always the case, the multiplicative factor of 1 / 2π is not absolute, but rather depends on the particular normalizing constants used in the definition of the various Fourier transforms.