similar to: Fourier / Bandpass filter help?

Greetings all, I'm working on a project trying to apply fourier analysis to timestamped router logs, using R to perform the analysis. The idea is to determine if any type of traffic (say, outgoing ICMP requests) has strong periodic features because it may indicate a compromise somewhere on the network. The FFT requires all data points to be evenly spaced, but the recorded events do not

Dear list, What on earth is spec.pgram() normalized too? If you would like to skip my proof as to why it's not normed too the mean squared or sum squared amplitude of the discrete function a[], feel free too skip the rest of the message. If it is, but you know why it's not exact in spec.pgram() when it should be, skip the rest of this message. The issue I refer herein refers only too a

Dear Tom, Short answer, if your using spec.pgram(), use the smoothing kernel to get a better estimate at the frequency centered in the bandwidth. If your frequency bin of interest is wider than the bandwidth of the kernel, average across frequencies (I think). The estimate appears to be normalized already. If you are calculating your PSD independently, then oversample (e.g. 2, perhaps 4 or more

I have done a fair bit of spectral analysis, and hadn't finished collecting my thoughts for a reply, so hadn't replied yet. What exactly do you mean by normalize? I have not used the functons periodogram or spectrum, however from the description for periodogram it appears that it returns the spectral density, which is already normalized by frequency, so you don't have to worry about

Hello List, I am working on creating periodograms from IP network traffic logs using the Fast Fourier Transform. The FFT requires all the data points to be evenly-spaced in the time domain (constant delta-T), so I have a step where I zero-pad the data. Lately I've been wondering if there is a faster way to do this. Here's what I've got: * data1 is a data frame consisting of

Dear Prof Ripley, First of all, unless you are an english professor, then I do not think you have any business policing language. I'm still very much a student, both in R, and regarding signal analysis. My competence on the subject as compared too your own level of expertise, or my spelling for that matter, may be a contension for you, but it would have been better had you kept that opinion

I'm doing convolutions in the frequency domain and need to know the layout of the Fourier modes returned by fft. (This is leading up to a more involved question about moment generating functions, but I need to know if I've got this part correct first.) I think in 1D the pattern is: 0 1 2 3 -2 1 (even) 0 1 2 3 -3 2 1 (odd) In 2D is it simply (for a square matrix): 0 1 2 -1 (horizontal)

well if you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will

Dear R Helpers, Is there any literaure available (including R code) on Fast Fourier Transform being used in CreditRisk+? I need to learn how to apply the Fast Fourier Transform. I agree I am too vaue in my question and sincerely apologize for the same, but I am not able to understand as to where do I start for this particular assignment. I tried to search google for CRAN and Fast Fourier

Hi, here is a practical problem we would like to solve. In a pneumatic post the acceleration of the capsule is measured and plotted over time. From the graph achieved we would like to derive some kind of statistic value that describes the stress the capsule, or what is in it, is exhibited to. The amount of stress introduced to the capsule will probably depend on two things, the maximum

Dear listers In R, if I have a sequence x(t), t=1,...N, fft(x) is actually giving us sum(x(t)exp(-i*omega*t)) at fourier frequency omega= 2*pi*i/N, i=0,1,...(N-1). The question is if I want to calculate sum(x(t)exp(-i*2*omega*t)), how can I do it? thanks a lot! ------------------------------- liyun (Lauren) Ma Dept of Statistics North Carolina State University

Hello world, I am actually trying to transfer a lecture from Statistica to R and I ran into problems with spectral analysis, I think I just don't get it 8-( (The posting from "FFT, frequs, magnitudes, phases" from 2005 did not enlighten me) As a starter for the students I have a 10year data set of air temperature with daily values and I try to get a periodogram where the annual

Hello, I have a series and I need to run a Fourier Transform for that series. I have done that using the function fft from stats package. However I am not sure whether the result I am getting is correct or not. Seems that the first value of the Fourier Transform list is the sum of all elements of the given series and all the following numbers represent a palindromic series for some how. I have

Hello All, I wanted to perform a fourier transform on high frequency financial data. I have searched and have not found much on this topic for R. I was wondering if anyone has used any libraries for it or have come across any papers I may read. Many Thanks, Neil Gupta [[alternative HTML version deleted]]

Greetings all, I am using Fourier analysis to search for periodicities in IP network traffic by generating periodograms and then visually examining them for large, distinct peaks. However, in many cases it is not readily apparent where there are periodicities. I have no experience with discrete maths so I've come up against a block here: How do I define what the "noise floor"

I did a fourier transform on a function in time domain to get the following functions in frequency domain (in latex): $Y_1[\omega] = \frac{1}{1-\phi_1 e^{-jw}}$ $Y_2[\omega] = \frac{1}{1-(\phi_1 + \phi_2)e^{-jw} +\phi_1\phi_2e^{-2jw}}$ How do I find the spectrum of this function for given $\phi_1$ and $\phi_2$ coefficients and in the discretization interval $w = [-\pi:.1*\pi: \pi]$? Then, how

I wrote a script that I anticipating seeing a spike at 10Hz with the function 10* sin(2*pi*10*t). I can't figure out why my plots do not show spikes at the frequencies I expect. Am I doing something wrong or is my expectations wrong? require(stats) layout(matrix(c(1,2,3), 3, 1, byrow = TRUE)) #SETUP n <- 256 #nextn(1001) gives next power 2 F <- 100 #Hz -50 to 50 Hz dt

Hi, does anybody have any ideas regarding the easiest and most efficient way of implementing a bandpass filter in R ? any help would be truly appreciated. cheers, Pat Johnston -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Dear R-help members, To whom it may concern, our research group is conducting a study to evaluate the predictive value of 24 hour blood pressure variability. We are looking for an R routine that performs a fast Fourier transform spectral analysis (with an output of the approximation function of the Fourier and estimates the validity of the model for the various harmonics). Thanks Vittorio

Dear R helpers,   Following is a R script I am using to run the Fast Fourier Transform. The csv files has 10 columns with titles m1, m2, m3 .....m10.     When I use the following commands, I am getting the required results. The probelm is if there are 100 columns, it is not wise to define 100 commands as fk <- ONS$mk and so on. Thus, I need some guidance to write the loop for the STEP A and