Radio Frequency Distortion Part II: A power spectrum model
Summary
This article presents a ready-to-use model for nonlinear distortion caused by radio frequenfcy components in wireless receivers and linear transmitters. Compared to the similar model presented in my earlier blog entry, it operates on expectation values of the the power spectrum instead of the signal itself: Use the signal-based model to generate distortion on a signal, and the one from this article to directly obtain the power spectrum much more efficiently.In...
Accelerating Matlab DSP Code on the GPU
Intrigued by GPUs, I've spent a few days testing out Jacket, an interface that lets you accelerate MATLAB (my favorite, if frustrating language) on NVIDIA GPUs. It's definitely got some caveats. But it was really easy to accelerate my code. And the results were impressive. So I thought I'd put up a few simple DSP-related benchmarks I created and ran on my laptop (a Macbook Air with NVIDIA GeForce 9400M graphics card). The m-files for the two functions I benchmarked (2D FFT and 2D...
Time Machine, Anyone?
Abstract: Dispersive linear systems with negative group delay have caused much confusion in the past. Some claim that they violate causality, others that they are the cause of superluminal tunneling. Can we really receive messages before they are sent? This article aims at pouring oil in the fire and causing yet more confusion :-).
IntroductionIn this article we reproduce the results of a physical experiment...
Correlation without pre-whitening is often misleading
White Lies
Correlation, as one of the first tools DSP users add to their tool box, can automate locating a known signal within a second (usually larger) signal. The expected result of a correlation is a nice sharp peak at the location of the known signal and few, if any, extraneous peaks.
A little thought will show this to be incorrect: correlating a signal with itself is only guaranteed to give a sharp peak if the signal's samples are uncorrelated --- for example if the signal is composed...
Instantaneous Frequency Measurement
I would like to talk about the oft used method of measuring the carrier frequency in the world of Signal Collection and Characterization world. It is an elegant technique because of its simplicity. But, of course, with simplicity, there come drawbacks (sometimes...especially with this one!).
In the world of Radar detection and characterization, one of the key characteristics of interest is the carrier frequency of the signal. If the radar is pulsed, you will have a very wide bandwidth, a...
Modelling a Noisy Communication Signal in MATLAB for the Analog to Digital Conversion Process
A critical thing to realize while modeling the signal that is going to be digitally processed is the SNR. In a receiver, the noise floor (hence the noise variance and hence its power) are determined by the temperature and the Bandwidth. For a system with a constant bandwidth, relatively constant temperature, the noise power remains relatively constant as well. This implies that the noise variance is a constant.
In MATLAB, the easiest way to create a noisy signal is by using...
Delay estimation by FFT
Given x=sig(t) and y=ref(t), returns [c, ref(t+delta), delta)] = fitSignal(y, x);:Estimates and corrects delay and scaling factor between two signals Code snippetThis article relates to the Matlab / Octave code snippet: Delay estimation with subsample resolution It explains the algorithm and the design decisions behind it.
IntroductionThere are many DSP-related problems, where an unknown timing between two signals needs to be determined and corrected, for example, radar, sonar,...
Instant CIC
Summary:
A floating point model for a CIC decimator, including the frequency response.
Description:
A CIC filter relies on a peculiarity of its fixed-point implementation: Normal operation involves repeated internal overflows that have no effect to the output signal, as they cancel in the following stage.
One way to put it intuitively is that only the speed (and rate of change) of every little "wheel" in the clockworks carries information, but its absolute position is...
A Markov View of the Phase Vocoder Part 1
Introduction
Hello! This is my first post on dsprelated.com. I have a blog that I run on my website, http://www.christianyostdsp.com. In order to engage with the larger DSP community, I'd like to occasionally post my more engineering heavy writing here and get your thoughts.
Today we will look at the phase vocoder from a different angle by bringing some probability into the discussion. This is the first part in a short series. Future posts will expand further upon the ideas...
Correlation without pre-whitening is often misleading
White LiesCorrelation, as one of the first tools DSP users add to their tool box, can automate locating a known signal within a second (usually larger) signal. The expected result of a correlation is a nice sharp peak at the location of the known signal and few, if any, extraneous peaks.
A little thought will show this to be incorrect: correlating a signal with itself is only guaranteed to give a sharp peak if the signal's samples are uncorrelated --- for example if the signal is composed...
A Markov View of the Phase Vocoder Part 2
IntroductionLast post we motivated the idea of viewing the classic phase vocoder as a Markov process. This was due to the fact that the input signal’s features are unknown to the computer, and the phase advancement for the next synthesis frame is entirely dependent on the phase advancement of the current frame. We will dive a bit deeper into this idea, and flesh out some details which we left untouched last week. This includes the effect our discrete Fourier transform has on the...
Update to a Narrow Bandpass Filter in Octave or Matlab
Following my earlier blog post (June 2020) featuring a Narrow Bandpass Filter, I’ve had some useful feedback and suggestions. This has inspired me to come up with an updated version, incorporating the following changes compared to the earlier one :
- Simpler code in Octave or Matlab
- Float32 precision replaces float64
- Faster processing by a factor of at least 4 times
- Easier setup of input parameters
- Normalized signal output level
A new experimental version in...
'z' as in 'Zorro': Frequency Masking FIR
An efficient way to implement FIR filters. Matlab / Octave example included. Keywords: Frequency masking FIR filter implementation
IntroductionAn "upsampled" FIR filter uses multiple-sample delays between the taps, compared to the unity delays in a conventional FIR filter. The resulting frequency response has steeper edges, but contains periodic images along the frequency axis (Fig. 1). Due to the latter, it is typically not too useful on its own.
Figure 1: Conventional and 'upsampled'...Matlab Programming Contest
Every 6 months Mathworks hosts an online Matlab programming contest. If you love or hate Matlab check out the contest. The group does a really good job putting together the puzzles. The contest runs for a week and starts today at noon EST (10 Nov 2010).
If you are an experienced Matlab programmer or new to Matlab it is worth checking out. Even if you do not intend on submitting solutions. Also, the problems / puzzles only require the base Matlab...
Matlab Programming Contest
Every 6 months Mathworks hosts an online Matlab programming contest. If you love or hate Matlab check out the contest. The group does a really good job putting together the puzzles. The contest runs for a week and starts today at noon EST (10 Nov 2010).
If you are an experienced Matlab programmer or new to Matlab it is worth checking out. Even if you do not intend on submitting solutions. Also, the problems / puzzles only require the base Matlab...
Update to a Narrow Bandpass Filter in Octave or Matlab
Following my earlier blog post (June 2020) featuring a Narrow Bandpass Filter, I’ve had some useful feedback and suggestions. This has inspired me to come up with an updated version, incorporating the following changes compared to the earlier one :
- Simpler code in Octave or Matlab
- Float32 precision replaces float64
- Faster processing by a factor of at least 4 times
- Easier setup of input parameters
- Normalized signal output level
A new experimental version in...
Decimators Using Cascaded Multiplierless Half-band Filters
In my last post, I provided coefficients for several multiplierless half-band FIR filters. In the comment section, Rick Lyons mentioned that such filters would be useful in a multi-stage decimator. For such an application, any subsequent multipliers save on resources, since they operate at a fraction of the maximum sample frequency. We’ll examine the frequency response and aliasing of a multiplierless decimate-by-8 cascade in this article, and we’ll also discuss an interpolator cascade using the same half-band filters.
A Markov View of the Phase Vocoder Part 1
IntroductionHello! This is my first post on dsprelated.com. I have a blog that I run on my website, http://www.christianyostdsp.com. In order to engage with the larger DSP community, I'd like to occasionally post my more engineering heavy writing here and get your thoughts.
Today we will look at the phase vocoder from a different angle by bringing some probability into the discussion. This is the first part in a short series. Future posts will expand further upon the ideas...
FREE Peer-reviewed IEEE signal processing courses
The IEEE signal processing society is offereing FREE peer reviewed courses, though not many, they are peer reviewed and span differenet topics like; wavelets, speech analysis, and statistical detection.
Enjoy
http://cnx.org/lenses/ieeesps/endorsements?b_start:int=0&-C=
A Markov View of the Phase Vocoder Part 2
IntroductionLast post we motivated the idea of viewing the classic phase vocoder as a Markov process. This was due to the fact that the input signal’s features are unknown to the computer, and the phase advancement for the next synthesis frame is entirely dependent on the phase advancement of the current frame. We will dive a bit deeper into this idea, and flesh out some details which we left untouched last week. This includes the effect our discrete Fourier transform has on the...
The Discrete Fourier Transform of Symmetric Sequences
Symmetric sequences arise often in digital signal processing. Examples include symmetric pulses, window functions, and the coefficients of most finite-impulse response (FIR) filters, not to mention the cosine function. Examining symmetric sequences can give us some insights into the Discrete Fourier Transform (DFT). An even-symmetric sequence is centered at n = 0 and xeven(n) = xeven(-n). The DFT of xeven(n) is real. Most often, signals we encounter start at n = 0, so they are not strictly speaking even-symmetric. We’ll look at the relationship between the DFT’s of such sequences and those of true even-symmetric sequences.
