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hi.... for DIT fft algorithm, refer to this link, it has c-code for that. http://cnx.org/content/m12016/latest/
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What are there advantages and where are there used fft?
because they have a high speed compared to fft
How is math used in hacking?
Calculus is used primarily to hack into signals, your basic FFT analyzers which incorporate power series, etc .... if you use math to construct signals than the reverse can be applied. Thus integration and Differentiation.
What is power spectral density?
power spectral density (PSD), which describes how the power of a signal or time series is distributed with frequency. Here power can be the actual physical power, or more often, for convenience with abstract signals, can be defined as the squared value of the signal, that is, as the actual power if the signal was a voltage applied to a 1-ohm load.Since a signal with nonzero average power is not square integrable, the Fourier transforms do not exist in this case. Fortunately, the Wiener-Khinchin theorem provides a simple alternative. The PSD is the Fourier transform of the autocorrelation function, R(Ï„), of the signal if the signal can be treated as a wide-sense stationary random process.The power of the signal in a given frequency band can be calculated by integrating over positive and negative frequencies.The power spectral density of a signal exists if and only if the signal is a wide-sense stationary process. If the signal is not stationary, then the autocorrelation function must be a function of two variables, so no PSD exists, but similar techniques may be used to estimate a time-varying spectral density.
Difference between ofdm dsss fhss?
Orthogonal Frequency Division Multiplexing (OFDM) is a multiple-carrier (MC) modulation technique which creates frequency diversity. A high-speed data stream is converted into multiple low-speed data streams via Serial-to-Parallel (S/P) conversion. Each data stream is modulated by a subcarrier. That way, instead of having a frequency-selective fading wireless channel, where each frequency component of the signal is attenuated and phase-shifted in different amount, we have multiple flat-fading subchannels. In other words, instead of having a signal with symbol duration smaller than the channel delay (remember that high frequency means low duration because f = 1/T), we have may subsignals with duration larger than the channel delay (to simplify things, consider this: if the symbol duration is 1 s and the channel delay is 10 s, we will have interference between 10 symbols). That way, channel distortion is compensated. The OFDM symbol is the composite signal formed by the sum of the subcarriers, so the data rate is still the same as if we transmit the original high-speed signal, but as we said, the channel distortion is compensated. OFDM compensates the Inter-Symbol Interference (ISI) caused by the fact that different signals take different paths and arrive at the receiver with different delay (multipaht propagation distortion). OFDM subchannels are not separated by a guard band, but they overlap. However, they are orthogonal at the subcarrier frequencies, and that way they don't interfere with each other. We have very good utilization of the available bandwidth due to the overlapping of the subchannels. Moreover, each subcarrier can be modulated seperatelly (usually, QAM or QPSK modulation is used, depending on the channel conditions, which are measuer using channel estimation via pilot carriers). Also, we can use adaptive modulation and conding (AMC) at each subchannel in order to accomplish error-free communication with the highest data-rate possible. Due to the orthogonality principle, we don't need a bank of modulators at the transmiter and a bank of demodulators/detectors at the receiver, but simply a chip implementing the Inverse Discete Fourier Transform (IDFT) and the DFT respectively - and that can be done easy, effectively and with low-cost using a chip running the Fast Fourier Transform (FFT) algorithm. Timing errors/phase distortion must be controlled because they may create ISI between OFDM symbols and ICI (Inter-Carrier Interference) between subcarriers. We add a Cyclic Prefix (CP) to avoid ISI between OFDM symbols and synchronization methods to avoid ICI. Also, the inherent Peak-to-Average Power Ratio (PAPR) of the OFDM signal must be reduced because forces the power amplifier of the transmitter to operate on the non-linear region of its characteristic function. Spread Spectrum (SS) techniques convert a low-speed data stream into a high-speed data stream. That way, the bandwidth of the modulated carrier becomes much larger than the minimum required transmission bandwidth. This is like Frequency Modulation (FM): we trade transmission bandwidth with Signal-to-Noise (S/N) ratio, meaning that we can have error-free communication transmiting lower-power signals. The signal is spreaded in a huge bandwidth. That way, instead of having the noise (which is like an interference signal) concetrated to some symbols and corrupting the signal, the noise is uniformly distributed over the signal bandwidth. Moreover, the signal is not easilly detectable by a third-party because it is hided in the background noise. Finally, it has anti-jamming characteristics. There are two techniques to create a SS signal: Direct-Sequence Spread Spectrum (DSSS) multiplies the data stream with a high-data rate sequence called chip sequence or Pseudo-Noise (PN) sequence, because due to its lenght is seems as a random signal, like the noise (but of course, it is a completely deterministic signal; that's why we use the Greek term "Pseudo-", which means "it appears to be, but it is not"). DSSS creates time diversity (a "variety" in the time domain). Frequency-Hopping Spread Specturm (FHSS) uses a chip sequence to conrol the frequency hops of the carrier. The resulting signal is like a progressive-FM signal. FHSS creates frequency diversity (a "variety" in the frequency domain). SS techniques give a Spreading Gain (SG) to the transmitted signal, which is simillar to the Coding Gain (CG) of the error-control codes [remember: in Forward-Error Correction (FEC) techniques, we add additional bits to correct errors; we use this rendudancy for error control. That is, we increase the transmitted bandwidth but we can decrease the transmitted power required to have an acceptable S/N at the receiver). Moreover, SS techniques can be combined with multiple access techniques (patterns for multiple users access a network by sharing the common channel). With Code Division Multiple Access (CDMA) a code sequence is used to give an identity to each user, which than we will transmit a signal spreaded by a PN sequence. So, each user can use the whole available bandwidth for all the time, but users do not interfere because they are separated in the code domain. The orthogonality of the codes (of the signals) must be maintained, because otherwise we will have interference between the users. Finally, a RAKE receiver can be used to resolve the multiple paths and compensated the ISI caused due to the multipath propagation. OFDM and SS techniques can be combined (MC-CDMA). OFDM can also be combined with the Frequency Division Multiple Access (FDMA) -> OFDMA. Finally, OFDM can be combined with Multiple Input-Multiple Output (MIMO) techniques. In MIMO, we have multiple transmiting and receiving antennas. So, we have N parallel channels instead of a single channel, and this creates a signal which is N times faster (oversimplified, but basically true ...). In each channel we can use OFDM to avoid ISI and frequency-selectivity, while maintaining the high-data rate. Finally, MIMO can create diversity which enables the system to receive the best-quality signal.
Difference between fourier series and z-transform?
Laplace = analogue signal Fourier = digital signal Notes on comparisons between Fourier and Laplace transforms: The Laplace transform of a function is just like the Fourier transform of the same function, except for two things. The term in the exponential of a Laplace transform is a complex number instead of just an imaginary number and the lower limit of integration doesn't need to start at -∞. The exponential factor has the effect of forcing the signals to converge. That is why the Laplace transform can be applied to a broader class of signals than the Fourier transform, including exponentially growing signals. In a Fourier transform, both the signal in time domain and its spectrum in frequency domain are a one-dimensional, complex function. However, the Laplace transform of the 1D signal is a complex function defined over a two-dimensional complex plane, called the s-plane, spanned by two variables, one for the horizontal real axis and one for the vertical imaginary axis. If this 2D function is evaluated along the imaginary axis, the Laplace transform simply becomes the Fourier transform.
Need of fft?
FT is needed for spectrum analysis, FFT is fast FT meaning it is used to obtain spectrum of a signal quickly, the FFT algorithm inherently is fast algorithm than the conventional FT algorithm
How many adders are required to realize a 256point radix-2 DIT-FFT algorithm?
i don’t know
What is twiddle factor?
A twiddle factor, in fast Fourier transform (FFT) algorithms, is any of the trigonometric constant coefficients that are multiplied by the data in the course of the algorithm.
What are there advantages and where are there used fft?
because they have a high speed compared to fft
What are there advantages and where are there used?
FFT reduces the computation since no. of complex multiplications required in FFT are N/2(log2N). FFT is used to compute discrete Fourier transform.
How do you plot frequency spectrum using fft function in MATLAB?
To plot the frequency spectrum using the fft function in MATLAB, first compute the one-dimensional discrete Fourier transform using fft, provide the data series as an input. Then use the abs() function to get the magnitude spectrum, and plot the result using the plot() function, with the frequency domain on the x-axis and the magnitude spectrum on the y-axis.
What is the need of FFT?
There's no need for it.
Why FFT is faster than DFT?
FFT is faster than DFT because no. of complex multiplication in DFT is N^2 while in FFT no. of complex multiplications are N/2(log2N). for example if N=8 no. of complex multiplications required in DFT are 64. while no. of complex multiplications required in FFT are 12 thus reduces computation time.
What does fft mean in txt talk?
Food For Thought
What is the meaning of fft in ofdma?
Fast Fourier Transform
How much health does wiegraf have in demon form on fft?
1045
How do you plot FFT in simulink?
FFT is the frequency domain representation. In can be shown in Simulink with blocks. These blocks graphically show the domain or x value plotted against the frequency or y value.
