Computationally efficient methods for analysis and synthesis of real signals using FFT and IFFT

Fertner, Antoni

doi:10.1109/78.752603

Cited by 11 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternatively, the original data may be cut into two-halves, consisting of odd-and even-numbered data points and by treating these halves as the real and the imaginary inputs to the FFT 51 . However, the general requirement of the FFT algorithm for complex numbers from real data remains 41,52 …”

Section: The Fourier Transformmentioning

confidence: 99%

See 1 more Smart Citation

Application of wavelet transforms in de-noising optical emission transient signals generated from microsamples introduced into a microplasma and comparison with Fourier- and Hartley-transforms

Hunter

Karanassios

2011

SPIE Proceedings

View full text Add to dashboard Cite

The application of wavelet transforms in de-noising optical emission transient, time-domain signals generated from microsamples introduced into an atmospheric-pressure microplasma-on-a-chip is described. The wavelet method of denoising transient signals is compared with traditional noise-filtering methods such as Fast Fourier Transform (FFT) and Fast Hartley Transform (FHT) signal processing. For the transient signals of interest to this work, the wavelet method proved to be superior to both the FFT and the FHT.

show abstract

Section: The Fourier Transformmentioning

confidence: 99%

“…The forward and inverse Hartley transforms (described by eqs. 5 and 6) have been developed for inherently real mathematical functions 52 .…”

Section: The Hartley Transformmentioning

confidence: 99%

Application of wavelet transforms in de-noising optical emission transient signals generated from microsamples introduced into a microplasma and comparison with Fourier- and Hartley-transforms

Hunter

Karanassios

2011

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…On the other hand, the IFFT is the inverse processing of the FFT into the time domain. The implementation of the IFFT on a DSP or microcontroller is carried out using convolution and a correlation [12], respectively. Linear filtering is used to pass 0.5-4.5 Hz of frequencies and block others.…”

Section: Signal Reconstructionmentioning

confidence: 99%

Electret Condenser Microphone as Sensor in Arterial Pulse Recording Device

Yudaningtyas¹

2020

GEOMATE

View full text Add to dashboard Cite

One of the human pulses could be detected on an arterial radial that represents the human health condition. Several sensors could capture the pulse signal. However, it still needed a sensor with a small dimension and lightweight. One of the sensors that comply with those specifications is an electret condenser microphone (ECM). The ECM was designed to pick up the signal from the human radial arterial pulse. The ECM pick up system was equipped with an instrumentation amplifier and a signal processing system. The characteristic of the ECM was analyzed using a sixth-order polynomial equation. The equation was used to reconstruct the signal, which flattened the frequency response at the range of 0.5-4.5 Hz. The signal processing consists of an FFT, peak detection and a sixth-order anti-polynomial computational unit which is implemented in a computer. The minimum error was found at 1.5 Hz, which is closed to the designated frequency of the arterial pulse normal frequency.

show abstract

“…Proof: When ck and uk have HS and WS symmetry, respectively, dk c uk has HS symmetry [26]. The mirror symmetry in the input and output signals thereby allows their FFT/IFFT to be computed using half-length counterparts [27]. Note that Eq.…”

Section: B Discretization Of Continuous Convolutionmentioning

confidence: 99%

Discretization of continuous convolution operators for accurate modeling of wave propagation in digital holography

2013

View full text Add to dashboard Cite

Discretization of continuous (analog) convolution operators by direct sampling of the convolution kernel and use of fast Fourier transforms is highly efficient. However, it assumes the input and output signals are band-limited, a condition rarely met in practice, where signals have finite support or abrupt edges and sampling is nonideal. Here, we propose to approximate signals in analog, shift-invariant function spaces, which do not need to be band-limited, resulting in discrete coefficients for which we derive discrete convolution kernels that accurately model the analog convolution operator while taking into account nonideal sampling devices (such as finite fill-factor cameras). This approach retains the efficiency of direct sampling but not its limiting assumption. We propose fast forward and inverse algorithms that handle finite-length, periodic, and mirror-symmetric signals with rational sampling rates. We provide explicit convolution kernels for computing coherent wave propagation in the context of digital holography. When compared to band-limited methods in simulations, our method leads to fewer reconstruction artifacts when signals have sharp edges or when using nonideal sampling devices.

show abstract

Computationally efficient methods for analysis and synthesis of real signals using FFT and IFFT

Cited by 11 publications

References 2 publications

Application of wavelet transforms in de-noising optical emission transient signals generated from microsamples introduced into a microplasma and comparison with Fourier- and Hartley-transforms

Application of wavelet transforms in de-noising optical emission transient signals generated from microsamples introduced into a microplasma and comparison with Fourier- and Hartley-transforms

Electret Condenser Microphone as Sensor in Arterial Pulse Recording Device

Discretization of continuous convolution operators for accurate modeling of wave propagation in digital holography

Contact Info

Product

Resources

About