Periodic Signal Processing Using a Brillouin Gain Comb

2008 ◽  
Author(s):  
C. Jauregui ◽  
P. Petropoulos ◽  
D.J. Richardson
Keyword(s):  
Signal Processing ◽  
Periodic Signal ◽  
Brillouin Gain

scholarly journals Sub-Nyquist artefacts and sampling moiré effects

2015 ◽  
Vol 2 (3) ◽  
pp. 140550 ◽  
Author(s):  
Isaac Amidror
Keyword(s):  
Signal Processing ◽  
Low Frequency ◽  
Sampling Frequency ◽  
Signal Frequency ◽  
Periodic Signal ◽  
Unified Approach ◽  
Sampled Signal

Sampling moiré effects are well known in signal processing. They occur when a continuous periodic signal g ( x ) is sampled using a sampling frequency f s that does not respect the Nyquist condition, and the signal-frequency f folds over and gives a new, false low frequency in the sampled signal. However, some visible beating artefacts may also occur in the sampled signal when g ( x ) is sampled using a sampling frequency f s which fully respects the Nyquist condition. We call these phenomena sub-Nyquist artefacts . Although these beating effects have already been reported in the literature, their detailed mathematical behaviour is not widely known. In this paper, we study the behaviour of these phenomena and compare it with analogous results from the moiré theory. We show that both sampling moirés and sub-Nyquist artefacts obey the same basic mathematical rules, in spite of the differences between them. This leads us to a unified approach that explains all of these phenomena and puts them under the same roof. In particular, it turns out that all of these phenomena occur when the signal-frequency f and the sampling frequency f s satisfy f ≈( m / n ) f s with integer m , n , where m / n is a reduced integer ratio; cases with n =1 correspond to true sampling moiré effects.

scholarly journals Multiple Harmonics Fitting Algorithms Applied to Periodic Signals Based on Hilbert-Huang Transform

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Wang ◽  
Zhengshi Liu ◽  
Bin Zhu ◽  
Quanjun Song
Keyword(s):  
Signal Processing ◽  
Harmonic Distortion ◽  
Digital Signal ◽  
Periodic Signal ◽  
Sinusoidal Wave ◽  
Analog To Digital ◽  
Triangular Wave ◽  
Hilbert Huang Transform ◽  
Digital To Analog Converters ◽  
Periodic Signals

A new generation of multipurpose measurement equipment is transforming the role of computers in instrumentation. The new features involve mixed devices, such as kinds of sensors, analog-to-digital and digital-to-analog converters, and digital signal processing techniques, that are able to substitute typical discrete instruments like multimeters and analyzers. Signal-processing applications frequently use least-squares (LS) sine-fitting algorithms. Periodic signals may be interpreted as a sum of sine waves with multiple frequencies: the Fourier series. This paper describes a new sine fitting algorithm that is able to fit a multiharmonic acquired periodic signal. By means of a “sinusoidal wave” whose amplitude and phase are both transient, the “triangular wave” can be reconstructed on the basis of Hilbert-Huang transform (HHT). This method can be used to test effective number of bits (ENOBs) of analog-to-digital converter (ADC), avoiding the trouble of selecting initial value of the parameters and working out the nonlinear equations. The simulation results show that the algorithm is precise and efficient. In the case of enough sampling points, even under the circumstances of low-resolution signal with the harmonic distortion existing, the root mean square (RMS) error between the sampling data of original “triangular wave” and the corresponding points of fitting “sinusoidal wave” is marvelously small. That maybe means, under the circumstances of any periodic signal, that ENOBs of high-resolution ADC can be tested accurately.

scholarly journals All-Optical Signal Processing of Periodic Signals Using a Brillouin Gain Comb

2008 ◽  
Vol 26 (17) ◽  
pp. 3110-3117 ◽  
Author(s):  
CÉsar Jauregui Misas ◽  
Periklis Petropoulos ◽  
David J. Richardson
Keyword(s):  
Signal Processing ◽  
Optical Signal Processing ◽  
Optical Signal ◽  
All Optical ◽  
Periodic Signals ◽  
Brillouin Gain

Advanced Integrated Microwave Signal Processing With Giant On-Chip Brillouin Gain

2017 ◽  
Vol 35 (4) ◽  
pp. 846-854 ◽  
Author(s):  
Amol Choudhary ◽  
Blair Morrison ◽  
Iman Aryanfar ◽  
Shayan Shahnia ◽  
Mattia Pagani ◽  
...  
Keyword(s):  
Signal Processing ◽  
Microwave Signal ◽  
Brillouin Gain ◽  
On Chip

On-line frequency adaptation and movement imitation for rhythmic robotic tasks

2011 ◽  
Vol 30 (14) ◽  
pp. 1775-1788 ◽  
Author(s):  
Tadej Petrič ◽  
Andrej Gams ◽  
Auke Jan Ijspeert ◽  
Leon Žlajpah
Keyword(s):  
Signal Processing ◽  
Fourier Series ◽  
Feedback Loop ◽  
Periodic Signal ◽  
Basic Frequency ◽  
Additional Signal ◽  
Emg Signal ◽  
Non Linear ◽  
Novel Method ◽  
Robotic Tasks

In this paper we present a novel method to obtain the basic frequency of an unknown periodic signal with an arbitrary waveform, which can work online with no additional signal processing or logical operations. The method originates from non-linear dynamical systems for frequency extraction, which are based on adaptive frequency oscillators in a feedback loop. In previous work, we had developed a method that could extract separate frequency components by using several adaptive frequency oscillators in a loop, but that method required a logical algorithm to identify the basic frequency. The novel method presented here uses a Fourier series representation in the feedback loop combined with a single oscillator. In this way it can extract the frequency and the phase of an unknown periodic signal in real time and without any additional signal processing or preprocessing. The method determines the Fourier series coefficients and can be used for dynamic Fourier series implementation. The proposed method can be used for the control of rhythmic robotic tasks, where only the extraction of the basic frequency is crucial. For demonstration several highly non-linear and dynamic periodic robotic tasks are shown, including also a task where an electromyography (EMG) signal is used in a feedback loop.

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