The Impact of Order Statistics on Signal Processing

1996 ◽  
pp. 153-176
Author(s):  
Alan C. Bovik ◽  
Scott T. Acton
Keyword(s):  
Signal Processing ◽  
Order Statistics ◽  
The Impact

Nonlinear Techniques for Signals Characterization

2011 ◽  
pp. 1266-1272
Author(s):  
Jesús Bernardino Alonso Hernández ◽  
Patricia Henríquez Rodríguez
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Order Statistics ◽  
Chaos Theory ◽  
Initial Conditions ◽  
Digital Signal ◽  
Higher Order ◽  
Higher Order Statistics ◽  
Three Body ◽  
Nonlinear Signal

The field of nonlinear signal characterization and nonlinear signal processing has attracted a growing number of researchers in the past three decades. This comes from the fact that linear techniques have some limitations in certain areas of signal processing. Numerous nonlinear techniques have been introduced to complement the classical linear methods and as an alternative when the assumption of linearity is inappropriate. Two of these techniques are higher order statistics (HOS) and nonlinear dynamics theory (chaos). They have been widely applied to time series characterization and analysis in several fields, especially in biomedical signals. Both HOS and chaos techniques have had a similar evolution. They were first studied around 1900: the method of moments (related to HOS) was developed by Pearson and in 1890 Henri Poincaré found sensitive dependence on initial conditions (a symptom of chaos) in a particular case of the three-body problem. Both approaches were replaced by linear techniques until around 1960, when Lorenz rediscovered by coincidence a chaotic system while he was studying the behaviour of air masses. Meanwhile, a group of statisticians at the University of California began to explore the use of HOS techniques again. However, these techniques were ignored until 1980 when Mendel (Mendel, 1991) developed system identification techniques based on HOS and Ruelle (Ruelle, 1979), Packard (Packard, 1980), Takens (Takens, 1981) and Casdagli (Casdagli, 1989) set the methods to model nonlinear time series through chaos theory. But it is only recently that the application of HOS and chaos in time series has been feasible thanks to higher computation capacity of computers and Digital Signal Processing (DSP) technology. The present article presents the state of the art of two nonlinear techniques applied to time series analysis: higher order statistics and chaos theory. Some measurements based on HOS and chaos techniques will be described and the way in which these measurements characterize different behaviours of a signal will be analized. The application of nonlinear measurements permits more realistic characterization of signals and therefore it is an advance in automatic systems development.

Detection of Impact Location for Composite Stiffened Panel Using FBG Sensors

2010 ◽  
Vol 123-125 ◽  
pp. 895-898 ◽  
Author(s):  
Sang Oh Park ◽  
Byeong Wook Jang ◽  
Yeon Gwan Lee ◽  
Yoon Young Kim ◽  
Chun Gon Kim ◽  
...  
Keyword(s):  
Signal Processing ◽  
Noise Level ◽  
Composite Plate ◽  
Composite Plates ◽  
Stiffened Panel ◽  
Arrival Times ◽  
Fbg Sensor ◽  
Composite Stiffened Panel ◽  
Fbg Sensors ◽  
The Impact

We carried out experiments to detect impact locations on a composite plate using two types of composite plates, a composite flat plate with a constant thickness of 5 mm and a composite stiffened panel with stringers. Four multiplexed FBG sensors were attached to the bottom surface of the composite plates to acquire impact signals. The FBG sensor wavelength shift data were collected at a sampling frequency of 40 kHz using a high-speed FBG interrogator (SFI-710, Fiberpro Inc., Korea). The arrival times of the impact signals at each FBG sensor were obtained using a signal processing procedure. The arrival times were affected by noise level and signal-to-noise ratio. In order to overcome this weakness, signal processing techniques such as wavelet decomposition, normalization using each noise level and filtering with a moving average were adopted. To calculate the impact locations of the composite plate, a neural network algorithm was applied.

scholarly journals The Expected Utility Insurance Premium Principle with Fourth-Order Statistics: Does It Make a Difference?

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 116
Author(s):  
Alessandro Mazzoccoli ◽  
Maurizio Naldi
Keyword(s):  
Order Statistics ◽  
Fourth Order ◽  
Expected Utility ◽  
Value At Risk ◽  
Order Approximation ◽  
Insurance Premium ◽  
Second Order ◽  
Accurate Approximation ◽  
Increased Risk ◽  
The Impact

The expected utility principle is often used to compute the insurance premium through a second-order approximation of the expected value of the utility of losses. We investigate the impact of using a more accurate approximation based on the fourth-order statistics of the expected loss and derive the premium under this expectedly more accurate approximation. The comparison between the two approximation levels shows that the second-order-based premium is always lower (i.e., an underestimate of the correct one) for the commonest loss distributions encountered in insurance. The comparison is also carried out for real cases, considering the loss parameters values estimated in the literature. The increased risk of the insurer is assessed through the Value-at-Risk.

Experimental Analysis of an Instrumented Charpy Impact Using Signal Processing Approach

2011 ◽  
Vol 197-198 ◽  
pp. 1621-1625
Author(s):  
Mohd Basri Ali ◽  
Shahrum Abdullah ◽  
M.Zaki Nuawi ◽  
M.M. Padzi ◽  
K.A. Zakaria
Keyword(s):  
Signal Processing ◽  
Data Acquisition ◽  
Method Comparison ◽  
Low Carbon Steel ◽  
Charpy Impact ◽  
Dynamic Responses ◽  
Low Carbon ◽  
Impact Machine ◽  
Processing Approach ◽  
The Impact

The dynamic responses of the standard charpy impact machine are experimentally studied using the relevant data acquisition system in order to obtain the impact response. For this reason, strain gauges were connected to the data acquisition set and it was then attached to the charpy striker for the signal collection. Aluminium 6061 and low carbon steel 1050 were used for extracting strain responses on the striker during the testing. In this work, the power spectrum density (PSD) approach was then used for the energy based observation and a signal was converted from the time domain to the frequency domain using the fast Fourier transform (FFT) method. Comparison between experimental findings with related parameters such as of different materials, strain signals pattern, I-kaz, were finally correlated and discussed. It was found that the modulus of elasticity were proportional to the energy absorbed, strain signals amplitude and PSD. Finally, it is suggested that the properties of materials and the impact signals pattern is suitable to be analysed using the signal processing approach.

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