scholarly journals Identifying Odd/Even-Order Binary Kernel Slices for a Nonlinear System Using Inverse Repeat m-Sequences

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jin-yan Hu ◽  
Gang Yan ◽  
Tao Wang
Keyword(s):  
Nonlinear System ◽  
Cross Correlation ◽  
Correlation Method ◽  
Kernel Functions ◽  
Third Order ◽  
Mathematical Properties ◽  
M Sequence ◽  
System Output ◽  
Even Order ◽  
Order Kernel

The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time. A well-established model based on kernel functions for input of the maximum length sequence (m-sequence) can be used to estimate nonlinear binary kernel slices using cross-correlation method. In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output. We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. An instance of third-order Wiener nonlinear model is simulated to justify the proposed method.

Identification of the Nonlinear Vibration Characteristics Based on the Wiener Kernels

2012 ◽  
Vol 204-208 ◽  
pp. 4668-4672
Author(s):  
Yi Chun Ren ◽  
Yin Hua Yu
Keyword(s):  
Nonlinear System ◽  
White Noise ◽  
Input Signal ◽  
Correlation Functions ◽  
Nonlinear Response ◽  
Cross Correlation ◽  
Gaussian White Noise ◽  
Wiener Kernels ◽  
Input And Output ◽  
Order Kernel

Wiener series are the important functional series describing nonlinear system. If an input signal is the Gaussian white noise, the wiener kernels are estimated through calculating the cross-correlation functions of the input and output of a nonlinear system. In this paper, a single-DOF quadratic nonlinear system is identified by the Wiener series. The results show that the intensity of the nonlinear response can be expressed by the second-order kernel.

A Linear Compressibility Assumption for the Multiple Integral Representation of Nonlinear Creep of Polyurethane

1970 ◽  
Vol 37 (2) ◽  
pp. 441-448 ◽  
Author(s):  
K. G. Nolte ◽  
W. N. Findley
Keyword(s):  
Integral Representation ◽  
Multiple Integral ◽  
Kernel Functions ◽  
Nonlinear Creep ◽  
Volume Changes ◽  
Creep Tests ◽  
Third Order ◽  
Nonlinear Viscoelastic ◽  
Different Order ◽  
Order Kernel

The assumption that volume changes associated with creep of a nonlinear viscoelastic material are only linearly dependent on the stress history is incorporated into a third-order multiple integral representation. This assumption reduces the number of independent kernel functions in the representation from 12 to 7. The traces of these independent kernels may be determined from two tension, two torsion, and one combined tension and torsion creep tests. Experiments on polyurethane are well represented by this method. The time-dependence of the kernel functions is expressed by time raised to a power with the power differing for different-order kernel functions.

Application of cross correlation to HREM images of surfaces

1987 ◽  
Vol 45 ◽  
pp. 754-755
Author(s):  
D. E. Luzzi ◽  
L. D. Marks ◽  
M. I. Buckett
Keyword(s):  
Cross Correlation ◽  
A Priori ◽  
Matrix Material ◽  
Correlation Method ◽  
Accurate Knowledge ◽  
Complex Image ◽  
Fourier Filtering ◽  
The Matrix ◽  
Low Pass ◽  
Data Reduction Technique

As the HREM becomes increasingly used for the study of dynamic localized phenomena, the development of techniques to recover the desired information from a real image is important. Often, the important features are not strongly scattering in comparison to the matrix material in addition to being masked by statistical and amorphous noise. The desired information will usually involve the accurate knowledge of the position and intensity of the contrast. In order to decipher the desired information from a complex image, cross-correlation (xcf) techniques can be utilized. Unlike other image processing methods which rely on data massaging (e.g. high/low pass filtering or Fourier filtering), the cross-correlation method is a rigorous data reduction technique with no a priori assumptions.We have examined basic cross-correlation procedures using images of discrete gaussian peaks and have developed an iterative procedure to greatly enhance the capabilities of these techniques when the contrast from the peaks overlap.

Cross-Correlation Method for the Formation of Laser Energy Fields with Complex Distributions

2013 ◽  
Vol 58 (2) ◽  
pp. 122-125 ◽  
Author(s):  
O.V. Gnatovskyy ◽  
◽  
A.M. Negriyko ◽  
V.O. Gnatovskyy ◽  
A.V. Sidorenko ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Laser Energy ◽  
Correlation Method

scholarly journals A Novel Gesture Recognition System Based on CSI Extracted from a Smartphone with Nexmon Firmware

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 222
Author(s):  
Tao Li ◽  
Chenqi Shi ◽  
Peihao Li ◽  
Pengpeng Chen
Keyword(s):  
Frequency Domain ◽  
Gesture Recognition ◽  
Channel State Information ◽  
Cross Correlation ◽  
Recognition Accuracy ◽  
Correlation Method ◽  
Bottom Layer ◽  
Recognition System ◽  
Channel State ◽  
State Information

In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system’s mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system’s recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy.

scholarly journals RECOGNIZING DIFFERENT TYPES OF STOCHASTIC PROCESSES

2006 ◽  
Vol 06 (01) ◽  
pp. L1-L6
Author(s):  
JONG U. KIM ◽  
LASZLO B. KISH
Keyword(s):  
Image Processing ◽  
Stochastic Processes ◽  
Cross Correlation ◽  
Correlation Method ◽  
Correlation Coefficients ◽  
Power Density Spectrum ◽  
Density Spectrum ◽  
Random Telegraph Signals ◽  
Different Types ◽  
Coefficient Method

We propose a new cross-correlation method that can recognize independent realizations of the same type of stochastic processes and can be used as a new kind of pattern recognition tool in biometrics, sensing, forensic, security and image processing applications. The method, which we call bispectrum correlation coefficient method, makes use of the cross-correlation of the bispectra. Three kinds of cross-correlation coefficients are introduced. To demonstrate the new method, six different random telegraph signals are tested, where four of them have the same power density spectrum. It is shown that the three coefficients can map the different stochastic processes to specific sub-volumes in a cube.

Sign in / Sign up

Export Citation Format

Share Document