Orthogonal waveform separation in multiple‐input and multiple‐output imaging sonar with fractional Fourier filtering

The azimuth resolution of imaging sonar lies on the aperture of the sensor array. In the paper, the Multiple-input Multiple-output Synthetic Aperture Sonar (MIMO-SAS) composite acoustic imaging technique was applied to imaging sonar equipped on the movable platform. The azimuth resolutions of both cross-track direction and along-track direction were obtained. The simulation results demonstrate that comparing with the conventional imaging technique Single-Input Multiple-output (SIMO), the azimuth resolution of cross-track direction is improved since the virtual array aperture of MIMO technique, and the azimuth resolution of along-track direction is obtained since the synthetic aperture of SAS technique. The simulation results are consistent with the theoretic value of the azimuth resolution.

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Multiple-input single-output Fourier filtering systems using bacteriorhodopsin material

Journal of Optics A Pure and Applied Optics ◽

10.1088/1464-4258/4/6/310 ◽

2002 ◽

Vol 4 (6) ◽

pp. 662-664

Author(s):

S Chang ◽

Q Yuan ◽

C P Grover

Keyword(s):

Single Output ◽

Fourier Filtering ◽

Multiple Input

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Application of cross correlation to HREM images of surfaces

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100128080 ◽

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.

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Two structural states of the Z-band in cardiac and skeletal muscle

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100157097 ◽

1989 ◽

Vol 47 ◽

pp. 1022-1023

Author(s):

J.P. Schroeter ◽

M.A. Goldstein ◽

J.P. Bretaudiere ◽

L.H. Michael ◽

R.L. Sass

Keyword(s):

Skeletal Muscle ◽

Cross Section ◽

Real Space ◽

Contractile State ◽

Contour Maps ◽

False Color ◽

Grey Scale ◽

Fourier Filtering ◽

Cardiac Muscles ◽

Enhancement Algorithm

We have recently established the existence of two structural states of the Z band lattice in cross section in cardiac as well as in skeletal muscle. The two structural states are related to the contractile state of the muscle. In skeletal muscle at rest, the Z band is in the small square (ss) lattice form, but tetanized muscle exhibits the basket weave (bw) form. In contrast, unstimu- lated cardiac muscle exhibits the bw form, but cardiac muscles exposed to EGTA show the ss form.We have used two-dimensional computer enhancement techniques on digitized electron micrographs to compare each lattice form as it appears in both cardiac and skeletal muscle. Both real space averaging and fourier filtering methods were used. Enhanced images were displayed as grey-scale projections, as contour maps, and in false color.There is only a slight difference between the lattices produced by the two different enhancement techniques. Thus the information presented in these images is not likely to be an artifact of the enhancement algorithm.

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An Effective Model of the Overshooting Effect for Multiple-Input Gates in Nanometer Technologies

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e97.a.1059 ◽

2014 ◽

Vol E97.A (5) ◽

pp. 1059-1074

Author(s):

Li DING ◽

Zhangcai HUANG ◽

Atsushi KUROKAWA ◽

Jing WANG ◽

Yasuaki INOUE

Keyword(s):

Effective Model ◽

Multiple Input

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Multiple-input multiple-output time-series models from short data records

10.2514/6.1991-994 ◽

1991 ◽

Author(s):

JOSEPH HOLLKAMP

Keyword(s):

Time Series ◽

Multiple Input Multiple Output ◽

Time Series Models ◽

Multiple Input ◽

Input Multiple Output ◽

Output Time

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Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

Xplore Journal of Statistics ◽

10.29244/xplore.v7i3.149 ◽

2019 ◽

Vol 7 (3) ◽

Author(s):

Nur Laela Fitriani ◽

Pika Silvianti ◽

Rahma Anisa

Keyword(s):

Exchange Rate ◽

Transfer Function ◽

Multivariate Time Series ◽

Model Identification ◽

Transfer Function Model ◽

Function Model ◽

Jones Index ◽

Multiple Input ◽

Identification Stage ◽

Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

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