scholarly journals Analysis on SSR frequency components by studying two-dimensional modulation of synchronous generator's flux linkage

2019 ◽  
Vol 2019 (16) ◽  
pp. 1313-1318
Author(s):  
Juan Li ◽  
Xiaoming Yuan ◽  
Meiqing Zhang ◽  
Zhihao Liu
Keyword(s):  
Two Dimensional ◽  
Flux Linkage ◽  
Frequency Components ◽  
Ssr Frequency

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

scholarly journals Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (12) ◽  
pp. 1405 ◽  
Author(s):  
Razika Bazine ◽  
Huayi Wu ◽  
Kamel Boukhechba
Keyword(s):  
Hyperspectral Imagery ◽  
Wiener Filter ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Computational Time ◽  
Support Vector ◽  
Two Dimensional ◽  
Significant Information ◽  
Dct Domain ◽  
Frequency Components

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.

Prediction of Complex Two-Dimensional Trajectories by a Cerebellar Model of Smooth Pursuit Eye Movement

1997 ◽  
Vol 77 (4) ◽  
pp. 2115-2130 ◽  
Author(s):  
R. E. Kettner ◽  
S. Mahamud ◽  
H.-C. Leung ◽  
N. Sitkoff ◽  
J. C. Houk ◽  
...  
Keyword(s):  
Eye Movement ◽  
Visual Feedback ◽  
Smooth Pursuit ◽  
Low Frequency ◽  
Learning Rule ◽  
Two Dimensional ◽  
Circular Trajectory ◽  
Smooth Pursuit Eye Movement ◽  
Small Phase ◽  
Frequency Components

Kettner, R. E., S. Mahamud, H.-C. Leung, N. Sitkoff, J. C. Houk, B. W. Peterson, and A. G. Barto. Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement. J. Neurophysiol. 77: 2115–2130, 1997. A neural network model based on the anatomy and physiology of the cerebellum is presented that can generate both simple and complex predictive pursuit, while also responding in a feedback mode to visual perturbations from an ongoing trajectory. The model allows the prediction of complex movements by adding two features that are not present in other pursuit models: an array of inputs distributed over a range of physiologically justified delays, and a novel, biologically plausible learning rule that generated changes in synaptic strengths in response to retinal slip errors that arrive after long delays. To directly test the model, its output was compared with the behavior of monkeys tracking the same trajectories. There was a close correspondence between model and monkey performance. Complex target trajectories were created by summing two or three sinusoidal components of different frequencies along horizontal and/or vertical axes. Both the model and the monkeys were able to track these complex sum-of-sines trajectories with small phase delays that averaged 8 and 20 ms in magnitude, respectively. Both the model and the monkeys showed a consistent relationship between the high- and low-frequency components of pursuit: high-frequency components were tracked with small phase lags, whereas low-frequency components were tracked with phase leads. The model was also trained to track targets moving along a circular trajectory with infrequent right-angle perturbations that moved the target along a circle meridian. Before the perturbation, the model tracked the target with very small phase differences that averaged 5 ms. After the perturbation, the model overshot the target while continuing along the expected nonperturbed circular trajectory for 80 ms, before it moved toward the new perturbed trajectory. Monkeys showed similar behaviors with an average phase difference of 3 ms during circular pursuit, followed by a perturbation response after 90 ms. In both cases, the delays required to process visual information were much longer than delays associated with nonperturbed circular and sum-of-sines pursuit. This suggests that both the model and the eye make short-term predictions about future events to compensate for visual feedback delays in receiving information about the direction of a target moving along a changing trajectory. In addition, both the eye and the model can adjust to abrupt changes in target direction on the basis of visual feedback, but do so after significant processing delays.

A Numerical Analysis of Phonation Using a Two-Dimensional Flexible Channel Model of the Vocal Folds

2001 ◽  
Vol 123 (6) ◽  
pp. 571-579 ◽  
Author(s):  
Tadashige Ikeda ◽  
Yuji Matsuzaki ◽  
Tatsuya Aomatsu
Keyword(s):  
Channel Model ◽  
Flow Direction ◽  
Speech Sound ◽  
Three Dimensional ◽  
Vocal Folds ◽  
Two Dimensional ◽  
Threshold Values ◽  
One Dimensional ◽  
Frequency Components ◽  
Main Flow Direction

A two-dimensional flexible channel model of the vocal folds coupled with an unsteady one-dimensional flow model is presented for an analysis of the mechanism of phonation. The vocal fold is approximated by springs and dampers distributed in the main flow direction that are enveloped with an elastic cover. In order to approximate three-dimensional collision of the vocal folds using the two-dimensional model, threshold values for the glottal width are introduced. The numerical results show that the collision plays an important role in speech sound, especially for higher resonant frequency components, because it causes the source sound to include high-frequency components.

scholarly journals Resonating neurons stabilize heterogeneous grid-cell networks

2020 ◽  
Author(s):  
Divyansh Mittal ◽  
Rishikesh Narayanan
Keyword(s):  
Neural Activity ◽  
Biological Networks ◽  
Spatial Information ◽  
Physiological Mechanism ◽  
Low Frequency ◽  
Two Dimensional ◽  
Attractor Network ◽  
Network Function ◽  
Frequency Components ◽  
The Impact

ABSTRACTGrid cells in the medial entorhinal cortex manifest multiple firing fields, patterned to tessellate external space with triangles. Although two-dimensional continuous attractor network (CAN) models have offered remarkable insights about grid-patterned activity generation, their functional stability in the presence of biological heterogeneities remains unexplored. In this study, we systematically incorporated three distinct forms of intrinsic and synaptic heterogeneities into a rate-based CAN model driven by virtual trajectories, developed here to mimic animal traversals and improve computational efficiency. We found that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in neural activity. Quantitatively, grid score and spatial information associated with neural activity reduced progressively with increasing degree of heterogeneities, and perturbations were primarily confined to low-frequency neural activity. We postulated that suppressing low-frequency perturbations could ameliorate the disruptive impact of heterogeneities on grid-patterned activity. To test this, we formulated a strategy to introduce intrinsic neuronal resonance, a physiological mechanism to suppress low-frequency activity, in our rate-based neuronal model by incorporating filters that mimicked resonating conductances. We confirmed the emergence of grid-patterned activity in homogeneous CAN models built with resonating neurons and assessed the impact of heterogeneities on these models. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing, through suppression of low-frequency components in neural activity. Our analyses suggest a universal role for intrinsic neuronal resonance, an established mechanism in biological neurons to suppress low-frequency neural activity, in stabilizing heterogeneous network physiology.SIGNIFICANCE STATEMENTA central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. However, several theoretical and modeling frameworks employ unnatural homogeneous networks in assessing network function owing to the enormous analytical or computational costs involved in assessing heterogeneous networks. Here, we investigate the impact of biological heterogeneities on a powerful two-dimensional continuous attractor network implicated in the emergence of patterned neural activity. We show that network function is disrupted by biological heterogeneities, but is stabilized by intrinsic neuronal resonance, a physiological mechanism that suppresses low-frequency perturbations. As low-frequency perturbations are pervasive across biological systems, mechanisms that suppress low-frequency components could form a generalized route to stabilize heterogeneous biological networks.

Application of Two-Dimensional Continuous Wavelet Transform in Demodulating Fringe Pattern with Two Frequency Components

2010 ◽  
Vol 47 (7) ◽  
pp. 071001
Author(s):  
蔡义祥 Cai Yixiang ◽  
陈文静 Chen Wenjing ◽  
李思坤 Li Sikun ◽  
赵玥 Zhao Yue ◽  
许罗鹏 Xu Luopeng
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Fringe Pattern ◽  
Continuous Wavelet ◽  
Two Dimensional ◽  
Frequency Components

DIRECTIONAL SELECTION OF TWO-DIMENSIONAL COMPLEX DISCRETE WAVELET TRANSFORM AND ITS APPLICATION TO IMAGE PROCESSING

2010 ◽  
Vol 08 (04) ◽  
pp. 659-676 ◽  
Author(s):  
ZHONG ZHANG ◽  
NARIYA KOMAZAKI ◽  
TAKASHI IMAMURA ◽  
TETSUO MIYAKE ◽  
HIROSHI TODA
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Directional Selection ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Directional Filter ◽  
Sampling Process ◽  
Frequency Components ◽  
Selection Of

In this study, a novel direction selection method using the two-dimensional complex discrete wavelet transform (2D-CDWT) is proposed. In order to achieve arbitrary direction selection, the directional filters are first designed. Calculation procedure of directional selection can be shown as follows: (1) The 16 sub-images are generally generated from the original image by the 2D-CDWT without a down-sampling process and the 12 sub-images that correspond to the high-frequency components are selected. (2) The 12 sub-images are filtered by using the designed directional filter. (3) The down-sampling process is carried out and the resulting images are obtained. Furthermore, this method is applied to the surface analysis of a wafer, and it is confirmed that our method is effective in detecting irregular direction components.

Inductance analytical calculations of brushless surface-mounted permanent-magnet machines based on energy method

2019 ◽  
Vol 38 (2) ◽  
pp. 536-556 ◽  
Author(s):  
Fatemeh Ebadi ◽  
Mohammad Mardaneh ◽  
Akbar Rahideh
Keyword(s):  
Magnetic Field ◽  
Permanent Magnet ◽  
Energy Method ◽  
Design Methodology ◽  
Permanent Magnet Machines ◽  
Two Dimensional ◽  
Permanent Magnet Motors ◽  
Flux Linkage ◽  
Content Type ◽  
Linkage Method

Purpose This paper aims to show the proposed energy method for inductance calculation is valid for any number of poles, phases and any winding layout. Design/methodology/approach A two-dimensional (2-D) analytical energy-based approach is presented to calculate self-inductances and mutual inductances of brushless surface-mounted permanent-magnet machines. Findings The proposed calculation procedure is valid for brushless permanent-magnet machines with slotted or slotless stator structure. Comparisons between energy method and flux linkage method are presented based on simulation and experimental results. It shows that the energy method has an excellent agreement with the result obtained from finite element method (FEM) and experimental study. Originality/value This paper compares energy-based method with flux linkage method and FEM for inductance calculations in slotless and slotted permanent-magnet motors. The relations for inductance calculation are presented which are obtained based on 2-D analytical representation of magnetic field.

Spectrophotometric measurements of objective-prism spectra

1966 ◽  
Vol 24 ◽  
pp. 118-119
Author(s):  
Th. Schmidt-Kaler
Keyword(s):  
Progress Report ◽  
Two Dimensional ◽  
Objective Prism ◽  
Made In

I should like to give you a very condensed progress report on some spectrophotometric measurements of objective-prism spectra made in collaboration with H. Leicher at Bonn. The procedure used is almost completely automatic. The measurements are made with the help of a semi-automatic fully digitized registering microphotometer constructed by Hög-Hamburg. The reductions are carried out with the aid of a number of interconnected programmes written for the computer IBM 7090, beginning with the output of the photometer in the form of punched cards and ending with the printing-out of the final two-dimensional classifications.

Sign in / Sign up

Export Citation Format

Share Document