Phase response characteristics of model neurons determine which patterns are expressed in a ring circuit model of gait generation

1997 ◽  
Vol 77 (6) ◽  
pp. 367-380 ◽  
Author(s):  
C.C. Canavier ◽  
R.J. Butera ◽  
R.O. Dror ◽  
D.A. Baxter ◽  
J.W. Clark ◽  
...  
Keyword(s):  
Circuit Model ◽  
Phase Response ◽  
Gait Generation ◽  
Response Characteristics

scholarly journals Numerical calculation and experimental study on response characteristics of pneumatic solenoid valves

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1382-1393 ◽  
Author(s):  
Xiang Zhang ◽  
Yonghua Lu ◽  
Yang Li ◽  
Chi Zhang ◽  
Rui Wang
Keyword(s):  
Switching Time ◽  
Response Characteristic ◽  
Test System ◽  
Solenoid Valve ◽  
Circuit Model ◽  
Inlet Pressure ◽  
Driving Voltage ◽  
Response Characteristics ◽  
Aerodynamic Model ◽  
Set Up

In order to analyze the response characteristics of the solenoid valve in depth, the flow field of the solenoid valve is analyzed by means of the computational fluid dynamics, and the aerodynamic parameters that are difficult to be obtained by the traditional methods are obtained with software FLUENT. We also set up the mathematical model of the solenoid valve, including the aerodynamic model, the circuit model, the magnetic circuit model and the mechanical motion model. The calculation is completed in the Simulink, and the results of the calculation are analyzed. A set of the solenoid valve response characteristic test system is built, and the response characteristic parameters such as response time and maximum action frequency of the solenoid valve are tested. The experimental results are verified by comparing them with the simulation results. The final result shows that the response characteristics are basically irrelevant to the action frequency at a suitable working frequency. The open switching time of the solenoid valve decreases with the increase in the inlet pressure and the driving voltage and increases with the increase in the number of coil turns. The close switching time increases with the increase in the inlet pressure, the driving voltage and the number of coil turns.

A Circuit Model of Quantum Cascade Laser Based on a Numerical Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 61-65 ◽  
Author(s):  
Jie Yang ◽  
Wei Zhou ◽  
Xiang Li ◽  
Jie Wu ◽  
Su Jing Xia
Keyword(s):  
Numerical Calculation ◽  
Numerical Analysis ◽  
Quantum Cascade Laser ◽  
Circuit Simulation ◽  
Equivalent Circuit Model ◽  
Circuit Model ◽  
Quantum Cascade ◽  
Response Characteristics ◽  
Emission Effect ◽  
Frequency Response Characteristics

Based on the dynamic analysis and numerical computation of quantum cascade laser(QCL), a simple equivalent circuit model of QCL was established, in which we considered the spontaneous emission effect for QCL's start delay. The direct current(DC), transient and frequency response characteristics of QCL were obtained by means of the circuit simulation of PSPICE program, and some relevant parameters that may affect the QCL performance have been analyzed. The results of simulation are consistent with that of numerical calculation in previous literatures.

Phase-response characteristics of the transfer function of a spatially limited electrochemical cell

2007 ◽  
Vol 43 (12) ◽  
pp. 1358-1363 ◽  
Author(s):  
E. V. Egorov ◽  
V. A. Kozlov ◽  
A. V. Yashkin
Keyword(s):  
Transfer Function ◽  
Electrochemical Cell ◽  
Phase Response ◽  
Response Characteristics

Phase response characteristics of sinoatrial node cells

2007 ◽  
Vol 37 (1) ◽  
pp. 8-20 ◽  
Author(s):  
D.G. Tsalikakis ◽  
H.G. Zhang ◽  
D.I. Fotiadis ◽  
G.P. Kremmydas ◽  
Ł.K. Michalis
Keyword(s):  
Sinoatrial Node ◽  
Phase Response ◽  
Response Characteristics ◽  
Sinoatrial Node Cells

Creating Filters With Arbitrary Response Characteristics for Use in Hearing and Speech Research

1996 ◽  
Vol 39 (2) ◽  
pp. 390-395 ◽  
Author(s):  
James Hillenbrand ◽  
Robert A. Houde
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Phase Response ◽  
Response Curves ◽  
Simple Method ◽  
Response Characteristics ◽  
Signal Sample ◽  
Impulse Response Filter ◽  
Speech Research

Digital filters with conventional lowpass, highpass, bandpass, and band reject frequency response curves are perfectly adequate for many research applications in speech and hearing. However, there are some specialized applications for which these conventional response characteristics are less than ideal. In this paper we describe a simple method for generating digital filters with virtually any amplitude and phase response. The process involves (a) calculating the impulse response of a finite impulse response filter from a text file that specifies the desired magnitude and phase response of the filter, and (b) convolving the impulse response with the input signal. Sample applications of this method are described.

Phase-Response Analysis of Stretch-Mediated Beat Coordination in the Oyster Heart. I. Phase-Response Characteristics of Auricle and Ventricle to Brief Stretches

1995 ◽  
Vol 12 (4) ◽  
pp. 397-403 ◽  
Author(s):  
Hiroyuki Uesaka ◽  
Hiroshi Yamagishi ◽  
Arinobu Ebara
Keyword(s):  
Phase Response ◽  
Response Analysis ◽  
Response Characteristics

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

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