Method of successive restoration of the input signal of an instrument

1993 ◽  
Vol 36 (5) ◽  
pp. 508-510
Author(s):  
K. A. Kazlauskas
Keyword(s):  
Input Signal ◽  
Successive Restoration

Input Signal Voltage Variation and its Effects on Non Invasive Bio-impedance Diagnosis

2011 ◽  
Vol 4 (2) ◽  
pp. 13-14
Author(s):  
Hari Krishnan G Hari Krishnan G ◽  
◽  
Dr. Ananda Natarajan R ◽  
Dr. Anima Nanda
Keyword(s):  
Input Signal ◽  
Non Invasive ◽  
Voltage Variation

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

Pulse-Width Modulated Amplifier for DC Servo System and Its Matlab Simulation

2015 ◽  
Vol 9 (1) ◽  
pp. 625-631
Author(s):  
Ma Xiaocheng ◽  
Zhang Haotian ◽  
Cheng Yiqing ◽  
Zhu Lina ◽  
Wu Dan
Keyword(s):  
Mathematical Model ◽  
Transfer Function ◽  
Input Signal ◽  
Pulse Width ◽  
Rotation Speed ◽  
Servo Motor ◽  
Matlab Simulation ◽  
Pulse Width Modulated ◽  
Signal Changes ◽  
The Relationship

This paper introduces a mathematical model for Pulse-Width Modulated Amplifier for DC Servo Motor. The relationship between pulse-width modulated (PWM) signal and reference rotation speed is specified, and a general model of motor represented by transfer function is also put forward. When the input signal changes, the rotation speed of the servo motor will change accordingly. By changing zeros and poles, transient performance of this system is discussed in detail, and optimal ranges of the parameters is recommended at the end of discussion.

Falsification of Hybrid Systems Using Adaptive Probabilistic Search

2021 ◽  
Vol 31 (3) ◽  
pp. 1-22
Author(s):  
Gidon Ernst ◽  
Sean Sedwards ◽  
Zhenya Zhang ◽  
Ichiro Hasuo
Keyword(s):  
Hybrid Systems ◽  
Input Signal ◽  
Tree Search ◽  
Single Trial ◽  
Decision Point ◽  
Directed Tree ◽  
Probabilistic Choice ◽  
Original Algorithm ◽  
Fine Grained ◽  
Probabilistic Search

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.

scholarly journals Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane

2020 ◽  
Vol 10 (15) ◽  
pp. 5257
Author(s):  
Nathan Berwick ◽  
Hyunkook Lee
Keyword(s):  
Transfer Function ◽  
Input Signal ◽  
Function Analysis ◽  
Median Plane ◽  
Speech Reception ◽  
Transfer Function Analysis ◽  
Spatial Unmasking ◽  
Head Related Transfer Function ◽  
Speech Reception Thresholds ◽  
Energy Weighting

This study examined whether the spatial unmasking effect operates on speech reception thresholds (SRTs) in the median plane. SRTs were measured using an adaptive staircase procedure, with target speech sentences and speech-shaped noise maskers presented via loudspeakers at −30°, 0°, 30°, 60° and 90°. Results indicated a significant median plane spatial unmasking effect, with the largest SRT gain obtained for the −30° elevation of the masker. Head-related transfer function analysis suggests that the result is associated with the energy weighting of the ear-input signal of the masker at upper-mid frequencies relative to the maskee.

scholarly journals Protein:Protein Interactions Control Sensitivity of a Transcription Response to Input Signal

2018 ◽  
Vol 114 (3) ◽  
pp. 69a
Author(s):  
Dorothy Beckett ◽  
Chenlu He ◽  
Jingheng Wang ◽  
Gregory Custer ◽  
Silvina Matysiak
Keyword(s):  
Input Signal ◽  
Protein:Protein Interactions
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