scholarly journals Performance of qpsk system in the presence of pulse interference and noisy carrier reference signal

2003 ◽  
Vol 1 (1) ◽  
pp. 123-130
Author(s):  
Mihajlo Stefanovic ◽  
Milan Milosevic ◽  
Dragana Pavlovic
Keyword(s):  
Linear Model ◽  
Gaussian Noise ◽  
System Performance ◽  
Carrier Phase ◽  
Error Probability ◽  
Reference Signal ◽  
Signal Extraction ◽  
Pulse Interference ◽  
Noise Pulse ◽  
First Order

The aim of this paper is the determining the system performance in detecting the QPSK signal. The error probability is determined when the signal, Gaussian noise, pulse interference and imperfect carrier phase recovery are taken into consideration. Phase locked loop, as the constituent part of the receiver, is used in providing the synchronization reference signal extraction, which is assumed to be imperfect. The obtained results are based on the PLL non-linear model of the first order, with the emphasis on the degradation in the system performance produced by the imperfect carrier signal extraction. The system performance is determined when the signal corrupted by pulse interference and Gaussian noise is applied at the input of the receiver.

Statistical method of steepest improvement of response

2018 ◽  
Vol 84 (11) ◽  
pp. 74-87
Author(s):  
V. B. Bokov
Keyword(s):  
Statistical Method ◽  
Linear Model ◽  
Response Prediction ◽  
Initial Experiment ◽  
First Order ◽  
Prediction Response ◽  
Conditional Extremum ◽  
Order Polynomial ◽  
Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

scholarly journals Influence of the Porosity of Polymer Foams on the Performances of Capacitive Flexible Pressure Sensors

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1968 ◽  
Author(s):  
Sylvie Bilent ◽  
Thi Hong Nhung Dinh ◽  
Emile Martincic ◽  
Pierre-Yves Joubert
Keyword(s):  
Experimental Data ◽  
Linear Model ◽  
Dielectric Material ◽  
Pressure Sensors ◽  
Volume Ratio ◽  
Measurement Range ◽  
Polymer Foams ◽  
First Order ◽  
Non Linear ◽  
Flexible Pressure Sensors

This paper reports on the study of microporous polydimethylsiloxane (PDMS) foams as a highly deformable dielectric material used in the composition of flexible capacitive pressure sensors dedicated to wearable use. A fabrication process allowing the porosity of the foams to be adjusted was proposed and the fabricated foams were characterized. Then, elementary capacitive pressure sensors (15 × 15 mm2 square shaped electrodes) were elaborated with fabricated foams (5 mm or 10 mm thick) and were electromechanically characterized. Since the sensor responses under load are strongly non-linear, a behavioral non-linear model (first order exponential) was proposed, adjusted to the experimental data, and used to objectively estimate the sensor performances in terms of sensitivity and measurement range. The main conclusions of this study are that the porosity of the PDMS foams can be adjusted through the sugar:PDMS volume ratio and the size of sugar crystals used to fabricate the foams. Additionally, the porosity of the foams significantly modified the sensor performances. Indeed, compared to bulk PDMS sensors of the same size, the sensitivity of porous PDMS sensors could be multiplied by a factor up to 100 (the sensitivity is 0.14 %.kPa−1 for a bulk PDMS sensor and up to 13.7 %.kPa−1 for a porous PDMS sensor of the same dimensions), while the measurement range was reduced from a factor of 2 to 3 (from 594 kPa for a bulk PDMS sensor down to between 255 and 177 kPa for a PDMS foam sensor of the same dimensions, according to the porosity). This study opens the way to the design and fabrication of wearable flexible pressure sensors with adjustable performances through the control of the porosity of the fabricated PDMS foams.

scholarly journals Sensitivity of the General Linear Model to Assumptions

2020 ◽  
Vol 6 (5) ◽  
pp. 21-25
Keyword(s):  
Linear Model ◽  
Gaussian Noise ◽  
Linear Models ◽  
General Linear Model ◽  
White Gaussian Noise ◽  
General Linear ◽  
Optimal Estimator ◽  
Significant Performance ◽  
Non Gaussian ◽  
Noise Models

Non-Gaussian noise often causes in significant performance abatement for systems which are designed using Gaussian assumption. This report challenges the question of General Linear Model with White Gaussian Noise assumption in order to define the sensitivity of the performance of an optimal estimator. Gaussian noise models provide an important role in many signal processing applications. The Laplacian and Uniform signal are two worthy examples of noise that can be compared to the White Gaussian Noise, though the sensitivity which can be compared with any non-Gaussian. White Gaussian Noise has been considered for General Linear Models and deviation from whiteness would affect on our estimates under different circumstances. Moreover, new assumptions have been considered to generate different type of signals in order to evaluate the sensitivity of the General Linear Model.

Development of Solar-Powered Microcontroller-Relay-Based Control System Omnidirectional Wheelchair

2021 ◽  
pp. 181-191
Author(s):  
Patrick Uche Okafor ◽  
Ndidi Stella Arinze ◽  
Osondu Ignatius Onah ◽  
Ebenezer Nnajiofo Ogbodo
Keyword(s):  
Power Supply ◽  
System Performance ◽  
Performance Test ◽  
Reference Signal ◽  
Control Mechanisms ◽  
Solar Module ◽  
Dc Motors ◽  
Solar Powered ◽  
Physically Challenged ◽  
Test Result

A solar-powered omnidirectional wheelchair is implemented for physically challenged persons. The framework was mounted on the wheels that were connected with two direct current (DC) motors. The ratings of the battery and solar module were determined using system voltage (12V). A 7,805-voltage regulator was used to supply 5VDC to the AT89352 microcontroller. The microcontroller was programmed to provide a reference signal to the motor. The motor provides the needed torque to drive the wheels through interconnected relays. The relays are energized by the microcontroller and omnidirectional movement achieved through relays connected with microprocessor and micro switches, eliminating the need for joysticks and complex control mechanisms. System performance test result showed that the auxiliary solar power supply of the wheelchair increased the travel range by approximately 86% compared with that of a wheelchair powered by battery alone.

Parameter Estimation of Co-Channel AIS Signal Using Ambiguity Function

2014 ◽  
Vol 644-650 ◽  
pp. 4035-4039
Author(s):  
Hao Su Zhou ◽  
Jian Xin Wang
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Reference Signal ◽  
Two Dimensions ◽  
Ambiguity Function ◽  
Weak Signal ◽  
Strong Signal ◽  
Estimated Parameters ◽  
Simulation Results

A new data-aided algorithm for parameter estimation of the co-channel AIS signal transmitted over the additive white Gaussian noise channel is proposed in this paper. The co-channel signal consists of a strong signal with high power and a weak signal with low power. The parameters of the strong signal are estimated by searching the ambiguity function of the co-channel signal in two dimensions. A reference signal is therefore reconstructed with the estimated parameters and the aided data. By removing the ambiguity function of the reconstructed reference signal from that of the original co-channel signal, a new co-channel signal ambiguity function is obtained, from which the parameters of the weak signal are estimated. The simulation results illustrate that the proposed algorithm can estimate the parameters of the co-channel AIS signal effectively.

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