Optimizing the Switching Speed of the Current Probe Utilizing the FPGA for Input Signal Processing

2021 ◽  
Author(s):  
Michal Mikulasek ◽  
Pavel Masek ◽  
Martin Stusek ◽  
Lukas Novak ◽  
Radek Mozny ◽  
...  
Keyword(s):  
Signal Processing ◽  
Input Signal ◽  
Switching Speed

scholarly journals Prototyping a System for Truck Differential Lock Control

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3619 ◽  
Author(s):  
Pavel Kučera ◽  
Václav Píštěk
Keyword(s):  
Signal Processing ◽  
Input Signal ◽  
Control Algorithm ◽  
Main Part ◽  
Proper Function ◽  
Mechatronic System ◽  
Important Benefit ◽  
Differential Lock ◽  
The Many ◽  
Prototype Testing

The article deals with the development of a mechatronic system for locking vehicle differentials. An important benefit of this system is that it prevents the jamming of the vehicle in difficult adhesion conditions. The system recognizes such a situation much sooner than the driver and is able to respond immediately, ensuring smooth driving in off-road or snowy conditions. This article describes the control algorithm of this mechatronic system, which is designed for firefighting, military, or civilian vehicles with a drivetrain configuration of up to 10 × 10, and also explains the input signal processing and the control of actuators. The main part of this article concerns prototype testing on a vehicle. The results are an evaluation of one of the many experiments and monitor the proper function of the developed mechatronic system.

A Humidity Measurement Method Based on Comb-Shaped Sensor

2013 ◽  
Vol 325-326 ◽  
pp. 827-831
Author(s):  
Xuan Zhe Wang ◽  
Man Man Liu ◽  
Shan Ting Ding ◽  
Hao Wei Wang
Keyword(s):  
Signal Processing ◽  
Input Signal ◽  
Measurement Method ◽  
Capacitive Sensor ◽  
Corrosion Monitoring ◽  
Atmospheric Humidity ◽  
Humidity Measurement ◽  
Noise Impact ◽  
Humidity Monitoring ◽  
Important Significance

Humidity measurement is an important link of corrosion monitoring in aircrafts and tanks. But the traditional monitor systems are not portable, can’t be real-time monitoring and other shortcomings. In order to solve these problems, a method that using a new sensor -- comb-shaped capacitive sensor in the humidity monitoring has been proposed. This method is based on electrical theory, combined with the processing of circuit, including the generation of the input signal, signal processing, and acquisition of output signal, and finally analyze the data collected to achieve the purpose of the monitoring of humidity.The problem of how to reduce the noise impact, how to simplify the theoretical calculation and other problems are considered and resolved in the design of the system.Compared with the traditional methods, the system based on this method is simple and compact because of its simple on the hardware design and the characteristics of the sensor. The experimental results show that this method has an very important significance in the atmospheric humidity monitoring.

Discrete Electronic Warfare Signal Processing using Compressed Sensing Based on Random Modulator Pre-Integrator

2015 ◽  
Vol 65 (6) ◽  
pp. 472 ◽  
Author(s):  
M. Sreenivasa Rao ◽  
Chandan C. Mishra ◽  
K. Krishna Naik ◽  
K. Maheshwara Reddy
Keyword(s):  
Signal Processing ◽  
Input Signal ◽  
High Speed ◽  
Traditional Approach ◽  
Ultra Wideband ◽  
Radar Signal ◽  
Electromagnetic Spectrum ◽  
Electronic Warfare ◽  
High Speed Adc ◽  
Detection Of Signals

Electronic warfare receiver works in the wide electromagnetic spectrum in dense radar signal environment. Current trends in radar systems are ultra wideband and low probability of intercept radar technology. Detection of signals from various radar stations is a concern. Performance and probability of intercept are mainly dependent on high speed ADC technology. The sampling and reconstruction functions have to be optimized to capture incoming signals at the receiver to extract characteristics of the radar signal. The compressive sampling of the input signal with orthonormal base vectors, projecting the basis in the union of subspaces and recovery through convex optimisation techniques is the current traditional approach. Modern trends in signal processing suggest the random modulator pre-integrator (RMPI), which sample the input signal at information rate non-adaptively and recovery by the processing of discrete and finite vectors. Analysis of RMPI theory, application to EW receiver, simulation and recovery of EW receiver signals are discussed.

scholarly journals THE USE OF DIGITAL SIGNAL PROCESSING AND A NEURAL NETWORK WHEN GENERATING A FORECAST OF TIME SERIES OF DATA FOR THE PURPOSE OF DETECTING ANOMALIES IN THE IN THE AUTOMATED CONTROL OF TECHNOLOGICAL PROCESSES

2020 ◽  
Vol 20 (1) ◽  
pp. 24-34
Author(s):  
A. N. Ragozin ◽  
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Input Signal ◽  
Forecast Error ◽  
Digital Signal ◽  
Processing Unit ◽  
Automated Control ◽  
Prediction Result ◽  
Low Pass

n order to detect anomalies and improve the quality of forecasting dynamic data flows observed from sensors in Industrial Control System (ACS)., it is proposed to use a predictive mod-ule consisting of a series-connected digital signal processing unit (DSP) and a predictive unit using a neural network (predictive autoencoder ( Auto Encoder), predictive Autoencoder (PAE)). The study showed that the preliminary DSP block of the predicted input signal, consisting of a parallel set (comb) of digital low-pass filters with finite impulse responses (FIR-LPF), leads to a non-equilibrium account of the correlation relationships of the time samples of the input signal and to increase the accuracy of the final prediction result. The predicted autoencoder (PAE) pro-posed and considered in the work, in addition to restoring the input signal or part of the input signal at the PAE output, also generates the predicted samples of the input signal for the speci-fied number of «forward» time steps at the output, which increases the accuracy of the predic-tion result. The reduction of the forecast error occurs due to the imposition of restrictions in the formation of the forecast, that is, an additional requirement to restore the input samples of the samples – «stabilizers» at the NS output. The introduction of «stabilizers» increases the accuracy of the prediction result.

MATHEMATICAL IMPLEMENTATION OF HYBRID FAST FOURIER TRANSFORM AND DISCRETE WAVELET TRANSFORM FOR DEVELOPING GRAPHICAL USER INTERFACE USING VISUAL BASIC FOR SIGNAL PROCESSING APPLICATIONS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240031 ◽  
Author(s):  
MOUSA K. WALI ◽  
M. MURUGAPPAN ◽  
R. BADLISHAH AHMMAD
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Input Signal ◽  
Wavelet Decomposition ◽  
Wavelet Coefficient ◽  
Visual Basic ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
End User ◽  
Decomposition Level

In recent years, the application of discrete wavelet transform (DWT) on biosignal processing has made a significant impact on developing several applications. However, the existing user-friendly software based on graphical user interfaces (GUI) does not allow the freedom of saving the wavelet coefficients in .txt or .xls format and to analyze the frequency spectrum of wavelet coefficients at any desired wavelet decomposition level. This work describes the development of mathematical models for the implementation of DWT in a GUI environment. This proposed software based on GUI is developed under the visual basic (VB) platform. As a preliminary tool, the end user can perform "j" level of decomposition on a given input signal using the three most popular wavelet functions — Daubechies, Symlet, and Coiflet over "n" order. The end user can save the output of wavelet coefficients either in .txt or .xls file format for any further investigations. In addition, the users can gain insight into the most dominating frequency component of any given wavelet decomposition level through fast Fourier transform (FFT). This feature is highly essential in signal processing applications for the in-depth analysis on input signal components. Hence, this GUI has the hybrid features of FFT with DWT to derive the frequency spectrum of any level of wavelet coefficient. The novel feature of this software becomes more evident for any signal processing application. The proposed software is tested with three physiological signal — electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) — samples. Two statistical features such as mean and energy of wavelet coefficient are used as a performance measure for validating the proposed software over conventional software. The results of proposed software is compared and analyzed with MATLAB wavelet toolbox for performance verification. As a result, the proposed software gives the same results as the conventional toolbox and allows more freedom to the end user to investigate the input signal.

Sign in / Sign up

Export Citation Format

Share Document