scholarly journals Digital Ultrasonic Sensing Device with Programmable Frequency: Development and Analysis

2021 ◽  
Vol 1 (1) ◽  
pp. 33-45
Author(s):  
Manish Man Shrestha ◽  
Bibek Ropakheti ◽  
Uddhav Bhattarai ◽  
Ajaya Adhikari ◽  
Shreeram Thakur
Keyword(s):  
Signal Processing ◽  
Ultrasonic Wave ◽  
Analog Circuit ◽  
Damage Index ◽  
Digital Signal ◽  
Ultrasonic Waves ◽  
Complex Signal ◽  
Ultrasonic Sensing ◽  
Low Pass ◽  
Signal Processing Algorithms

Ultrasonic wave is widely used in Structure Health Monitoring (SHM) systems. A piezoelectric transducer (PZT) is one of the most widely used sensors to acquire the structure's ultrasonic wave. As today's world is digital, it is necessary to digitize the traditional analog PZT sensing system. This paper describes the development and analysis of a digital ultrasonic sensing device (DUSD) for PZT sensors. We removed the complexities of the analog circuit by interfacing the microcontroller directly with the charge amplifier circuit. The microcontroller used in this research is a 32-bit ARM Cortex-M4 with in-built FPU (Floating Point Unit) and DSP (Digital signal processing) instructions. These features make it possible to compute complex signal processing algorithms and methods in the controller itself. The developed sensing device can communicate with the user and other devices using Universal Asynchronous Receiver/Transmitter (UART). The user can select cut-off frequencies of both high pass filters (HPF) and low pass filters (LPF) as well as types of data (ultrasonic waves, damage index) that the user wishes to collect from the device. To illustrate the proficiencies of the device, the ultrasonic wave was collected and evaluated to detect the damage in the test specimen.

scholarly journals Software and hardware solution for digital signal processing algorithms testing

2019 ◽  
Vol 124 ◽  
pp. 03006
Author(s):  
M. V. Talanov ◽  
V. M. Talanov
Keyword(s):  
Signal Processing ◽  
Control System ◽  
Digital Signal Processing ◽  
Control Systems ◽  
System Simulation ◽  
Digital Signal ◽  
Drive Control ◽  
Signal Processing Algorithms ◽  
Software And Hardware ◽  
Processing Algorithms

The article describes the microprocessor system for various digital signal processing algorithms testing. The development of electric drive control systems is carried out with the usage of modeling systems such as, MATLAB/Simulink. Modern digital control systems are based on specialized digital signal microcontrollers. The present market offers evaluation boards, for example STM32F4DISCOVERY, which enables to connect a microcontroller to a personal computer. It makes possible to use the microcontroller as a part of the mathematical model of the control system. However, the designing of the control system simulation model and the program for the microprocessor is carried out in different programming environments. Thus, the software and hardware solution for testing programs for the microprocessor, which is a part of the control system, is relevant. This article deals with the designing of the modeling method in which the prototype program for the microprocessor is debugged as a part of the electric drive control system simulation model.

Discrete Time Signal Processing Framework with Support Vector Machines

2011 ◽  
pp. 150-178
Author(s):  
José Luis Rojo-Álvarez ◽  
Manel Martínez-Ramón ◽  
Gustavo Camps-Valls ◽  
Carlos E. Martínez-Cruz ◽  
Carlos Figuera
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Reproducing Kernel ◽  
Digital Signal ◽  
Feature Space ◽  
Time Signal ◽  
Support Vector ◽  
Straightforward Application ◽  
Signal Processing Algorithms ◽  
Signal Models

Digital signal processing (DSP) of time series using SVM has been addressed in the literature with a straightforward application of the SVM kernel regression, but the assumption of independently distributed samples in regression models is not fulfilled by a time-series problem. Therefore, a new branch of SVM algorithms has to be developed for the advantageous application of SVM concepts when we process data with underlying time-series structure. In this chapter, we summarize our past, present, and future proposal for the SVM-DSP frame-work, which consists of several principles for creating linear and nonlinear SVM algorithms devoted to DSP problems. First, the statement of linear signal models in the primal problem (primal signal models) allows us to obtain robust estimators of the model coefficients in classical DSP problems. Next, nonlinear SVM-DSP algorithms can be addressed from two different approaches: (a) reproducing kernel Hilbert spaces (RKHS) signal models, which state the signal model equation in the feature space, and (b) dual signal models, which are based on the nonlinear regression of the time instants with appropriate Mercer’s kernels. This way, concepts like filtering, time interpolation, and convolution are considered and analyzed, and they open the field for future development on signal processing algorithms following this SVM-DSP framework.

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