MATLAB Signal Processing Research Articles

Modal Data Identification of the Prestressed Concrete Bridge Using Variational Mode Decomposition

In this study, the VMD algorithm was used to identify the modal characteristics of the structure using the decomposition of the acceleration responses recorded by the sensors. This algorithm has advantages over other signal decomposition methods that is resistant to the noise and sampling frequency. Also, the VMD algorithm extracts the natural frequencies of the structure concurrently. The damping ratios of the structure were estimated by fitting a linear function to the logarithmic diagram of the modal response in the decaying amplitude and calculating the slope of this line. The efficiency and accuracy of this algorithm were investigated by decomposing the acceleration responses obtained from the sensors installed on a prestressed concrete bridge that is located under the load of passing vehicles. The VMD algorithm was used for signal processing in MATLAB to estimate the natural frequencies, damping ratios and mode shapes of the bridge and ARTeMIS was utilized for verifying the results. The finite element modeling and modal analysis of the bridge were performed in ABAQUS and the natural frequencies and mode shapes of the bridge were obtained. The results showed that the mode shapes estimated by the VMD algorithm was in good agreement with the finite element model and ARTeMIS. Also, the damping ratios estimated by this algorithm were obtained close to the damping value of the prestressed concrete bridge. The difference between the frequencies calculated by the VMD algorithm and ARTeMIS was about 1%, and the difference with the finite element model frequencies was close to 5%.

Transmission line fault detection and classification using cross-correlation and k-nearest neighbor

A method for detecting and classifying transmission line faults using cross-correlation andk-Nearest Neighbor (k-NN) has been presented in this article. A unique analogy between the cross-correlogram obtained from the sound phase and a faulty phase in an electric power system, defined here as the fault correlogram, and a normal electrocardiogram (ECG) of human heart has been validated in the proposed work. The proposed method uses synthetic fault data within half cycle of pre-fault and half cycle of post-fault to detect and classify the different faults under varying fault parameters. EMTP/ATP software has been used as the platform to carry out simulation of the power system network followed by signal processing in MATLAB.

IR-UWB-based chipless RFID system

This paper presents a chipless radio frequency identification (RFID) system, based on impulse radio ultra-wideband (IR-UWB) technology. This UWB-RFID system consists of a transmitter, a receiver, two tapered slot antennas, and several chipless tags. Covering the lower UWB frequency band in 3–5 GHz, the transmitter integrated circuit generates signals with high peak-to-peak amplitude of 6.6 V and short pulse duration of 1 ns. The receiver front end consists of amplifiers and filters, with total front-end gain up to 36 dB. This is followed by an equivalent time sampling analog to digital converter to sample the equivalent RF signals directly followed by digital signal processing in MATLAB. The tapered slot antenna has a high and relatively flat gain of 10 dBi. The chipless tag is uniplanar and with a size of 23 × 23 × 0.508 mm3 only. Based on these hardware designs developed in our research group, a novel method is proposed to separate two overlapped time-domain signals. This method, which is experimentally validated in this paper, greatly simplifies the hardware designs, especially the chipless tag. This chipless UWB-RFID system is an excellent candidate to realize a low-cost, low-power, and high-performance system for next-generation RFID applications.

Wavelet Based Some Julia Sets of Rational Maps Having Zhukovskii Function

The dynamics of rational maps and their properties are interesting because of the presence of poles and zeros. In this paper we have computed Julia sets of rational maps having Zhukovskii Function for which the double of the first derivative has no Herman rings. The data points out of the Julia set in Matlab workspace were imported to Matlab Signal Processing Tool for their analysis. We have sampled the data points with the sampling frequency of 8192 Hz and obtained complex signals. We have then applied the band pass filter to these complex signals. The effect of the band pass filter has generated complex analogue modulated signals.

Analogue Wavelet Transform Based the Predicted Imaginary Part of the Dynamics of Rational Map Having Zeros

Significant work has already been done for complex quadratics. However, the dynamics of rational functions and their properties are equally interesting. In this paper we have generated computer images from a C++ computer program. We have then developed an artificial neural network model using predictive modeling software based on RMS type of error out of two samples of points obtained from the generated images. The imaginary part of sample II was predicted by applied the real parts of sample I and sample II to the artificial neural network. The real part of sample II was more important than the real part of sample I in predicting the imaginary part of sample II. The predicted imaginary part of sample II was then imported to Matlab Signal Processing Tool (SPTool) via Matlab workspace. We have applied a stable band pass filter to the model to eliminate noise from it for its analysis. A modulated signal produced reveals that the methodology used shall be applied to explore properties of computer generated images from the generated wavelet. We have further imported the predicted imaginary part of sample II to autoSIGNAL software for time and frequency range analysis of the continuous wavelet transform.

SignalCAD: A Digital Signal Processing Software for Forced and Ambient Vibration Testing of Engineering Structures

Abstract Commercial tools for measurement and analysis of vibration signals have traditionally been very expensive. In the last decade, however, multi-channel measurement systems have become relatively inexpensive. The analysis functionality in most inexpensive instruments is limited. Therefore, many companies are using alternatives for post processing of measurement results. MATLAB is a platform that is popular for this purpose and which offers many advantages over dedicated menu driven systems. The open functions in MATLAB assure flexibility and the possibility to modify functions for specific needs. In this paper, the presentation and numerical applications of an interactive and comparative digital signal processing software developed in MATLAB by writers is described. The software is named as SignalCAD. For developing SignalCAD, about 120 new functions have been created and are used with MATLAB Signal Processing Toolbox functions. The SignalCAD program is a powerful tool that deals with processing raw measured data obtained from forced and ambient vibration testing of engineering structures. SignalCAD offers extensive functionalities for the visualization and processing of the measurement data and the determination and visualization of the spectral analysis results. The program disposes of a graphical user interface, which what makes it very intuitive and easy to handle. The most common spectral analysis techniques have been used in SignalCAD. The possibilities of the program are demonstrated with a three dimensional steel frame model vibration test and some results are compared with commercial PULSE signal analysis software.

Improved narrowband low-pass IIR filters in fixed-point systems [DSP Tips & Tricks

Due to their resistance to quantized-coefficient errors, traditional second-order infinite impulse response (IIR) filters are the fundamental building blocks in computationally efficient high-order IIR digital filter implementations. However, when used in fixed-point number systems, the inherent properties of quantized-coefficient second-order IIR filters do not readily permit their use in narrowband low-pass filtering applications. Narrowband low- pass IIR filters have traditionally had a bad reputation; for example, MATLAB's Signal Processing Toolbox documentation warns: "All classical IIR low-pass filters are ill conditioned for extremely low cutoff frequencies." This article presents a neat trick that can be used to overcome the shortcomings of narrowband second-order low- pass IIR filters, with no increase in filter coefficient bit widths and no increase in the number of filter multiplies per output sample.

Adaptive Filter Application in Echo Cancellation System and Implementation using FPGA

In telephony system, the received signal by the loudspeaker, is reverberated through the environment and picked up by the microphone. It is called an echo signal. Which is in the form of time delayed and attenuated image of original speech signal, and causes a reduction in the quality of the communication. Adaptive filters are a class of filters that iteratively alter their parameters in order to minimize a difference between a desired output and their output. In the case of acoustic echo, the optimal output is an echoed signal that accurately emulates the unwanted echo signal. This is then used to negate the echo in the return signal. The better the adaptive filter simulates this echo, the more successful the cancellation will be. This paper examines LMS algorithm of adaptive filtering and the application in acoustic echo cancellation system. Employing a discrete signal processing in Matlab for simulation with real acoustic signals. Also a hardware implementation of an adaptive filter have been developed using XC3S500E Xilinx FPGA chip, and VHDL language on RTL abstraction level. Keywords: Acoustic echo cancellation, Adaptive Filter, FPGA, VHDL.

Identification of early caries in human tooth using histogram and power spectral analysis

This paper deals with the detection of caries in its early stage using histogram and power spectral analysis. The x-ray images of normal and decayed teeth of individuals are analysed using MATLAB signal processing tool. For each image the histogram and power spectrum are calculated. It is found that, in a histogram the pixel intensities are concentrated in different ranges for normal and decayed teeth. It is also found that there are distinct differences in the spectral components obtained, between normal and decayed teeth. The spectrum of a decayed tooth has high frequency components compared to the spectrum of the normal tooth.

Telescoping Languages: A System for Automatic Generation of Domain Languages

The software gap - the discrepancy between the need for new software and the aggregate capacity of the workforce to produce it - is a serious problem for scientific software. Although users appreciate the convenience (and, thus, improved productivity) of using relatively high-level scripting languages, the slow execution speeds of these languages remain a problem. Lower level languages, such as C and Fortran, provide better performance for production applications, but at the cost of tedious programming and optimization by experts. If applications written in scripting languages could be routinely compiled into highly optimized machine code, a huge productivity advantage would be possible. It is not enough, however, to simply develop excellent compiler technologies for scripting languages (as a number of projects have succeeded in doing for MATLAB). In practice, scientists typically extend these languages with their own domain-centric components, such as the MATLAB signal processing toolbox. Doing so effectively defines a new domain-specific language. If we are to address efficiency problems for such extended languages, we must develop a framework for automatically generating optimizing compilers for them. To accomplish this goal, we have been pursuing an innovative strategy that we call telescoping languages. Our approach calls for using a library-preprocessing phase to extensively analyze and optimize collections of libraries that define an extended language. Results of this analysis are collected into annotated libraries and used to generate a library-aware optimizer. The generated library-aware optimizer uses the knowledge gathered during preprocessing to carry out fast and effective optimization of high-level scripts. This enables script optimization to benefit from the intense analysis performed during preprocessing without repaying its price. Since library preprocessing is performed only at infrequent language-generation times, its cost is amortized over many

Chebyshev digital FIR filter design

A new multiple-exchange ascent algorithm is presented for designing optimal Chebyshev digital FIR filters with arbitrary magnitude and phase specifications. Compared to existing Chebyshev design techniques, the new design algorithm exhibits faster convergence while maintaining high accuracy, and is guaranteed to converge to the optimal solution. In addition, the proposed algorithm exactly reduces to the classic second Remez (Parks–McClellan) algorithm when real-only or imaginary-only filters are designed and is, therefore, a generalization of the classic Remez algorithm to the complex case. The described algorithm has been incorporated as part of the MATLAB signal processing toolbox. Design examples are presented to illustrate the performance of the proposed algorithm.