Detection of Transients in Vibration Signals using Reverse Dispersion Entropy

In this work, the reverse dispersion entropy (RDE) is used to process the squared envelope signal in order to detect nonstationarites. Based on the idea of spectral kurtosis (SK) and kurtogram, the squared envelope signal is first extracted by applying STFT to vibration signal. Then, as an alternative to negative Shannon entropy, the RDE is used to process the squared envelope to detect the range of frequencies at which the transients occur. The RDEgram color-coded map is used to represent the RDE values as a function of frequency and frequency resolution from which the ideal filter parameters can be inferred. Once, the best frequency and frequency bandwidth pair are found, an optimum FIR filter can be designed to filter the original vibration signal. The proposed method is tested against simulated and actual vibration signals and proved to be superior to existing methods.

Ideal and Technical Filters

Chapter 3 shows how the logic of noise reduction is anchored in historical discourses on sound and technology, taking a closer look at the development, from the early nineteenth century onward, of some of the most important concepts in physical acoustics and sound engineering. First, it discusses two uncertainty principles fundamental to information theory and communication engineering, which entail compromises that limit the accuracy of any reproduction. Second, it focuses on the mathematical principles of Fourier analysis, which gave rise to the now-familiar representation of sound in terms of a “spectrum” of singular frequencies or “sine waves.” The chapter thereby explores the difference between a timeless, mathematical plane of the ideal filter in which clear, noiseless reproduction always seems possible, and a physical domain of technical filters in which transience and noise haunt every transmission. The contrast between the two, in turn, highlights the important relation between noise and time.

Machine Learning Algorithm for Surface Quality Analysis of Friction Stir Welded Joint

The Friction Stir Welding process usually produces weld members of good quality compared to composite weld made with a standard welding process. However, there is a possibility of the formation of various defects if the input parameters are not properly selected. In the recent case study, an image-based feature recognition system using the Fourier conversion method which is a computer visual recognition tool is developed. Five types of filters like Ideal Filter, Butterworth Filter, Low Filter, Gaussian Filter, and High Pass Filter. The results showed that the high pass filter has more ability to detect surface defects compared to the other four filters. It has also been observed that the Ideal filter has a lot of distortions compared to the Gaussian Filter and the Butterworth Filter.

Design selective band antenna using coupling sidewall and multi resonator for wireless communications

Design and simulation of antenna with performance operating at bandwidth (4.5-6.5) GHz, with center frequency 5.5 GHz. This antenna constriction from two cylindrical antenna shapes and four cylindrical resonators with include iris to matching impedance, to enhancement sharp edge band and four tuned between coupling sidewall to removed distortions. Improving the frequency selection of the bandwidth requirement antenna is by adjusting the cylinder length of the antenna The proposed antenna is operating ideal filter because the edge of the band rejection is matched the edge of the bandwidth requirement which makes it perform well including broad edges for the band rejection and sharp edges of the bandwidth requirement together with a little insertion between two types and good return loss of the bandwidth requirement. CST software used to investigate and simulated results.

Procedure of definition of structure and parameters of multi-band matching circuits on the basis of intracavitary complex criterion for conformity to the ideal filter

The article gives the procedure of structurally parametric synthesis of multi-band frequency-selective circuits. A short characteristic of approaches to solve the problems of synthesis of the multi-band frequencyselective circuits based on the use of multifrequency resonators, frequency transformations and parametric transformations using numerical procedures of optimisation is given. The research presents a short description of three widely used estimation criteria of the characteristics of frequency-selective circuits: Taylor, Chebyshev and average-degree criteria. Expedients of an estimate of joint approximation of amplitude-frequency and phasefrequency characteristics of synthesized circuits to those of the ideal filter are shown. The criterion is suggested and the procedure is presented for structurally parametrical synthesis of multi-band matching circuits on the basis of intracavitary complex criterion of conformity to the ideal filter in a transmission band and interband Chebyshev criterion realized by numerical optimization of synthesized circuits relative to the chosen criterion for affinessy is given. With that, the multi-band circuit is represented in form of a ladder-type connection that, generally, consists of n different reactive resistances. The example of using the proposed procedure to solve the problem of structurally parametric synthesis of a two-band matching circuit is given. As a matching load we selected the first-type equivalent consisting of a serial connection of active resistance and capacity. In the example there is an additional requirement to ensuring interband frequency selectivity. Such requirement is ensured by introducing zero to the function of transmission of the synthesized circuit on an interband average geometric frequency. The requirement is implemented by introducing a parallel oscillating circuit to the first serial arm of the synthesized circuit. The structurally parametric synthesis of the two-band matching circuit selected in the example is carried out through Mathcad 15 software based on the integrated method of Levenberg-Marquardt optimization.