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Frequency-Domain Models
Published in Clarence W. de Silva, Modeling of Dynamic Systems with Engineering Applications, 2023
The Fourier transform involves the mathematical transformation from the time domain into the frequency domain) according to Y(jω)=∫−∞∞y(t)exp(−jωt)dtorY(jω)=Fy(t)
Designing Tall Buildings with Natural Materials
Published in Graham A. Ormondroyd, Angela F. Morris, Designing with Natural Materials, 2018
Richard Harris, Wen-Shao Chang
Analysis of measured data can fall into two main categories, frequency domain analysis and time domain analyses. Frequency domain analysis usually involves the use of a fast Fourier transform (FFT) of the data and then peak picking to determine the natural frequency of the structure. This is a fast and simple way to determine both the natural frequency and damping ratio. However, when the measured data involves significant amount of noise, it is less reliable. There are several ways to deal with data in time domain; the one that often used to extract the dynamic properties of tall timber buildings is the use of curve fitting techniques on the random decrement signature [8]. This method is particularly useful when only the natural frequency and the damping ratio are needed. Figures 8.9 and 8.10 illustrate the procedure and equipment set-up for measuring dynamic properties of tall timber buildings after they are completed.
Application Development
Published in Scott E. Umbaugh, Digital Image Processing and Analysis, 2017
In Figure 12.13-2, the image is histogram equalized so that we can see the grid, the nonuniformity, and the noise, and a horizontal line, that is, a line defect. For minimization of the effects of this noise, or the unwanted information, filtering was essential. Two methods of filtering, frequency domain filtering and spatial domain filtering, are widely used in many applications and the optimum filter method depends on the application. Frequency domain filters provide us with the most conveniently defined filter types such as notch filter, and/or bandpass filter for efficiently filtering out specific frequencies. However, with the fast Fourier transform used here, the frequency domain filters operate on square images such as 1024×1024, and our oversampled images are 1280×1024. This restriction required us to divide the input image into square subimages (or to pad the image for extending it to 2048×2048). Moreover, computationally expensive frequency domain filters are impractical for real-time applications, whereas spatial domain convolution filters are much faster, and can be implemented by hardware. Thus to avoid the square image restriction, and the extensive arithmetic, the Moore–Penrose generalized inverse matrix is used to approximate a frequency domain filter as a spatial domain filter.
Time domain and frequency domain of coated milling inserts using FFT spectrum
Published in Materials and Manufacturing Processes, 2022
N. Tamiloli, J. Venkatesan, P. Raja Raghu Vamsi Krishna, T. Sampath Kumar
Table 3. Shows the vibration response of the cutting parameters based on the orthogonal array. Frequency (Hz) and acceleration amplitude are the vibration responses studied in this study (g). Fig. 6. Illustrates the various machining conditions as well as the time amplitude. Using the data, FFT analysis is used to translate the time domain signal to the frequency domain. Depict frequency-domain data. The amplitude and frequency values are found in Fig. 6. The third run’s minimum amplitude is 0.1173 g at 929 Hz for minimal amplitude, the matching speed, feed, and DoC are 500 rpm, 100 mm/min, 1 mm depth of cut, and DLC coated tool, as shown in Table 3. AP3 coating at 710 rpm, 100 mm/min, and 0.5 DoC produced the peak (940 Hz) results.
Classification of Nonlinear Features of Uterine Electromyogram Signal Towards the Prediction of Preterm Birth
Published in IETE Journal of Research, 2022
P. Shaniba Asmi, Kamalraj Subramaniam, Nisheena V. Iqbal
Herein, seven non-linear features were compared with four features that have been analyzed in different literatures. Among these four features, the RMS showed the highest classification accuracy. When 11 features were considered, the entropy, Teager energy, detrended fluctuation analysis, and bi-spectrum analysis showed better performance. The Fourier transform converts a signal from the time domain to the frequency domain, in which the amplitude or power is a function of frequency. The power spectrum also limits the information to power and frequency. These two information ignore the phase information present in the signal. The bi-spectral analysis revealed the phase coupling characteristics of the signal. This phase-coupling information along with the context layer of the ENN architecture, which stores the prior information, renders the system useful for clinical purposes to predict preterm labor. This earlier prediction will facilitate the proper treatment for patients. The classification accuracy of the bi-spectrum feature with the ENN classifier is 99.8875% with sensitivity 100% and specificity 99.77%.
Embedded real-time systems in cyber-physical applications: a frequency domain analysis methodology
Published in International Journal of General Systems, 2020
Claudio Aciti, Ricardo Cayssials, Edgardo Ferro, José Urriza, Javier Orozco
Frequency domain analysis is widely used in signal processing and control applications. It is based on the Fourier transform (Rahman 2011) that transforms a time domain signal into a summation of sine and cosine components, each parameterized by the amplitude and angle at a certain frequency. Therefore, the response of a linear time-invariant controller to any input signal can be deduced from the response of the controller to each one of the frequency components that the input signal contains. The response of a linear time-invariant controller to a stimulus equals: which produces an output response where U is the amplitude of the input signal, α is the phase of the input signal, f is the frequency of both the input and output signals, t is the time, Y is the amplitude of the output signal and ϕ is the phase of the output signal. Because no other frequency component is involved when the system input signal is equal to u, all the energy of the output is contained in the frequency component f.