Automotive Oscilloscopes: Waveform Analysis LINK Download Epub Mobi Pdf Fb2 14
Lorie Silano
For the reason of the limited resources on the onboard CPUs, electric and magnetic field spectra are generated from intermittent waveforms by the OFA (shown in the next section). In order to obtain instantaneous phase differences between electric and magnetic fields, a time window of electric field waveform needs to coincide with that of magnetic field. Since the electric and magnetic field packets are collected and processed by the different CPUs, we need a time-synchronization system for the simultaneous observation. Figure 3 shows a schematic diagram of the signal buffers on the each CPU. The CPU stores each 512 samples waveform into a data packet (small rectangles). For example, we collect 1024 samples (2 packets, shown as blue box) every 65,536 samples (128 packets) when we perform 1024 point FFT analysis at 1-s interval. If data loss has occurred for some reason (e.g., CPU overloaded or data communication error), the time-synchronized observation fails.
Decomposition of EMG signal has been done by wavelet spectrum matching and principle component analysis of wavelet coefficients. According to Jianjung et al. (12), more than one single motor unit (SMU) potential will be registered at same time overlapping with each other, especially during a strong muscle contraction. In 1997, they developed a technique using wavelet transform to classify SMU potentials and to decompose EMG signals into their constituent SMU potentials. The distinction of this technique is that it measures waveform similarity of SMU potentials from wavelet domain, which is very advantageous. This technique was based on spectrum matching in wavelet domain. Spectrum matching technique is sometimes considered to be more effective than waveform matching techniques, especially when the interference is induced by low frequency baseline drift or by high frequency noise. The technique developed for multi-unit EMG signal decomposition consists of four separate procedures: signal de-noising procedure, spike detection procedure, spike classification procedure, and spike separation procedure. According to Daniel et al. (13), only wavelet coefficients of lower frequency bands are more important in the differentiation of action potential (AP) characterization than higher bands. This concept is a subjective one which was designed empirically. Experimental results of Rie Yamada et al. (14) in 2003 showed that high frequency information, which were not considered, are also important in the classification of MUAP. To overcome the subjective criterion for feature selection, they proposed another method using principle components analysis (PAC) for wavelet coefficients. The decomposition algorithm consists of four processing stages: segmentation, wavelet transform, PCA, and clustering. The advantage of this method is that it does not require manual selection of coefficients, and takes all frequency information in account.
Automotive Oscilloscopes: Waveform Analysis download epub mobi pdf fb2 14
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A statistical method to estimate the amplitude and the number of newly MUAPs has been proposed by Kanosue et al. In 1974 (43). The method uses the second-and fourth-order moments with parametric model of the elementary MUAP waveforms. Low-order models are obtained using second-order statistics (SOS) and provide parsimonious description of real data. Recently, there has been an increasing interest towards employing higher-order statistics (HOS). Higher-order Statistics (HOS) is a technique for analyzing and interpreting the characteristics and nature of a random process. The subject of HOS is based on the theory of expectation (probability theory) (1). In 1991, Giannakis and Tsatsanis (44) used HOS for EMG signal analysis. According to Giannakis and Tsatsanis, SOS is phase-blind, but has low-variance estimators and when limited to linear-Gaussian processes, they yield computationally and statistically efficient models. In 1995, Yana et al. (45) has generalized the method to estimate MUAP waveforms and their occurring frequency without any assumption for the MUAP waveforms. The method was utilized as a noninvasive method to analyze the forth production mechanism of the muscle. According to his theory, H(w) and λ respectively donate a single MUAP waveform and its occurring frequency is given by equation 9 and 10.
Abstract:In this study, a modified imaging algorithm was implemented to improve the imaging accuracy for defects located on a structure. Based on analysis of the Lamb wave mode, a guided ultrasonic wave inspection technique was applied, which was able to illustrate images of defects in a 6 mm steel plate simulating containment liner plate (CLP) in nuclear power plants. The dominant Lamb wave mode was determined through short-time Fourier transform waveform analysis and imaging verification. Following tomography verification, limitations of the antisymmetric mode in the thick steel plate were identified. In addition, a modified shape factor, based on the energy distribution factor according to the beam pattern and beam width, was suggested for field applications and improved imaging accuracy. Results of the analysis revealed a beam skewing phenomenon for the Lamb wave mode. In the case of S0 2.7 MHzmm, skewing as well as distortion effects are not observed in the experiment, while the S0 modes at 2.64 and 2.74 MHzmm show either of them. Considering skewing width, the size of the shape function was modified. Application of the modified shape function allows us to obtain more accurate image to actual defect shape.Keywords: tomography; image modification; mode verification; guided wave; beam pattern analysis; signal processing
This Quick Link is for Dedicated GPS navigation systems. Dedicated devices are typically two types, either handheld or devices intended for use in automobiles. GPS that are used in automobiles can be either mobile or a permanently installed component. Mobile automotive GPS systems can be used outside the car.
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