Hard and soft threshold functions are discontinuous at the threshold and deviate at the wavelet estimation coefficient, respectively. Aiming at this problem, a rolling element bearing (REB) fault feature extraction method is proposed based on the empirical wavelet transform (EWT) and an arctangent threshold function (ATF). First, the input signal is decomposed with the EWT, and intrinsic mode functions (IMFs) containing fault information are selected according to their cross-correlation coefficients and kurtosis values. Second, the selected IMFs are denoised by the ATF. Finally, to extract the fault characteristic frequency and determine the fault type, the denoised IMFs are added to form a reconstructed signal for envelope analysis. The superiority of the proposed method is verified on simulation signals and actual fault signals (including two cases); the developed approach has strong denoising and fault feature extraction effects.
Signals And Systems By S Poornachandra:pdf
When the receiver detects that the signal changes, it indicates that the external environment has been changed. By de-noising the acquired data and further processing with classification algorithm, we can reduce the environmental factors that lead to the change of the received signal, so as to obtain the desirable information in the environment. In this work, since the antennas were used for detection and monitoring applications in a regular shape room, basic omnidirectional monopole antennas were considered; for irregular shape space and room, specially designed antennas would be necessary to enhance the performance and accuracy of sensing. The main differences between received signal strength and channel state information are explained in Zhu and Zhang (2010) and Zheng et al. (2013). CSI considers the number of antennas and subcarriers, and can measure more fine-grained information, the facility which confirms to the IEEE 802.11n standard was used to collect the CSI data. The IEEE 802.11n standard uses orthogonal frequency division multiplexing (OFDM) to transmit a single data stream with 20 MHz bandwidth through 56 orthogonal subcarriers, the signals transmitted on each subcarrier have different signal strength and phase (Lorincz and Begusic, 2006). The facility used in this paper provides 30 available subcarriers to users. Next, we will further explain the principle of C-Band wireless sensing measurement from the formula.
This paper presents an ECG identification system based on the CSP feature extraction technique. The system is composed of three stages: preprocessing, CSP-based feature extraction, and identification. The single-lead ECG signal is transformed into two channels using Hilbert transform to facilitate CSP being applied to a single-lead ECG signal. Then, the signals are segmented into nonoverlapping segments. Six segment lengths are investigated, namely, 1, 3, 5, 7, 10, 15 seconds. Then the features are extracted using the CSP algorithm. The extracted features are used to train the classifier in the identification stage; the trained classifier is tasked to identify the subject through one-versus-all classification. The proposed method is tested on different reference subject populations of 10 to 200 subjects from the PTB database. The results show an identification rate of 95.15% and equal error rate of 0.1 on a single limb-based lead (I) and an identification rate of 98.92% and equal error rate of 0.08 using a single chest-based lead (V3) for 200 reference subjects. 2ff7e9595c
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