Current Issue : April - June Volume : 2020 Issue Number : 2 Articles : 6 Articles
Detection of the loose particles is urgently required in the spacecraft production processes. PIND (particle impact noise detection)\nis the most commonly used method for the detection of loose particles in the aerospace electronic components. However, when the\nmass of loose particles is smaller than 0.01 mg, the weak signals are difficult to be detected accurately. In this paper, the aperiodic\nstochastic resonance (ASR) is firstly used to detect weak signals of loose particles. The loose particle signal is simulated by the\noscillation attenuation signal. The influences of structure parameters on the potential height and detection performance of ASR\nare studied by a numerical iteration method. The cross-correlation coefficient C1 between input and output is chosen as a\ncriterion for whether there is an existing a particle or not. Through normalization, the loose particle signal-labeled high\nfrequency of 135 kHz is converted into the low-frequency band, which can be detected by the ASR method. According to the\nalgorithm, weak signals covered by noise could be detected. The experimental results show that the detection accuracy is 66.7%.\nThis algorithm improves the detection range of weak loose particle signals effectively....
This paper proposes an area-efficient fast Fourier transform (FFT) processor for zero-padded\nsignals based on the radix-2^2 and the radix-2^3 single-path delay feedback pipeline architectures.\nThe delay elements for aligning the data in the pipeline stage are one of the most complex units\nand that of stage 1 is the biggest. By exploiting the fact that the input data sequence is zero-padded\nand that the twiddle factor multiplication in stage 1 is trivial, the proposed FFT processor can\ndramatically reduce the required number of delay elements. Moreover, the 256-point FFT processors\nwere designed using hardware description language (HDL) and were synthesized to gate-level\ncircuits using a standard cell library for 65 nm CMOS process. The proposed architecture results in a\nlogic gate count of 40,396, which can be efficient and suitable for zero-padded FFT processors....
The periodic nonuniform sampling plays an important role in digital signal processing and other engineering fields. In this paper,\nwe introduce the Gaussian regularization method to accelerate the convergence rate of periodic nonuniform sampling series. We\nprove that the truncation error of the Gaussian regularized periodic nonuniform sampling series decays exponentially. Numerical\nexperiments are presented to demonstrate our result....
Huge video data has posed great challenges on computing power and storage space,\ntriggering the emergence of distributed compressive video sensing (DCVS). Hardware-friendly\ncharacteristics of this technique have consolidated its position as one of the most powerful architectures\nin source-limited scenarios, namely, wireless video sensor networks (WVSNs). Recently, deep\nconvolutional neural networks (DCNNs) are successfully applied in DCVS because traditional\noptimization-based methods are computationally elaborate and hard to meet the requirements of\nreal-time applications. In this paper, we propose a joint samplingâ??reconstruction framework for DCVS,\nnamed â??JsrNetâ?. JsrNet utilizes the whole group of frames as the reference to reconstruct each frame,\nregardless of key frames and non-key frames, while the existing frameworks only utilize key frames\nas the reference to reconstruct non-key frames. Moreover, different from the existing frameworks\nwhich only focus on exploiting complementary information between frames in joint reconstruction,\nJsrNet also applies this conception in joint sampling by adopting learnable convolutions to sample\nmultiple frames jointly and simultaneously in an encoder. JsrNet fully exploits spatialâ??temporal\ncorrelation in both sampling and reconstruction, and achieves a competitive performance in both\nthe quality of reconstruction and computational complexity, making it a promising candidate in\nsource-limited, real-time scenarios....
In laser Doppler velocimeter (LDV), calculation precision of Doppler shift is affected by noise contained in Doppler signal. In\norder to restrain the noise interference and improve the precision of signal processing, wavelet packet threshold denoising\nmethods are proposed. Based on the analysis of Doppler signal, appropriate threshold function and decomposition layer number\nare selected. Heursure, sqtwolog, rigrsure, and minimaxi rules are adopted to get the thresholds. Processing results indicate that\nsignal-to-noise ratio (SNR) and root mean square error (RMSE) of simulated signals with original SNR of 0 dB, 5 dB, and 10 dB in\nboth low- and high-frequency ranges are significantly improved by wavelet packet threshold denoising. A double-beam and\ndouble-scattering LDV system is built in our laboratory. For measured signals obtained from the experimental system, the\nminimum relative error of denoised signal is only 0.079% (using minimaxi rule). The denoised waveforms of simulated and\nexperimental signals are much more smooth and clear than that of original signals. Generally speaking, denoising effects of\nminimaxi and saqtwolog rules are better than those of heursure and rigrsure rules. As shown in the processing and analysis of\nsimulated and experimental signals, denoising methods based on wavelet packet threshold have ability to depress the noise in laser\nDoppler signal and improve the precision of signal processing. Owing to its effectiveness and practicability, wavelet packet\nthreshold denoising is a practical method for LDV signal processing....
The transient impact components in vibration signal, which are the major information for\nbearing fault severity recognition, are often interfered with by ambient noise. Meanwhile, for bearing\nfault severity recognition, the frequency band selection methods which are employed to pre-process\nthe contaminated vibration signal only select the partial frequency band of the vibration signal and\ncause information loss of other frequency band. Aiming at this issue, this paper proposes a novel fault\nseverity recognition method based on Huffman coding, which can retain all the information of the\nfrequency band, and is applied for the first time to bearing fault severity recognition. Specifically, the\naverage coding length of Huffman coding (ACLHC) of the original vibration signal is first calculated\nto reduce the noise and highlight the impact components of the signal. Then, the ACLHC is encoded\nby symbolic aggregate approximation (SAX) to reflect the modulation information of bearing. Finally,\nthe Lempel-Ziv indicator (LZ indicator) of the symbol sequence is calculated to reflect the fault\nseverity. The proposed method is verified by the bearing datasets under different working conditions.\nCompared with the methods based on frequency band selection, the proposed method effectively\nrecognizes the fault severity of bearing for more working conditions....
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