Passive sonar limits the ability to sample vertical scale spatiotemporally, and the received signal is indistinct by noise, resulting in the performance degradation or even failure of the source depth estimation method in deep ocean bottom bounce area. When vertical line array is arranged near the sea surface to locate the source by matched-field processing in bottom bounce area, there is great ambiguity in depth dimension. In this work, the problem of source depth estimation in bottom bounce area is addressed. The peak stripe of angle-range interference pattern is modeled and analyzed based on the ray theory, and a source depth estimation method is proposed, which comprises two parts: estimating sound source depth by utilizing peak stripe fluctuation characteristic combined with spatial spectrum analysis, and reconstructing interference pattern using principal component analysis. The flowchart of the method is listed as follows. Firstly, the spatial spectrum corresponding to each range is spliced to obtain the original angle-range interference pattern. Secondly, the original interference pattern is denoised by principal component analysis to obtain the reconstructed interference structure. Finally, the fluctuation period of peak fringes is extracted from the reconstructed interference pattern to calculate the source depth. Under the low signal-to-noise ratio (SNR) condition, the peak stripe destroyed by noise will reappear in the reconstructed interference pattern and the estimation result of sound source depth will be more accurate, making the source depth estimation method suitable for bottom bounce area. The method combines the space-time cumulative gain of the array and the low-rank characteristic of the interference pattern. The simulation results obtained by Bellhop indicate that the fluctuation period of peak stripe depends on the depth and frequency of the source, which is consistent with the modeling result, and the source depth can be estimated precisely by spatial Fourier transform. The simulation results also show that the rank of the interference pattern is very low and the peak stripe can be composed of a few principal components. Monte Carlo experimental results indicate that the estimated results of the source depth by using reconstructed interference pattern are more accurate than those without using principal component analysis at low SNR condition. The proposed method can achieve more than 80% accuracy at –3dB SNR.
Optical cavity has long been critical for a variety of applications ranging from precise measurement to spectral analysis. A number of theories and methods have been successful in describing the optical response of a stratified optical cavity, while the inverse problem, especially the inverse design of a displacement sensitive cavity, remains a significant challenge due to the cost of computation and comprehensive performance requirements. This paper reports a novel inverse design methodology combining the characteristic matrix method, mixed-discrete variables optimization algorithm, and Monte Carlo method-based tolerance analysis. The material characteristics are indexed to enable the mixed-discrete variables optimization, which yields considerable speed and efficiency improvements. This method allows arbitrary response adjustment with technical feasibility and gives a glimpse into the analytical characterization of the optical response. Two entirely different light-displacement responses, including an asymmetric sawtooth-like response and a highly symmetric response, are dug out and experimentally achieved, which fully confirms the validity of the method. The compact Fabry-Perot cavities have a good balance between performance and feasibility, making them promising candidates for displacement transducers. More importantly, the proposed inverse design paves the way for a universal design of optical cavities, or even nanophotonic devices.
Underwater acoustic source localisation in deep water shadow zone is difficult since the vertical aperture of the array is limited and the received signal-noise ratio (SNR) is low. This article investigates a novel interference pattern in the first shadow zone. The intensity displays two oscillating striations in the interference pattern, which can be used to estimate the source depth under the restriction of lacking adequate spatial samples and acoustic environmental parameters, and the range of the source can be estimated roughly using the position of which. The method is applicable to multi-target localisation. In practice, the interference pattern is always corrupted by noise under low SNR conditions, resulting in performance degradation or even failure of the localisation. To address the problem of recovering the corrupted interference pattern, an interference tensor is constructed and analysed, which expands the general two-dimensional interference pattern into range-grazing angle-frequency domain to make use of the multi-frame interaction coherently. A Tucker decomposition-based method that adopts a low-rank representation of the interference tensor is proposed to retrieve the intact interference pattern. Simulated data validate the accuracy of the localisation under low SNR conditions. It is shown that this method significantly improves the estimated performance over conventional matched-field processing.
An interferometric micro-optomechanical accelerometer usually has ultrahigh sensitivity and accuracy. However, cross-axis interference inevitably degrades the performance, including its detection accuracy and output signal contrast. To accurately clarify the influence of cross-axis interference, a modified mechanical–optical theoretical model is established. The rotation of the proof mass and the detected light intensity are quantitatively investigated with a load of cross-axis acceleration. A simulation and experiment are performed to verify the correctness of the theoretical model when the cross-axis acceleration is from 0 to 0.175 g. The results demonstrate that this model has a more than fivefold accuracy increase compared with conventional theoretical models when the cross-axis acceleration is from 0.06 to 0.175 g. In addition, we provide a suppression method to diminish the rotation of the proof mass based on squeeze film air damping, which significantly suppresses the contrast reduction caused by cross-axis interference.
Pressure sensors are considered the key technology for potential applications in real-time health monitoring, artificial electronic skins, and human− machine interfaces. Despite the significant progress in developing novel sensitive materials and constructing unique sensor structures, it remains challenging to fabricate large-area pressure sensor arrays due to the involvement of complex procedures including photolithography, laser writing, or coating. Herein, a scalable manufacturing approach for the realization of pressure sensor arrays with substantially enlarged sensitive areas is proposed using a versatile screen-printing technique. A compensation mechanism is introduced into the printing process to ensure the precise alignment of conductive electrodes, insulation layers, and sensitive microstructures with an alignment error of less than 4 μm. The fully screen-printed sensors exhibit excellent collective sensing performance, such as a reasonable pressure sensitivity of −2.2 kPa −1 , a fast response time of 40 ms, and superior durability over 3000 consecutive pressures. Additionally, an integrated 16 × 32 pressure sensor array with a sensing area of 190 × 380 mm 2 is demonstrated to precisely recognize the sitting postures and the body weights, showing great potential in continuous and real-time health status monitoring.
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