Location error and false alarm are noticeable problems in fiber distributed acoustic sensing systems based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). A novel method based on signal kurtosis is proposed to locate and discriminate perturbations in Φ-OTDR systems. The spatial kurtosis (SK) along the fiber is firstly obtained by calculating the kurtosis of acoustic signals at each position of the fiber in a short time period. After the moving average on the spatial dimension, the spatial average kurtosis (SAK) is then obtained, whose peak can accurately locate the center of the vibration segment. By comparing the SAK value with a certain threshold, we may to some degree discriminate the instantaneous destructive perturbations from the system noise and certain ambient environmental interferences. The experimental results show that, comparing with the average of the previous localization methods, the SAK method improves the pencil-break and digging locating signal-to-noise ratio (SNR) by 16.6 dB and 17.3 dB, respectively; and decreases the location standard deviation by 7.3 m and 9.1 m, respectively. For the instantaneous destructive perturbation (pencil-break and digging) detection, the false alarm rate can be as low as 1.02%, while the detection probability is maintained as high as 95.57%. In addition, the time consumption of the SAK method is adequate for a real-time Φ-OTDR system.

Here we describe the utilization of flagellated bacteria as actuators to propel spherical liposomes by attaching bacteria to the liposome surface. Bacteria were stably attached to liposomes using a cross-linking antibody. The effect of the number of attached bacteria on propulsion speed was experimentally determined. The effects of bacterial propulsion on the bacteria-antibody-liposome complex were stochastic. We demonstrated that liposomal mobility increased when bacteria were attached, and the propulsion speed correlated with the number of bacteria.

we address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped ULA by developing an algorithm represented by the convex optimal method. In this paper, the generalized conjugate symmetry property of L-shaped ULA is fully developed to increase not only virtual array aperture but virtual snapshots, which not only increases the maximum resolvable sources but yields better 2-D DOA estimation performance. Specifically, we first formulate the cost function as a quadratically constrained complex quadratic programming (QCCQP) problem via the subspace theory. The QCCQP problem can then be relaxed to a series of semidefinite programming (SDP) problems, which can be solved via the CVX solvers in polynomial time complexity per iteration. To avoid complex 2-D global iterations during the implementation of SDP problems, the PM-ESPRIT-like method is first applied to estimate azimuths, based on which, the proposed method can then be transformed to 1-D local iterations with no additional angles pairings needed. Furthermore, the superb performance of the proposed method still holds whether the spatial angles are very close or separate apart. Performances evaluations are confirmed based on multiple simulations examples and some criteria.

Distributed acoustic sensing based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been widely used in many fields. Phase demodulation of the Φ-OTDR signal is essential for undistorted acoustic measurement. Digital coherent detection is a universal method to implement phase demodulation, but it may cause severe computational burden. In this paper, analog I/Q demodulation is introduced into the Φ-OTDR based DAS system to solve this problem, which can directly obtain the I and Q components of the beat signal without any digital processing, meaning that the computational cost can be sharply reduced. Besides, the sampling frequency of the data acquisition card can theoretically be lower than the beat frequency as the spectrum aliasing would not affect the demodulation results, thus further reducing the data volume of the system. Experimental results show that the proposed DAS system can demodulate the phase signal with good linearity and wide frequency response range. It can also adequately recover the sound signal sensed by the optical fiber, indicating that it can be a promising solution for computational-cost-sensitive distributed acoustic sensing applications.

Simultaneous Localization and Mapping (SLAM) is one of the fundamental capabilities for autonomous vehicles to achieve accurate localization in dynamic urban environments. However, in real-world scenarios, the presence of moving objects such as pedestrians, bicycles, or vehicles affects the localization accuracy of SLAM and leaves behind ghost trails in the created map. Hence, it is essential for SLAM to remove dynamic objects in real-time to improve its accuracy. In this paper, we present DOR-LINS, a real-time moving objects removal framework for in LiDAR-Inertial SLAM. Built on the foundation of LIO-SAM, DOR-LINS integrates the capability of removing dynamic objects, enabling it to remove most of the dynamic objects and achieve precise odometry. Our contributions are reflected in three aspects. Firstly, our method extracts ground points from the current scan and clusters non-ground points to segment them into static and potential dynamic clusters. Then, we divide the submap and current scan into multiple bins and introduce a novel concept called ground pseudo occupancy to describe the occupancy of each bin. Secondly, based on the ground pseudo occupancy , dynamic clusters, and static clusters, we propose an approach named Beam Tracing Test (BTT) and combine it with Scan Ratio Test (SRT) to select candidate dynamic bins. Finally, we employ a dynamic point verification algorithm to filter out actual dynamic points from these candidate dynamic bins. As experimentally evaluated on the UrbanLoco dataset, our proposed method removes many dynamic points and yields promising performance against state-of-the-art methods.

In this article, we propose two computationally efficient approaches to jointly estimate direction of arrival (DOA) and carrier for coherent signals using a given single snapshot with uniform linear array. First, the proposed methods construct the Khatri-Rao product-like data vector by introducing P -level delays from the array output. Second, a row elementary transformation is applied to the structured data vector, the rotational invariance structure of the carrier vector is exploited and thereby the carrier is derived. To be able to proceed, the special structure of the data vector is also exploited to estimate the coherent waveform. As a result, the DOA estimation problem can be recast into two Frobenius-norm-based optimization problems: one is referred to as linear search-based covariance-like matrix fitting method, which is suitable for estimating spaced angles that are well separated. The other one is called subspace separation-based covariance-like matrix fitting method, which is suitable for estimating closely spaced angles. The proposed methods develop one-dimensional (1-D) search and K 1-D search procedures, respectively, and hence they are computationally efficient compared to the classical methods. The simulation results illustrate that the performance of the proposed methods is better than the spatial smoothing-based multiple signal classification method under different scenarios.