The unique mechanosensory lateral line system of fish inspires a novel and promising sensing scheme in dealing with the challenges that autonomous underwater vehicles (AUVs) face in underwater localization and navigation. In this paper, the quantitative and method-independent Cramer–Rao lower bound (CRLB) model is established to evaluate the practical performance of a lateral line sensor array (LLSA) for dipole source localization by considering the excitation pattern mismatches and the measurement noises. The effective localization area of the LLSA is studied under varying array lengths and sensor densities. Localizations for the LS (least squares) method and the MUSIC (multiple signal classification) method are compared in simulations. It is shown that the longer and denser the LLSA, the larger the effective localization area. Typically, the too separated sensor-to-sensor spacing deteriorates the near-body localization performance. The effective localization area obtained by employing the LS method matches the CRLB analysis, which also proves the validity of the analytical CRLB model. Besides, the MUSIC method presents smaller effective localization areas than the LS method. Physical experiments are also conducted and agree well with the CRLB analysis. Further study shows that the measurement noises have an equivalent effect on the excitation pattern mismatches, and can be effectively suppressed by choosing a large number of snapshots. In addition, the CRLB gives the perceptual distance that is consistent with the observation. At last, guidance on the design of a simplified LLSA with the least number of sensors and an optimal sensor-to-sensor spacing is provided for AUVs.

The lateral line is a critical mechanosensory organ that enables fish to perceive the surroundings accurately and rapidly. Massive efforts have been made to build an artificial lateral line system rivaling that of fish for underwater vehicles. Dipole source localization has become a standard problem for evaluating the sensing capabilities of the developed systems. In this paper we propose, for the first time, the MUSIC (multiple signal classification) method in order to achieve high-resolution dipole source localization based on spatial spectrum estimation. We also present the MVDR (minimum variance distortionless response) method by making an improvement to the previous Capon's method. Experiments are conducted on a linear prototype of lateral line canal and the localization performance of these two methods are compared. The results show that the MUSIC method provides an overall localization resolution improvement of 10.4% and maintains similar levels of localization accuracy compared with the MVDR method. Further studies show that the MUSIC method has the potential of localizing two closer incoherent dipole sources with a minimum lateral separation of 20 mm, versus 70 mm for the MVDR method, at a dipole-array distance of half the array length. Both localization methods have strong robustness to the vibrational state of the dipole source. Our work provides a promising and robust way to meet the high-resolution and multi-source sensing requirements of underwater vehicles.

Milk is a common food, which is consumed all over the world. It is an important source of calcium. Meanwhile, it provides abundant protein, minerals and vitamins. However, pathogenic bacteria which exist in milk not only causes nutrition loss, but also produces toxins which may cause diarrhea, food poisoning, and even death. In order to control the microbial level of raw milk and eliminate the contamination of materials, this assay applied loop-mediated isothermal amplification to explore a new way to detect enterotoxigenic Escherichia coli (ETEC) in raw milk. The best reaction condition in detecting ETEC from raw milk was confirmed to be: 0.016 μM each of forward outer primer (primer F3) and backward outer primer (primer B3), 0.128 μM each of forward inner primer (primer FIP) and backward inner primer (primer BIP), 0.45 μM deoxy-ribonucleoside triphosphate (dNTPs), 2IU Bst DNA polymerase large fragment and template DNA were incubated at 63°C for 60 min. LAMP was proved to be specific, rapid and sensitive in detecting pathogenic bacteria which exist in milk.

The utilization of optical and synthetic aperture radar (SAR) multi-source data to obtain better land classification results has received increasing research attention. However, there is a large property and distributional difference between optical and SAR data, resulting in an enormous challenge to fuse the inherent correlation information to better characterize land features. Additionally, scale differences in various features in remote sensing images also influence the classification results. To this end, an optical and SAR Siamese semantic segmentation network, OPT-SAR-MS²Net, is proposed. This network can intelligently learn effective multi-source features and realize end-to-end interpretation of multi-source data. Firstly, the Siamese network is used to extract features from optical and SAR images in different channels. In order to fuse the complementary information, the multi-source feature fusion module fuses the cross-modal heterogeneous remote sensing information from both high and low levels. To adapt to the multi-scale features of the land object, the multi-scale feature-sensing module generates multiple information perception fields. This enhances the network’s capability to learn contextual information. The experimental results obtained using WHU-OPT-SAR demonstrate that our method outperforms the state of the art, with an mIoU of 45.2% and an OA of 84.3%. These values are 2.3% and 2.6% better than those achieved by the most recent method, MCANet, respectively.

Single UAVs have limited capabilities for complex missions, so suitable solutions are needed to improve the mission success rate, as well as the UAVs’ survivability. A cooperative multi-UAV formation offers great advantages in this regard; however, for large and complex systems, the traditional control methods will be invalid when faced with unstable and changing environments. To deal with the poor self-adaptability and high requirements for the environmental state information of traditional control methods for a multi-UAV cluster, this paper proposes a consistent round-up strategy based on PPO path optimization to track targets. In this strategy, the leader is trained using PPO for obstacle avoidance and target tracking, while the followers are expected to establish a communication network with the leader to obtain environmental information. In this way, the tracking control law can be designed, based on the consistency protocol and the Apollonian circle, to realize the round-up of the target and obstacle avoidance. The experimental results show that the proposed strategy can achieve the round-up of the target UAV and guide the pursuing multi-UAV group to avoid obstacles in the absence of the initial detection of the target. In multiple simulated scenarios, the success rates of the pursuit multi-UAV cluster for rounding up the target are maintained above 80%.