In the marine environment, underwater targets are often affected by interference from other targets and environmental fluctuations, so traditional target tracking methods are difficult to use for tracking underwater targets stably and accurately. Among the traditional methods, the Kalman filtering method is widely used; however, it only has advantages in solving linear problems and it is difficult to use to realize effective tracking problems when the trajectory of the moving target is nonlinear. Aiming to solve this limitation, an LSTM–Kalman filtering method was proposed, which can efficiently solve the problem of overly large deviations in underwater target tracking. Using this method, we first studied the features of typical underwater targets and, according to these rules, constructed the corresponding target dataset. Second, we built a convolutional neural network (CNN) model to detect the target and determine the tracking value of the moving target. We used a long-term and short-term memory artificial neural network (LSTM-NN) to modify the Kalman filter to predict the azimuth and distance of the target and to update it iteratively. Then, we verified the new method using simulation tests and the measured data from an acoustic sea trial. The results showed that compared to the traditional Kalman filtering method, the relative error of the LSTM–Kalman filtering method was reduced by 60% in the simulation tests and 72.25% in the sea trial and that the estimation variance was only 4.79. These results indicate that the method that is proposed in this paper achieves good prediction results and a high prediction efficiency for underwater target tracking.

The development of smart metamaterials has brought changes to human society, and various new products based on smart metamaterials are emerging endlessly. In recent years, smart electromagnetic metamaterials, smart acoustic metamaterials, smart mechanical metamaterials, smart thermal metamaterials and machine learning have attracted much attention in metamaterials. These fields share similar theories, such as multiphysics coupling fields, novel artificial cells and programmability. Through theoretical and technical research, smart metamaterials will show exquisite applications in many fields, such as antenna and optical communication systems, microwave imaging, acoustic stealth, thermal camouflage, etc. In particular, the characteristics of the personalized microstructure design of smart metamaterials perfectly match the characteristics of 3D printing. The combination of them leads the development of metamaterials, which are undoubtedly of great value. In this paper, focusing on the representative key technologies, we review the development history, main research directions and latest applications of smart metamaterials. Finally, the possible development direction of metamaterials is predicted.

The active tracking technology of underwater acoustic targets is an important research direction in the field of underwater acoustic signal processing and sonar, and it has always been issued that draws researchers’ attention. The commonly used Kalman filter active tracking (KFAT) method is an effective tracking method, however, it is difficult to detect weak SNR signals, and it is easy to lose the target after the azimuth of different targets overlaps. This paper proposes a KFAT based on deep convolutional neural network (DCNN) method, which can effectively solve the problem of target loss. First, we use Kalman filtering to predict the azimuth and distance of the target, and then use the trained model to identify the azimuth-weighted time-frequency image to obtain the azimuth and label of the target and obtain the target distance by the time the target appears in the time-frequency image. Finally, we associate the data according to the target category, and update the target azimuth and distance information for this cycle. In this paper, two methods, KFAT and DCNN-KFAT, are simulated and tested, and the results are obtained for two cases of tracking weak signal-to-noise signals and tracking different targets with overlapping azimuths. The simulation results show that the DCNN-KFAT method can solve the problem that the KFAT method is difficult to track the target under the weak SNR and the problem that the target is easily lost when two different targets overlap in azimuth. It reduces the deviation range of the active tracking to within 200 m, which is 500~700 m less than the KFAT method.

To solve the mismatch between the comprehensive mechanical properties of the spinal fusion cage and body, a fusion cage inner hole design method based on controllable TPMS-P to characterize the inner hole structure is proposed to solve the related problems. Firstly, the method of TPMS-P parameterization was used to construct the bionic porous structure model, which was designed as the linear gradual internal porous structure model. Then, we optimized the topology of the obtained porous structure implants to achieve precise control of the overall comprehensive mechanical properties of the fusion cage structure and obtain an optimized model that matched the mechanical properties of the fusion cage. To verify whether the method met the requirements, its simulation model was established. The porous structure was fabricated by selective laser processing, and its properties were tested and analyzed. The results show that its yield strength is 79.83 MPa, which match well with spinal bone tissue.

Hydrogel microspheres are widely used in tissue engineering, such as 3D cell culture and injection therapy, and among which, heterogeneous microspheres are drawing much attention as a promising tool to carry multiple cell types in separated phases. However, it is still a big challenge to fabricate heterogeneous gel microspheres with excellent resolution and different material components in limited sizes. Here, we developed a multi-channel dynamic micromixer, which can use active mechanical mixing to achieve rapid mixing with multi-component materials and extrude the homogenized material. By changing the flow rate ratio of the solutions of the two components and by rapidly mixing in the micromixer, real-time concentration change of the mixed material at the outlet could be monitored in a process so-called “gradient printing”. By studying the mixing efficiency of the micromixer, its size and process parameters were optimized. Using the novel dynamic gradient printing method, the composition of the hydrogel microspheres can be distributed in any proportion and alginate heterogeneous gel microspheres with adjustable cell concentration were fabricated. The effects of cell concentration on cell viability and proliferation ability under three-dimensional culture conditions were also studied. The results showed that cells have very low death rate and can exchange substances within the microspheres. Due to the micromixing ability of the micromixers, the demand for biological reagents and materials such as cells, proteins, cytokines and other materials could be greatly reduced, which helps reduce the experimental cost and improve the feasibility of the method in practical use. The heterogeneous gel microsphere can be greatly valuable for research in various fields such as analytical chemistry, microarray, drug screening, and tissue culture.