With the rapid progress of artificial intelligence, humans are moving toward the era of the intelligent connection of all things. Therefore, the demand for sensors is drastically increasing with developing intelligent social applications. Traditional sensors must be triggered by an external power source and the energy consumption is high for equipment that is widely distributed and working intermittently, which is not conducive to developing sustainable green and healthy applications. However, self-powered sensors based on triboelectric nanogenerators (TENG) can autonomously harvest energy from the surrounding environment and convert this energy into electrical energy for storage. Sensors can also be self-powered without an external power supply, which is vital for smart cities, smart homes, smart transportation, environmental monitoring, wearable devices, and bio-medicine. This review mainly summarizes the working mechanism of TENG and the research progress of self-powered sensors based on TENG about the Internet of Things (IoT), robotics, human–computer interaction, and intelligent medical fields in recent years.

Microwave-absorbing materials have attracted increased research interest in recent years because of their core roles in the fields of electromagnetic (EM) pollution precaution and information security. In this paper, microwave-absorbing material NiFe-layered double hydroxide (NiFe-LDH) was synthesized by a simple co-precipitation method and calcined for the fabrication of NiFe-mixed metal oxide (NiFe-MMO). The phase structure and micromorphology of the NiFe-LDH and NiFe-MMO were analyzed, and their microwave-absorbing properties were investigated with a vector network analyzer in 2–18 GHz. Both NiFe-LDH and NiFe-MMO possessed abundant interfaces and a low dielectric constant, which were beneficial to electromagnetic wave absorption, owing to the synergistic effect of multi-relaxation and impedance matching. The optimum reflection loss (RL) of NiFe-LDH and NiFe-MMO was −58.8 dB and −64.4 dB, respectively, with the thickness of 4.0 mm in the C band. This work demonstrates that LDH-based materials have a potential application in electromagnetic wave absorption.

Customizing the frequency range of electromagnetic wave (EMW) absorbing materials, especially for low-frequency, is a key research focus for 5G/6G and stealth applications. However, achieving precise low-frequency tuning remains challenging due to unpredictable parameter variations in practical design. Here, a constant-permeability-based electromagnetic parameter inversion method predicts the required complex permittivity range for multilayer MXene’s effective microwave absorption in the target low-frequency band. Since traditional modulation methods are plagued by electromagnetic parameter fluctuations, this study regulated the dielectric response by adjusting the embedding amount of small-sized iron nanoparticles (Fe NPs) with stable permeability. Under this guidance, multilayer MXene/Fe NPs (MTF) are prepared by embedding small-sized Fe NPs on the MXene surface via electrostatic self-assembly and in-situ reduction. The introduction of Fe NPs increased charge carriers’ concentration and strengthened the interface effect, resulting in a significant increase in the real part of the complex permittivity (ε') compared with that of multi-layer MXene (7.13-8.89), reaching the predicted range of the real part of the low-frequency complex permittivity (13.12-15.16, 14.34-16.81, and 15.29-18.12). Experimental results show that the MTF has a small error in the frequency of the minimum reflection loss (RL_min) compared to the predicted value (error percentage of 4.69%), along with an in-situ enhancement of the effective absorption bandwidth (EAB) (325.00% growth). Thus, MTF exhibits enhanced low-frequency absorption, with MTF-2 achieving -46.3 dB RLmin at 4.64 GHz (4.35 mm) and 2.24 GHz EAB at 3.8 mm. This work offers a strategy for accurate prediction and regulation of absorption bands over a wide range.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

Flexible polymer-based magnetoelectric (ME) materials have broad application prospects and are considered as a new research field. In this article, FeCoSiB thin films were deposited on poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) substrate by DC magnetron sputtering. The structure of PVDF-TrFE/FeCoSiB heterostructure thin films was similar to 2-2. Under a bias magnetic field of 70 Oe, the composites have a dramatically increased ME voltage coefficient as high as 111 V/cm⋅Oe at a frequency of about 85 kHz. The piezoelectric coefficient of PVDF-TrFE thin films is 34.87 pC/N. The surface morphology of PVDF-TrFE thin films were studied by FESEM, and the results of XRD and FTIR showed that the β-phase of PVDF-TrFE thin films was dominant. Meanwhile, the effects of different heating conditions on the crystallization and piezoelectric properties of PVDF-TrFE films were also studied. The flexible ME heterojunction composite has a significant ME voltage coefficient and excellent piezoelectric properties at room temperature, which allows it to be a candidate material for developing flexible magnetoelectric devices.

Autonomous navigation technology is a core technology for intelligent operation, allowing the vehicles to perform tasks without relying on external information, which effectively improves the concealability and reliability. In this paper, based on the previous research on the bionic compound eye, a multi-channel camera array with different polarization degrees was used to construct the atmospheric polarization state measurement platform. A polarization trough threshold segmentation algorithm was applied to study the distribution characteristics and characterization methods of polarization states in atmospheric remote sensing images. In the extracted polarization feature map, the tilting suggestion box was obtained based on the multi-direction window extraction network (similarity-based region proposal networks, SRPN) and the rotation of the suggestion box (Rotation Region of interests, RRoIs). Fast Region Convolutional Neural Networks (RCNN) was used to screen the suggestion boxes, and the Non-maximum suppression (NMS) method was used to select the angle, corresponding to the label of the suggestion box with the highest score, as the solar meridian azimuth in the vehicle coordinate system. The azimuth angle of the solar meridian in the atmospheric coordinate system can be calculated by the astronomical formula. Finally, the final heading angle can be obtained according to the conversion relationship between the coordinate systems. By fitting the measured data based on the least Square method, the slope K value is −1.062, RMSE (Root Mean Square Error) is 6.984, and the determination coefficient R-Square is 0.9968. Experimental results prove the effectiveness of the proposed algorithm, and this study can construct an autonomous navigation algorithm with high concealment and precision, providing a new research idea for the research of autonomous navigation technology.