Pixel-level image fusion is an effective way to fully exploit the rich texture information of visible images and the salient target characteristics of infrared images. With the development of deep learning technology in recent years, the image fusion algorithm based on this method has also achieved great success. However, owing to the lack of sufficient and reliable paired data and a nonexistent ideal fusion result as supervision, it is difficult to design a precise network training mode. Moreover, the manual fusion strategy has difficulty ensuring the full use of information, which easily causes redundancy and omittance. To solve the above problems, this paper proposes a multi-stage visible and infrared image fusion network based on an attention mechanism (MSFAM). Our method stabilizes the training process through multi-stage training and enhances features by the learning attention fusion block. To improve the network effect, we further design a Semantic Constraint module and Push–Pull loss function for the fusion task. Compared with several recently used methods, the qualitative comparison intuitively shows more beautiful and natural fusion results by our model with a stronger applicability. For quantitative experiments, MSFAM achieves the best results in three of the six frequently used metrics in fusion tasks, while other methods only obtain good scores on a single metric or a few metrics. Besides, a commonly used high-level semantic task, i.e., object detection, is used to prove its greater benefits for downstream tasks compared with singlelight images and fusion results by existing methods. All these experiments prove the superiority and effectiveness of our algorithm.

In order to achieve high precision from non-contact temperature measurement, the hardware structure of a broadband correlative microwave radiometer, calibration algorithm, and temperature inversion algorithm are innovatively designed in this paper. The correlative radiometer is much more sensitive than a full power radiometer, but its accuracy is challenging to improve due to relatively large phase error. In this study, an error correction algorithm is designed, which reduces the phase error from 69.08° to 4.02°. Based on integral calibration on the microwave temperature measuring system with a known radiation source, the linear relationship between the output voltage and the brightness temperature of the object is obtained. Since the metal aluminum plate, antenna, and transmission line will have a non-linear influence on the receiver system, their temperature characteristics and the brightness temperature of the object are used as the inputs of the neural network to obtain a higher accuracy of inversion temperature. The temperature prediction mean square error of a back propagation (BP) neural network is 0.629 °C, and its maximum error is 3.351 °C. This paper innovatively proposed the high-precision PSO-LM-BP temperature inversion algorithm. According to the global search ability of the particle swarm optimization (PSO) algorithm, the initial weight of the network can be determined effectively, and the Levenberg–Marquardt (LM) algorithm makes use of the second derivative information, which has higher convergence accuracy and iteration efficiency. The mean square error of the PSO-LM-BP temperature inversion algorithm is 0.002 °C, and its maximum error is 0.209 °C.

In recent years, with the development of the communication industry, the need to use Ethernet switches to transmit big data has become more urgent, and its protocol standards are iterating towards higher return loss, wider bandwidth, lower impedance fluctuations and insertion loss. Based on the research of high-speed backplane with a single channel 25 Gbps transmission rate, a novel double grounded planar strip coplanar waveguide design is presented, which significantly improved return loss to 20 dB and reduced insertion loss, which meet the loss standard of 100GBASE-KR4. The resonant cavity model of transmission line reference plane is improved by introducing vias and the parameters of vias in the reference plane are studied to reduce the impact of resonance, which improved the transmission −1 dB bandwidth to 60 GHz. Based on equivalent circuit analysis of differential vias’ joint reverse pad, the parameters related to the differential vias are studied, the impedance fluctuation is reduced to 100 ± 3 Ω, which is 70% better than the impedance fluctuation standard (100 ± 10 Ω) of 100GBASE-KR4. After optimizing the mathematical model of strip coplanar waveguide, reference plane and differential vias, we built a simulation model of the backplane passive link which met the 100GBASE-KR4 backplane Ethernet specification. In the actual test, it was found that the optimized model can improve the link performance.