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Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.

With the development of artificial intelligence, intelligent communication jamming decision making is an important research direction of cognitive electronic warfare. In this paper, we consider a complex intelligent jamming decision scenario in which both communication parties choose to adjust physical layer parameters to avoid jamming in a non-cooperative scenario and the jammer achieves accurate jamming by interacting with the environment. However, when the situation becomes complex and large in number, traditional reinforcement learning suffers from the problems of failure to converge and a high number of interactions, which are fatal and unrealistic in a real warfare environment. To solve this problem, we propose a deep reinforcement learning based and maximum-entropy-based soft actor-critic (SAC) algorithm. In the proposed algorithm, we add an improved Wolpertinger architecture to the original SAC algorithm in order to reduce the number of interactions and improve the accuracy of the algorithm. The results show that the proposed algorithm shows excellent performance in various scenarios of jamming and achieves accurate, fast, and continuous jamming for both sides of the communication.

In this paper, a 3.3 GHz - 3.8 GHz Monolithic Microwave Integrated Circuit (MMIC) power amplifier (PA) using a 0.25μm GaN on SiC high electron mobility transistor (HEMT) technology for 5G communication system is proposed. The stabilization of the circuit structure is composed of parallel connection of a resistance and a capacitance which called RC stabilization circuits. Determination the optimal impedance matching using Load-Pull. This project measured results demonstrated that the average Pout of 18.12 dBm, an average large signal gain of 8.72 dB over frequency band with a great harmonic isolation that the second harmonic is -25.67 dBc@3.55 GHz and the third harmonic is -46.61 dBc@3.55 GHz. The power amplifier chip is integrated into a 1.0mm×2.6mm QFN package including RF pad.

Remote sensing images have advantages in large-area imaging and macroscopic integrity. However, in most commercial applications, further recognition and processing becomes difficult due to the low spatial resolution of the acquired images. Therefore, improving the resolution of remote sensing images has important practical significance. To solve this problem, we propose a remote sensing image super-resolution method based on deep learning technology. In order to obtain more detailed image information, we introduce multi-scale convolution to implement feature extraction and deconvolution be used to achieve the final 3× image reconstruction without bicubic interpolation. Experimental results show that our network achieves better performance than prior art methods and visual improvement of our results is easily noticeable.

DCSK modulation in chaos communication is a robust non-coherent modulation scheme. In this paper, the multiple-access DCSK scheme based on the OVSF code is proposed. Using the multiple-access DCSK scheme in RFID system, a DCSK-RFID system is presented. In the presented DCSK-RFID system, we use the DCSK for tag modulation for its low complexity and the simple receiver of the DCSK scheme is applied in reader part. The tag's BER performance of the proposed DCSK-RFID system is carefully generalized both in theoretic analysis and in simulations. From the simulation results, the theoretical and simulation values match closely with each other. Then, we design an anti-collision MAC protocol based on multi-access DCSK-RFID scheme. We theoretically analyze the throughout in given number of tags. The simulation shows that the proposed algorithm has better throughout than S-Aloha system.