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Abstract The rapid development of UAV communication technology makes it have application potential in wireless systems. However, for the optimization problem of UAV base station providing communication for ground personnel in the disaster area under special natural disasters, traditional deep reinforcement learning is used. Algorithms cannot solve such problems well. This article proposes the AD3QN algorithm combined with the attention mechanism, which can communicate with disaster victims on the ground better and more quickly, providing better information collection for rescue missions and completing the task of communication optimization. In the mission simulation environment, in order to make the environment more realistic, we innovatively designed the ground user distribution model, air-to-ground communication model, and electromagnetic interference model. Finally, the effect of the proposed algorithm is evaluated by comparing different deep reinforcement learning algorithms. The results show that the algorithm we proposed can provide communication services faster and has higher practicability in terms of algorithm.

Abstract Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully crafted adversarial interference to the transmitted signal. Based on the requirements of efficient computing and edge deployment, a lightweight automatic modulation classification model is proposed. Considering that the lightweight automatic modulation classification model is more susceptible to interference from adversarial attacks and that adversarial training of the lightweight auto‐modulation classification model fails to achieve the desired results, an adversarial attack defense system for the lightweight automatic modulation classification model is further proposed, which can enhance the robustness when subjected to adversarial attacks. The defense method aims to transfer the adversarial robustness from a trained large automatic modulation classification model to a lightweight model through the technique of adversarial robust distillation. The proposed method exhibits better adversarial robustness than current defense techniques in feature fusion based automatic modulation classification models in white box attack scenarios.

Radar is widely used in aviation, meteorology, and military fields, and radar pulse signal detection has become an indispensable and essential function of cognitive radio systems as well as electronic warfare systems. In this paper, we propose a deep learning-based radar signal detection method. Firstly, we propose a detection method based on raw in-phase and quadrature (IQ) input, which utilizes a convolutional neural network (CNN) to automatically learn the features of radar pulse signals and noises, to accomplish the detection task. To further reduce the computational complexity, we also propose a hybrid detection method that combines compressed sensing (CS) and deep learning, which reduces the length of the signal by compressed downsampling, and then feeds the compressed signal to the CNN for detection. Extensive simulation results show that our proposed IQ-based method outperforms the traditional short-time Fourier transform method as well as three existing deep learning-based detection methods in terms of probability of detection. Furthermore, our proposed IQ-CS-based method can achieve satisfactory detection performance with significantly reduced computational complexity.

Large-scale deep learning algorithms have emerged as the primary technology for underwater target detection, demonstrating exceptional inference effectiveness and accuracy. However, the real-time capabilities of these high-accuracy algorithms rely heavily on high-performance computing resources like CPUs and GPUs. It presents a challenge for deploying them on underwater embedded devices, where communication is limited and computational and energy resources are constrained. To overcome this, this paper focuses on constructing a lightweight yet highly accurate deep learning model suitable for real-time underwater target detection on edge devices. We develop a new lightweight model, named YOLO-TN, for real-time underwater object recognition on edge devices using a self-constructed image dataset captured by an underwater unmanned vehicle. This model is obtained by compressing the classical YOLO-V5, utilizing a globally differentiable deep neural architecture search method and a network pruning technique. Experimental results show that the YOLO-TN achieves a mean average precision (mAP) of 0.5425 and an inference speed of 28.6 FPS on embedded devices, while its parameter size is between 0.4 M and 0.6 M. This performance is a fifth of the parameter size and twelve times the FPS of the YOLO-V5 model, with almost no loss in inference accuracy. In conclusion, this framework significantly enhances the feasibility of deploying large-scale deep learning models on edge devices with high precision and compactness, ensuring real-time inference and offline deployment capabilities. This research is pivotal in addressing the computational challenges faced in underwater operations.

Abstract Snoring, as a prevalent symptom, seriously interferes with life quality of patients with sleep disordered breathing only (simple snorers), patients with obstructive sleep apnea (OSA) and their bed partners. Researches have shown that snoring could be used for screening and diagnosis of OSA. Therefore, accurate detection of snoring sounds from sleep respiratory audio at night has been one of the most important parts. Considered that the snoring is somewhat dangerously overlooked around the world, an automatic and high-precision snoring detection algorithm is required. In this work, we designed a non-contact data acquire equipment to record nocturnal sleep respiratory audio of subjects in their private bedrooms, and proposed a hybrid convolutional neural network (CNN) model for the automatic snore detection. This model consists of a one-dimensional (1D) CNN processing the original signal and a two-dimensional (2D) CNN representing images mapped by the visibility graph method. In our experiment, our algorithm achieves an average classification accuracy of 89.3%, an average sensitivity of 89.7%, an average specificity of 88.5%, and an average AUC of 0.947, which surpasses some state-of-the-art models trained on our data. In conclusion, our results indicate that the proposed method in this study could be effective and significance for massive screening of OSA patients in daily life. And our work provides an alternative framework for time series analysis.

In recent years, with the development of artificial intelligence, smart catering has become one of the most popular research fields, where ingredients identification is a necessary and significant link. The automatic identification of ingredients can effectively reduce labor costs in the acceptance stage of the catering process. Although there have been a few methods for ingredients classification, most of them are of low recognition accuracy and poor flexibility. In order to solve these problems, in this paper, we construct a large-scale fresh ingredients database and design an end-to-end multi-attention-based convolutional neural network model for ingredients identification. Our method achieves an accuracy of 95.90% in the classification task, which contains 170 kinds of ingredients. The experiment results indicate that it is the state-of-the-art method for the automatic identification of ingredients. In addition, considering the sudden addition of some new categories beyond our training list in actual applications, we introduce an open-set recognition module to predict the samples outside the training set as the unknown ones. The accuracy of open-set recognition reaches 74.6%. Our algorithm has been deployed successfully in smart catering systems. It achieves an average accuracy of 92% in actual use and saves 60% of the time compared to manual operation, according to the statistics of actual application scenarios.

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.

The accuracy of the traditional indoor fingerprint lo calization system is limited bymany factors,such as the density of the reference location node in the fingerprint database and the characteristics of the indoor environment.When the indoor environment changes dynami cally,the RSS fluctuates,and usually does notmeet the assumption of the same distribution.Therefore,it was difficult to obtain high-precision requirements for conventional fingerprint positioningmethod.Aiming at the problem that the traditional algorithm couldn’t locate accurately,an algorithm based on the indoor fingerprint database was designed and implemented.The algorithm adopted the idea ofmigration learning to embed different data sets into the latent feature space,and the adverse effects of environmental changes on the system weremitigated.The simulation results show that the average positioning error of this algorithm is 1.23m.

To further enhance the location precision of unknown nodes and solve the node location error in wireless sensor network,a location method based on cellular automata bat algorithm was presented.Mixed the idea of cellular automata and the bat algorithm and drawed into the cellular RCS niche technique and disaster mechanism,the algorithm could jump out of local optimum and increase the convergence speed.In order to verify the feasibility and efficiency,the proposed algorithm was verified through simulation of several benchmark functions.Then the algorithm implemented the CA-BA to node location prediction to increase the precision of the unknown node location.In the measured experiment,the results show that the proposed algorithm has higher accuracy compared to the improved PSO algorithm,which average localization error is less than 0.5m.