With the rapid development of Internet of things (IoT) and mobile communication technology, Internet of vehicles (IoV) has potential prospects in providing live streaming services and improving the quality of experience (QoE) of ground mobile vehicles (GMVs). However, when the communication link is blocked in emergency situations such as road congestion and base station (BS) paralysis, it is still an arduous task to provide realtime high-definition video for vehicle users. Fortunately, as the aerial mobile BS, unmanned aerial vehicle (UAV) is expected to be deployed for emergency communication recovery in extreme scenarios. But high mobility of GMVs and UAV leads to dynamic changes of network topology, how to establish a stable communication link between UAV and GMVs to ensure the QoE of vehicle users has become a huge challenge. Therefore, this study proposes a novel live video transmission scheme based on scalable video coding (SVC). By jointly optimizing the UAV trajectory, video layer selection, and bandwidth resource allocation, our objective is to maximize long-term QoE while reducing UAV’s flight energy consumption. To maximize QoE, time delay and bitrate switching at GMVs’ receiver are set as penalty items while maximizing the reward of video quality. The joint optimization problem is modeled as a Markov decision process (MDP), and is solved by the soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm, which is based on the maximum entropy framework. Extensive simulation results based on real-world datasets show that compared with other DRL algorithms, the proposed scheme has excellent performance in all respects.

Automated environmental sound recognition has clear engineering benefits; it allows audio to be sorted, curated, and searched. Unlike music and language, environmental sound is loaded with noise and lacks the rhythm and melody of music or the semantic sequence of language, making it difficult to find common features representative enough of various environmental sound signals. To improve the accuracy of environmental sound recognition, this paper proposes a recognition method based on multi-feature parameters and time–frequency attention module. It begins with a pretreatment that relies on multi-feature parameters to extract the sound, which supplements the phase information lost by the Log-Mel spectrogram in the current mainstream methods, and enhances the expressive ability of input features. A time–frequency attention module with multiple convolutions is designed to extract the attention weight of the input feature spectrogram and reduce the interference coming from the background noise and irrelevant frequency bands in the audio. Comparative experiments were conducted on three general datasets: environmental sound classification datasets (ESC-10, ESC-50) and an UrbanSound8K dataset. Experiments demonstrated that the proposed method performs better.

Purpose: The purpose of this paper is to solve the problem that the location of the initiation point cannot be measured accurately in the shallow underground space, this paper proposes a method, which is based on fusion of multidimensional vibration sensor information, to locate single shallow underground sources. Design/methodology/approach: First, in this paper, using the characteristics of low multipath interference and good P-wave polarization in the near field, the adaptive covariance matrix algorithm is used to extract the polarization angle information of the P-wave and the short term averaging/long term averaging algorithm is used to extract the first break travel time information. Second, a hybrid positioning model based on travel time and polarization angle is constructed. Third, the positioning model is taken as the particle update fitness function of quantum-behaved particle swarm optimization and calculation is performed in the hybrid positioning model. Finally, the experiment verification is carried out in the field. Findings: The experimental results show that, with root mean square error, spherical error probable and fitness value as evaluation indicators, the positioning performance of this method is better than that without speed prediction. And the positioning accuracy of this method has been improved by nearly 30%, giving all of the three tests a positioning error within 0.5 m and a fitness less than 1. Originality/value: This method provides a new idea for high-precision positioning of shallow underground single source. It has a certain engineering application value in the fields of directional demolition of engineering blasting, water inrush and burst mud prediction, fuze position measurement, underground initiation point positioning of ammunition, mine blasting monitoring and so on.

Aiming to solve the problem of the limited measuring range for angular motion parameters of high-speed rotating projectiles in the field of guidance and control, a self-adaptive measurement method for angular motion parameters based on the electromagnetic induction principle is proposed. First, a framework with type bent “I-shape” is used to design triaxial coils in a mutually orthogonal way. Under the condition of high rotational speed of a projectile, the induction signal of the projectile moving across a geomagnetic field is acquired by using coils. Second, the frequency of the pulse signal is adjusted self-adaptively. Angular velocity and angular displacement are calculated in the form of periodic pulse counting and pulse accumulation, respectively. Finally, on the basis of that principle prototype of the sensor is researched and developed, performance of measuring angular motion parameters are tested on the sensor by semi-physical and physical simulation experiments, respectively. Experimental results demonstrate that the sensor has a wide measuring range of angular velocity from 1 rps to 100 rps with a measurement error of less than 0.3%, and the angular displacement measurement error is lower than 0.2°. The proposed method satisfies measurement requirements for high-speed rotating projectiles with an extremely high dynamic range of rotational speed and high precision, and has definite value to engineering applications in the fields of attitude determination and geomagnetic navigation.

To reduce the large size of a gunshot recognition network model and to improve the insufficient real-time detection in urban combat, this paper proposes a fast gunshot type recognition method based on knowledge distillation. First, the muzzle blast and the shock wave generated by the gunshot are preprocessed, and the quality of the gunshot recognition dataset is improved using Log-Mel spectrum corresponding to these two signals. Second, a teacher network is constructed using 10 two-dimensional residual modules, and a student network is designed using depth wise separable convolution. Third, the lightweight student network is made to learn the gunshot features under the guidance of the pre-trained large-scale teacher network. Finally, the network’s accuracy, model size, and recognition time are tested using the AudioSet dataset and the NIJ Grant 2016-DN-BX-0183 gunshot dataset. The findings demonstrate that the proposed algorithm achieved 95.6% and 83.5% accuracy on the two datasets, the speed was 0.5 s faster, and the model size was reduced to 2.5 MB. The proposed method is of good practical value in the field of gunshot recognition.

The problem that the fuze overload signal sticks and is not easily identified by the counting layer during the high-speed intrusion of the projectile is an important factor affecting the explosion of the projectile in the specified layer. A three-pole plate dual-capacitance acceleration sensor based on the capacitive sensor principle is constructed in this paper. The modal simulation of the sensor structure is carried out using COMSOL 6.1 simulation software, the structural parameters of the sensor are derived from the mechanical properties of the model, and finally the physical sensor is processed and fabricated using the derived structural parameters. The mechanical impact characteristics of the model under different overloads were investigated using ANSYS/LS-DYNA, and the numerical simulation of the projectile intrusion into the three-layer concrete slab was carried out using LS-DYNA. Under different overload conditions, the sensor was tested using the Machette’s hammer test and the output signal of the sensor was obtained. The output signal was analyzed. Finally, a sensor with self-powered output, high output voltage amplitude, and low spurious interference was obtained. The results show that the ceramic capacitive sensor has a reasonable structure, can reliably receive vibration signals, and has certain engineering applications in the intrusion meter layer.