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In this study, we have fabricated a 368 nm LED with an epitaxial Indium Tin Oxide (ITO) contact layer. We analyze the thermal performance of the flip-chip LED with a symmetric electrode and metal reflective layer, applying ANSYS to build a coupled electro-thermal finite element model (FEM) of the temperature distribution in the multiple quantum wells (MQWs). We compare our system with the traditional Au-bump flip-chip LED and a flip-chip LED with a Distributed Bragg Reflector (DBR) layer. The simulation results have shown that the flip-chip LED with a metal reflective layer and symmetric electrode exhibits better heat dissipation performance, particularly at high input power. The influence of the insulating layer on the LED chip junction temperature is also examined. The simulation data establish an effect due to the thermal conductivity of the insulating layer in terms of heat dissipation, but this effect is negligible at an insulation layer thickness ≤1 µm.

In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV’s heading direction, considering its impact on the AUV’s speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV’s navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks.

It is of great significance to accurately detect ships on the ocean. To obtain higher detection performance, many researchers use deep learning to identify ships from images instead of traditional detection methods. Nevertheless, the marine environment is relatively complex, making it quite difficult to determine features of ship targets. In addition, many detection models contain a large amount of parameters, which is not suitable to deploy in devices with limited computing resources. The two problems restrict the application of ship detection. In this paper, firstly, an SAR ship detection dataset is built based on several databases, solving the problem of a small number of ship samples. Then, we integrate the SPP, ASFF, and DIOU-NMS module into original YOLOv3 to improve the ship detection performance. SPP and ASFF help enrich semantic information of ship targets. DIOU-NMS can lower the false alarm. The improved YOLOv3 has 93.37% mAP, 4.11% higher than YOLOv3 on the self-built dataset. Then, we use the MCP method to compress the improved YOLOv3. Under the pruning ratio of 80%, the obtained compressed model has only 6.7 M parameters. Experiments show that MCP outperforms NS and ThiNet. With the size of 26.8 MB, the compact model can run at 15 FPS on an NVIDIA TX2 embedded development board, 4.3 times faster than the baseline model. Our work will contribute to the development and application of ship detection on the sea.

Combining remote sensing images with deep learning algorithms plays an important role in wide applications. However, it is difficult to have large-scale labeled datasets for remote sensing images because of acquisition conditions and costs. How to use the limited acquisition budget to obtaina better remote sensing image dataset is a problem worth studying. In response to this problem, this paper proposes a remote sensing image quality evaluation method based on node entropy, which can be combined with active learning to provide low-cost guidance for remote sensing image collection and labeling. The method includes a node selection module and a remote sensing image quality evaluation module. The function of the node selection module is to select representative images, and the remote sensing image quality evaluation module evaluates the remote sensing image information quality by calculating the node entropy of the images. The image at the decision boundary of the existing images has a higher information quality. To validate the method proposed in this paper, experiments are performed on two public datasets. The experimental results confirm the superiority of this method compared with other methods.

In the era of rapid development of the Internet of things, deep learning, and communication technologies, social media has become an indispensable element. However, while enjoying the convenience brought by technological innovation, people are also facing the negative impact brought by them. Taking the users’ portraits of multimedia systems as examples, with the maturity of deep facial forgery technologies, personal portraits are facing malicious tampering and forgery, which pose a potential threat to personal privacy security and social impact. At present, the deep forgery detection methods are learning-based methods, which depend on the data to a certain extent. Enriching facial anti-spoofing datasets is an effective method to solve the above problem. Therefore, we propose an effective face swapping framework based on StyleGAN. We utilize the feature pyramid network to extract facial features and map them to the latent space of StyleGAN. In order to realize the transformation of identity, we explore the representation of identity information and propose an adaptive identity editing module. We design a simple and effective post-processing process to improve the authenticity of the images. Experiments show that our proposed method can effectively complete face swapping and provide high-quality data for deep forgery detection to ensure the security of multimedia systems.

Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.

Marine surveying is an important part of marine environment monitoring systems. In order to improve the accuracy of marine surveying and reduce investment in artificial stations, it is necessary to use high-precision GNSS for shipborne navigation measurements. The basic measurement is based on the survey lines that are already planned by surveyors. In response to the needs of survey vessels sailing to the survey line, a method framework for the shortest route planning is proposed. Then an intelligent navigation system for survey vessels is established, which can be applied to online navigation of survey vessels. The essence of the framework is that the vessel can travel along the shortest route to the designated survey line under the limitation of its own minimum turning radius. Comparison and analysis of experiments show that the framework achieves better optimization. The experimental results show that our proposed method can enable the vessel to sail along a shorter path and reach the starting point of the survey line at the specified angle.

Carbon monoxide (CO) burns or explodes at over-standard concentration. Hence, in this paper, a Wifi-based, real-time monitoring of a CO system is proposed for application in the construction industry, in which a sensor measuring node is designed by low-frequency modulation method to acquire CO concentration reliably, and a digital filtering method is adopted for noise filtering. According to the triangulation, the Wifi network is constructed to transmit information and determine the position of nodes. The measured data are displayed on a computer or smart phone by a graphical interface. The experiment shows that the monitoring system obtains excellent accuracy and stability in long-term continuous monitoring.

Continuous respiratory monitoring is an important tool for clinical monitoring. Associated with the development of biomedical technology, it has become more and more important, especially in the measuring of gas flow and CO2 concentration, which can reflect the status of the patient. In this paper, a new type of biomedical device is presented, which uses low-power sensors with a piezoresistive silicon differential pressure sensor to measure gas flow and with a pyroelectric sensor to measure CO2 concentration simultaneously. For the portability of the biomedical device, the sensors and low-power measurement circuits are integrated together, and the airway tube also needs to be miniaturized. Circuits are designed to ensure the stability of the power source and to filter out the existing noise. Modulation technology is used to eliminate the fluctuations at the trough of the waveform of the CO2 concentration signal. Statistical analysis with the coefficient of variation was performed to find out the optimal driving voltage of the pressure transducer. Through targeted experiments, the biomedical device showed a high accuracy, with a measuring precision of 0.23 mmHg, and it worked continuously and stably, thus realizing the real-time monitoring of the status of patients.

The realization of the Internet of Things greatly depends on the information communication among physical terminal devices and informationalized platforms, such as smart sensors, embedded systems and intelligent networks. Playing an important role in information acquisition, sensors for stereo capture have gained extensive attention in various fields. In this paper, we concentrate on promoting such sensors in an intelligent system with self-assessment capability to deal with the distortion and impairment in long-distance shooting applications. The core design is the establishment of the objective evaluation criteria that can reliably predict shooting quality with different camera configurations. Two types of stereo capture systems—toed-in camera configuration and parallel camera configuration—are taken into consideration respectively. The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting.

With the explosive growth of ocean data, it is of great significance to use ocean observation data to analyze ocean pycnocline data in military field. However, due to natural factors, most of the time the ocean hydrological data is not complete. In this case, predicting the ocean hydrological data by partial data has become a hot spot in marine science. In this paper, based on the traditional statistical analysis literature, we propose a machine-learning ocean hydrological data processing process under big data. At the same time, based on the traditional pycnocline gradient determination method, the open Argo data set is analyzed, and the local characteristics of pycnocline are verified from several aspects combined with the current research about pycnocline. Most importantly, in this paper, the combination of kernel function and support vector machine(SVM) is extended to nonlinear learning by using the idea of machine learning and convex optimization technology. Based on this, the known pycnocline training set is trained, and an accurate model is obtained to predict the pycnocline in unknown domains. In the specific steps, this paper combines the classification problem with the regression problem, and determines the proportion of training set and test formula set by polynomial regression. Subsequently, the feature scaling of the input data accelerated the gradient convergence, and a grid search algorithm with variable step size was proposed to determine the super parameter c and gamma of the SVM model. The prediction results not only used the confusion matrix to analyze the accuracy of GridSearch-SVM with variable step size, but also compared the traditional SVM and the similar algorithm. At the end of the experiment, two features which have the greatest influence on the Marine density thermocline are found out by the feature ranking algorithm based on learning.

Wireless local area network (WLAN) is a technology that combines computer network with wireless communication technology. The 2.4 GHz and 5 GHz frequency bands in the Industrial Scientific Medical (ISM) band can be used in the WLAN environment. Because of the development of wireless communication technology and the use of the frequency bands without the need for authorization, the application of WLAN is becoming more and more extensive. As the key part of the WLAN system, the antenna must also be adapted to the development of WLAN communication technology. This paper designs two new dual-frequency microstrip antennas with the use of electromagnetic simulation software—High Frequency Structure Simulator (HFSS). The two antennas adopt ordinary FR4 material as a dielectric substrate, with the advantages of low cost and small size. The first antenna adopts microstrip line feeding, and the antenna radiation patch is composed of a folded T-shaped radiating dipole which reduces the antenna size, and two symmetrical rectangular patches located on both sides of the T-shaped radiating patch. The second antenna is a microstrip patch antenna fed by coaxial line, and the size of the antenna is diminished by opening a stepped groove on the two edges of the patch and a folded slot inside the patch. Simulation experiments prove that the two designed antennas have a higher gain and a favourable transmission characteristic in the working frequency range, which is in accordance with the requirements of WLAN communication.

The Internet of Things is built based on various sensors and networks. Sensors for stereo capture are essential for acquiring information and have been applied in different fields. In this paper, we focus on the camera modeling and analysis, which is very important for stereo display and helps with viewing. We model two kinds of cameras, a parallel and a converged one, and analyze the difference between them in vertical and horizontal parallax. Even though different kinds of camera arrays are used in various applications and analyzed in the research work, there are few discussions on the comparison of them. Therefore, we make a detailed analysis about their performance over different shooting distances. From our analysis, we find that the threshold of shooting distance for converged cameras is 7 m. In addition, we design a camera array in our work that can be used as a parallel camera array, as well as a converged camera array and take some images and videos with it to identify the threshold.

The escalating demand for sustainable and high-performance energy storage systems has led to the exploration of alternative battery technologies for lithium-ion batteries. Sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs) have emerged as promising candidates because of their abundant Na/K resources, inexpensive costs, and similar chemistries to lithium-ion batteries. However, inherent challenges, such as large ionic radii, sluggish kinetics, and serious volume expansion, necessitate the development of robust and efficient anode materials for SIBs and PIBs. Vanadium nitride has attracted increasing attention as a viable anode due to its high electronic conductivity and potential capacity. In this study, we report on a flexible electrode for SIBs and PIBs that creates binder-free anodes by synthesizing vanadium nitride nanoparticles grown directly on carbon fiber cloths (VN/CFC). The unique architecture and binder-free nature of this anode ensure a robust electrode–electrolyte interface and enhance its electron/ion transport kinetics. The results demonstrate that the material exhibits an outstanding specific discharge capacity of 227 mAh g−1 after undergoing 1000 cycles at a current density of 2 A g−1 for SIBs. An electrochemical analysis indicated that the excellent performance of the material is attributed to the bind-free structure of carbon fiber cloth and the fast kinetics of surface pseudo-capacitive contribution. Furthermore, the material continues to demonstrate an impressive performance, even for PIBs, with a specific discharge capacity of 125 mAh g−1 after 1000 cycles at a current density of 1 A g−1. This study provides a new perspective for designing and developing advanced binder-free anodes for the storage of sodium and potassium ions, paving the way for high-performance energy storage applications.