In the D2D(Device-to-Device) communication system, when the distance between the source node and the destination node is too long, the communication delay is large and the communication process is interrupted. A relay selection algorithm for D2D communication based on social relationship and energy cache is proposed. Under the premise of limiting the location of the relay node, the algorithm considers the conventional conditions such as transmission rate, residual energy of the relay and residual buffer space, and considers the social relationship between nodes and the outage probability of the link. The weight of each link is calculated comprehensively, and the link with the highest weight is selected for data transmission. The simulation results show that the proposed algorithm effectively reduces the transmission delay of D2D communication link, improves the successful transmission probability of the system, and improves the stability of the relay system.

Aiming at the poor effect of D2D ( Device-to-Device) communication channel allocation, an optimization mathematical model of D2D communication channel allocation based on KM (Kuhn Munkras) algorithm is proposed. Based on the model of D2D communication system, the transmission rate of D2D communication channel is calculated, and the variables in the system are expressed in a two-dimensional coordinate system. A linear planning diagram is constructed, according to which the optimal transmission power of D2D users is solved. Based on KM ( Kuhn Munkras) algorithm, the mathematical model of D2D communication channel allocation optimization is established to realize D2D communication channel allocation. The experimental results show that the practical application effect of D2D communication channel allocation optimization mathematical model is better, and the throughput of communication system is greater.

During the process of digital video image acquisition, due to external environmental noise interference and low resolution of the original image, there may be significant distortion and artifacts during the compression process. Each compression and decompression introduces a certain amount of error, which gradually accumulates, resulting in poor compression performance. A research on digital video image compression method based on sparse encoding is proposed. Using multi threshold iterative methods to remove noise from digital video images is beneficial for subsequent image compression processing. The orthogonal basis coefficients of the denoised digital video image are obtained through sparse encoding method, redundant dictionary sparse encoding and compression transmission are performed on this coefficient, a multi frame decompressing artifact network is established, and the motion compensation module is used in the network to perform motion offset estimation and pixel compensation on the digital video image. The motion compensated frames are inputted into the decompressing artifact module to eliminate compressed artifacts and achieve digital video image compression. The experimental results verify that this method can effectively remove artifacts in compressed digital video images, and has high compression efficiency and signal-to-noise ratio.

To solve the problem of difficulty in segmentation of weak edge ultrasound images, an improved CNN (Convolutional Neural Networks) based weak edge ultrasound image segmentation method is proposed. The method first uses stationary wavelet transform to remove the noise in the image, and then uses weighted least square filter to enhance the image edge details. Then, an improved convolutional attention module is added to the residual network model to extract image features. Finally, the image segmentation accuracy is improved by optimizing the loss function. The experimental results show that the proposed method has good performance in processing weak edge details of ultrasound images and can improve the segmentation accuracy of medical ultrasound images. 

The energy consumption of a UAV(Unmanned Aerial Vehicle) determines the length of its operational cycle. To address the issue of low communication-to-energy consumption ratio, a reinforcement learning-based UAV path planning solution is proposed to reduce energy consumption while maintaining high communication quality. The continuous flight space is divided into multi-layer two-dimensional grids to facilitate the generation of UAV state points, and a reward function based on communication quality parameters and energy consumption parameters is established. The Q-Learning algorithm is employed to learn and obtain the path with the optimal communication-to-energy consumption ratio. Experimental results show that the path planned by this learning model can achieve a higher communication-to-energy consumption ratio, demonstrating its practical value.

In response to the issues of inaccurate neighborhood selection and deficiencies in the metric used in the LLE(Locally Linear Embedding) algorithm, which hinder its ability to extract the true manifold structure, an algorithm called AN-RWLLE ( Locally Linear Embedding Algorithm Based on Adaptive Neighborhood and Reconstruction Weight) is proposed. Firstly, the local neighborhoods of each sample point are identified by calculating the cosine similarity of high-dimensional sample points, followed by an adaptive selection of the optimal neighborhood within those neighborhoods. Secondly, the distance features and structural features of the sample points within the optimal neighborhood are combined to thoroughly explore the manifold structure of high- dimensional data and achieve weight reconstruction. Lastly, support vector machines are employed for feature recognition, preserving the intrinsic characteristics of high-dimensional data in a lower-dimensional space. Experimental results demonstrate that the AN-RWLLE algorithm exhibits excellent visualization, clustering performance, and effective feature extraction capabilities on two sets of bearing fault datasets.

Aiming at the problem of low spectrum efficiency in MIMO(Multiple Input Multiple Output) C-IoT (Cognitive Internet of Things) systems, a block coordinate descent algorithm based on alternating iterative assisted by IRS(Intelligent Reflecting Surface) is proposed. System weighted sum rate is maximized by jointly optimizing active beamforming at secondary transmitter and passive beamforming at IRS, and is constrained by the interference power at the primary receiver, the transmit power at the secondary transmitter, and the unit mode at the IRS. After decomposing the complex non-convex optimization problem into subproblems, the subproblems are processed using the Lagrange Dual method and the Successive Convex Approximation method, respectively. The simulation results show that the proposed algorithm can converge quickly in a multi-antenna user scenario, and the spectrum efficiency of the C-IoT system can be effectively improved by increasing the number of IRS reflective elements or correctly deploying the location of the IRS. 

In order to solve the problems of large proportion of small targets in optical remote sensing data, the aspect ratio is large, and multiple targets are closely arranged and difficult to detect,we present an optical remote sensing small ship target detection algorithm based on the YOLO V5(You Only Look Once V5), UPCBAM- RYOLO V5 (Upsampling Convolutional Attention Block Module-RYOLO V5) algorithm. An up-sampling attention mechanism module is designed to enhance the feature extraction ability of small size targets. The rotation angle loss is introduced into the frame regression formula to improve the algorithm’s perception ability of the ship’s appearance and direction. Based on the experiment of small ship target datasets composed of GF1 and GF2, the results show that the UPCBAM-RYOLO V5 algorithm model improves the positioning accuracy and classification accuracy of small ship target detection. The P value, R value, and MAP(Mean Average Precision) value reach 93%, 94%, and 95% respectively, which are 3%, 7%, and 7% higher than the original YOLO V5 model. In the upsampled attention-mechanism module added location ablation experiment to the network, the results show that compared with the addition of UPCBAM in Backbone and Prediction, the addition of UPCBAM in Neck has the greatest influence on the detection of small target ships in remote sensing images. The performance is the best, with P value, R value, and MAP value increased by 5%, 4%, and 2%, respectively. The UPCBAM-RYOLO V5 model is proved to have a certain research significance in optical remote sensing ship small target detection.

In order to improve the contrast enhancement effect of remote sensing blurred images and increase clarity, a method based on mean filtering for remote sensing blurred image contrast enhancement is proposed. Firstly, a fast median adaptive mean filtering algorithm is used to denoise the entire remote sensing blurred image. Secondly, combining the fractal self-similarity feature of remote sensing image edge and the change of gray scale gradient, the edge points of the image are extracted. On this basis, the whole area of the image is divided into bright areas and dim areas. Finally, the detail preserving mapping algorithm and perceptual contrast mapping method are used to enhance the contrast of the two regions, respectively, and the overall contrast of the remote sensing blurred image achieving color restoration of the image. The experimental results show that the proposed method can effectively denoise images, with an absolute mean difference of less than 0. 85, and exhibits good performance in enhancing image contrast and clarity. 

When photovoltaic panels are under uneven solar irradiation, the problem of low power generation efficiency arises. In order to solve the problem a chaotic Harris hawk algorithm combined with the conductivity increment method is proposed. The Harris Hawk algorithm introduces Levy flight function and Henon chaotic mapping to expand the search range of the algorithm in the early stage of tracking. Then the optimal individual strategy is introduced, which can further reduce the number of iterations of the algorithm. The algorithm makes it easier for the system to jump out of the local maximum power point, while in the late stage of tracking, the algorithm runs precisely in a small range, improving the local exploitation capability. The use of the conductance increment method alleviates the power oscillations when the system is located near the GMPP (Global Maximum Power Point) and stabilizes the output. 

Aiming at the problems of difficult detection and low recognition accuracy of brain cognitive diseases, a multi-head attention-guided convolutional neural network ( MAGINet: Multi-Head Attention-Guided Convolutional Network) is proposed. This integrates the local dependent modeling ability of the convolutional neural network with the global dependent modeling ability of the attention mechanism. It is used to identify NC (Normal), EMCI(Early Mild Cognitive Impairment), LMCI(Late Mild Cognitive Impairment), and AD (Alzheimer’s Disease), and to explore the complete evolution from NC through MCI(EMCI and LMCI) to AD. First, the complementary information of three FCN(Functional Connectivity Network) variants is integrated to form a multi-view learning framework. Secondly, a new multi-head attention module is designed in the convolutional neural network module in each view. By completing self-attention, channel attention, and spatial attention successively, it helps to model the global dependence relationship, compensates for the local mechanism of the convolutional neural network, optimizes the performance of the model, and proves its effective information extraction ability. Finally, the model is applied to several encephalopathy classification experiments to prove the strong universality and repeatability of the model. 

The phenomenon of oil theft at well sites is an important issue that affects the safe production and stable operation of oil fields. The current behavior recognition methods pay less attention to the need for detecting oil theft in well pads, and there are often limitations in the application of the oil theft target feature recognition process. An algorithm for identifying oil theft behavior at well sites is proposed based on 3D attention residuals. This network consists of multiple three-dimensional attention residual blocks, which embed channels and spatiotemporal attention modules in each residual block to obtain more feature discrimination information and enhance the model’s attention to important features. The effectiveness of the algorithm is varified on the dataset of oil theft behavior at the well site. The experimental results indicate that, compared to similar algorithms, this method has higher recognition accuracy. It can provide a reference for the practical application of automatic detection of oil theft behavior in oilfield well sites.

The core of the cadre information management system lies in enhancing the leadership level of the Party, strengthening the governance capacity of the Party, and reinforcing the Party’s self-construction, thereby promoting the sustained and healthy development of the cause of socialism with Chinese characteristics. By introducing information technology, the system improves the scientificity and standardization of cadre selection and appointment through modeling, visualization, and intelligence. It comprehensively supervises the daily ideology, work, and conduct of cadres, achieving comprehensive assessment and standardized management. The enhanced systemimproves the accuracy and efficiency of cadre selection and appointment, it enhances daily cadre management and supervision,it achieves the scientific and standardized performance assessment of cadres. In conclusion, through the cadre information management system, the Party’s level of development will be further elevated, and organizational work will progress towards scientification, standardization, and refinement, thereby achieving a new leap forward in the construction of Party member and cadre teams. 

Pilots need to simultaneously process multiple information sources and tasks while performing tasks, which increases the workload of mental labor. In order to improve flight safety and pilot work efficiency, a multidimensional comprehensive evaluation model for pilot mental workload based on random forest algorithm is studied. A linear finite pulse response bandpass filter is used to process EEG(Electroencephalogram) signals, removing high-frequency and low-frequency noise, calculating mismatched negative waves, obtaining linearly interpolated EEG signal sampling points, and extracting power spectral density and energy features of each rhythm based on overlapping sampling points in the EEG signal neighborhood. A multi-dimensional comprehensive evaluation model of the random forest algorithm is constructed, determine the output points of each signal fluctuation frequency, and combine the voting mode to obtain the optimal classification results of multi-dimensional mental load, achieving comprehensive evaluation of pilot mental load. The experimental results demonstrate that the proposed method has high classification accuracy and can accurately evaluate the mental workload status of pilots.

In order to explore the international research hotspots and international cooperation trends of scientific and technological resources, this paper uses the method of scientific bibliometrics and visual analysis to analyze the scientific and technological resources literature included in Web of Science. The results show that scientific and technological resources are in a period of rapid growth. The United States, Spain, Brazil and China are the main publishing countries. The core research institutions are the University of Sao Paulo and the Chinese Academy of Sciences. The main journals are “Sustainability” and “ Strategic Management Journal”. The research hotspots are technical resources, communication technology and data collection.

In order to enrich the experimental content of the university experimental teaching platform, a programmable brushless motor control experimental system is developed. To address the problems of inflexible control mode and poor versatility in existing brushless controllers, the experimental system provides a repeatable development scheme, which can directly drive a Hall brushless motor by establishing a free mapping between control signals and motor outputs using its accompanying upper computer programming software. The hardware is based on ARM(Advanced RISC Machine)-Cortex M3 MCU(Multipoint Control Unit), which can support digital level, analog signal and PWM(Pulse Width Modulation) signal input at the same time. The upper computer software is developed based on PyQt5, including the guide window interface and the main window. After testing, the motor can communicate with external devices according to the customized control signal and can reach the expected design speed of the program, and the system can provide a new experimental solution for teaching of motor control.

A portable nitrogen concentration detector based on the STC89C52 single chip microcomputer and an oxygen sensor is designed for the purpose of real-time detection of gas concentration in the gas regulating storage room. According to the principle that the concentrations of nitrogen and oxygen in the atmosphere is almost complementary when the rare gases are ignored, the monitoring of nitrogen concentration in grain storage rooms can be converted into monitoring oxygen concentration, which can ultimately be converted back to calculate the percentage of nitrogen concentration in the environment. When the nitrogen concentration percentage in the grain storage room is below the preset value of 97% , an audible and visual alarm function can be activated, while the nitrogen concentration percentage, temperature, and humidity information inside the grain storage room can be displayed in real-time. The hardware system includes signal acquisition circuits such as an oxygen sensor and a temperature and humidity sensor, signal conditioning circuits, analog-to-digital conversion circuits, an STC89C52 single chip microcomputer, and an audible and visual alarm unit. For software design, the C language and Keil development environment are selected. The experimental test results indicate that the detector can display environmental information in real-time and immediately trigger an alarm when the nitrogen concentration in the environment falls below the preset value of 97% , fulfilling its expected functions.

 Based on the background that colleges and universities focus on talent cultivation work, 31 universities in Jilin Province were analyzed in order to provide reference significance for the Ministry of Education to explore the potential of universities and allocate college resources rationally. Taking 31 universities in Jilin province as samples in 2017, 24 indicators were selected from the number of students, faculty level and school conditions of college profiles, and descriptive statistical analysis, factor analysis, cluster analysis and typical correlation analysis were used. The study showed that Jilin University outperformed other colleges and universities in terms of the number of students, teachers’ resources, and schooling conditions, followed by Northeast Normal University directly under the Ministry of Education and 12 universities under the province, and they all outperformed 17 universities directly under the local government. Moreover, the titles of teachers with high education are also generally high, and the titles of teachers with low education are also generally low.

In order to avoid interference of language meaning on English oral translation results and improve the accuracy of English oral translation, an accurate English oral translation system based on attribute features is proposed. The system extracts oral semantic feature parameters by analyzing input data variables. Semantic feature parameters can capture differences between vocabulary and expression methods, improving translation accuracy. Variational autoencoders is used to capture effective information of features and obtain English spoken semantic matching results. Oral semantic matching is encoded and decoded, and translation rules are set based on parameters to identify interpreting ambiguous word parameters, and CBOW(Continuous Bag-of-Words) model is used to identify and evaluate parameters. Translation rules for complex sentence structures are established and they are connected through semantic translation based on these rules, forming accurate English oral translation results. The experimental results show that the designed English oral translation system has high translation accuracy and can achieve accurate English oral translation. Therefore, it indicates that the studied translation system can meet the requirements of high-precision English oral translation.

In the context of rapid advancements in information technology, the application of digital technology in higher education has become a crucial means of driving educational reform. Addressing issues such as unequal resource distribution and low teaching efficiency in traditional teaching models, the paper proposes the “6i” digital education application and support service system, encompassing teaching (iLearn), learning (iSocial), evaluation (iSense), management (iEdu), services (iHelp), and environment (iMeta). Through specific measures such as the construction of three types of classrooms, the development of digital teaching resources, teaching model reform, digital teaching quality management, and the exploration of new digital technology applications in teaching, Jilin University has achieved a digital transformation of teaching resources and innovation in teaching models. The results indicate that the application of digital technology significantly enhances teaching quality and learning outcomes, providing students with more opportunities for autonomous and personalized learning. The findings of this study offer valuable insights for other universities in promoting digital transformation in education, helping to build a more open, efficient, and intelligent educational system, and advancing high-quality educational development.

With the development of Internet technology, the application of AI(Artificial Intelligence) technology in education has become a key trend of educational innovation. The research focuses on the role of AI in key areas such as classroom teaching optimization, student evaluation, exam assessment, and teacher training. By analyzing existing AI education products, summarizing the current application status of AI in the field of education, and looking forward to future development trends. The results indicate that AI technology can significantly improve teaching efficiency, achieve intelligent analysis of students’ learning situations, and provide personalized teaching support. In addition, AI has shown great potential in the allocation of educational resources and the analysis of learner characteristics, which can help achieve personalized and precise education. Although there are challenges in terms of application scope, research and development efforts, and application modes, the in-depth application of AI technology is expected to promote the development and progress of the education industry.

The MCU(Microcontroller Unit) programming language is mainly C language implemented in soft logic, which implements specific functions by sequentially executing instructions, and can not avoid the shortcomings of low speed. The MCU based on FPGA(Field-Programmable Gate Array) and ARM(Advanced RISC Machines) Cortex-M0 is designed to obtain a high-speed system while still retaining the advantages of MCU. FPGA-based MCUs execute in parallel because the logic is directly implemented by the hardware, which greatly improves the speed and can be widely used in complex logic control and data operations and processing. Based on the analysis of the ARM Cortex-M0 core and the AMBA(Advanced Microcontroller Bus Architecture) bus system, each unit is designed for the MCU system. The Verilog code of each module is designed according to the characteristics of each module. The simulation results verifys that the module functions well. The design of special peripherals based on the FPGA platform and the verification of hardware algorithms are explored, which reflects the high flexibility and efficiency of the FPGA platform for design of MCUs. Taking the timer interrupt system as an example, combined with software and hardware, a comprehensive simulation of the entire MCU system is carried out, the working state of the ARM Cortex-M0 core in actual operation is analyzed, and the data communication and scheduling between each module of the bus system are analyzed, verify the feasibility and efficiency of the FPGA platform to develop MCU. The MCU is designed based on a reconfigurable platform, which can customize peripheral functions according to needs, and has the advantage of higher flexibility than traditional MCUs.