To increase the coverage of MIoTs(Maritime Internet-of-Things) devices, a coverage Optimization algorithm based on Deployment of MEC-UAV(UMCO: MEC-UAV-based Coverage Optimization algorithm) is proposed. In UMCO, MEC(Mobile Edge Computing) empowered UAVs(Unmanned Aerial Vehicles) is used to meet the network coverage demand for MIoT, and to maximize the network profit. We formulate a problem of joint MEC-UAVs deployment and their association with MIoT devices as an ILP(Integer Linear Programming) to maximize the network profit. An iterative algorithm is developed based on the Bender decomposition to solve the ILP. Finally, numerical results demonstrate that the proposed UMCO algorithm achieves a near-optimal solution.

To improve the stability and accuracy of satellite electromagnetic detection target tracking, a twin network based satellite electromagnetic detection target tracking algorithm is proposed to avoid the tedious target acquisition process. Firstly, a multi-satellite scheduling model is established for electromagnetic detection satellites, matching suitable satellites and working modes for electromagnetic detection targets, in order to complete the collection of target electromagnetic signals. Secondly, a twin network is used to train the target signal, obtaining the electromagnetic feature information and true position information of the target by eliminating interfering clutter in the target signal. Finally, a particle filter algorithm is used to achieve stable tracking of satellite electromagnetic detection targets. The test results show that the proposed algorithm can effectively improve the efficiency of target tracking, and has high stability and accuracy.

A node deployment and scheduling algorithm based on fitness function and zero tolerance coverage is proposed to solve the problems of sensor blind area and poor connectivity between sensor nodes in wireless sensor network coverage. The network coverage is considered as a two-dimensional plane, the relationship between the maximum coverage range of node sensing and the distance value is analyzed to obtain the attribute values of the target points with hot spot distribution and overlapping coverage. Then, according to the deployment indicators such as wireless sensor target point coverage, connectivity and candidate locations, the fitness function is used to calculate the optimal deployment relationship of indicators, and to obtain the redundant parameters of nodes. The redundant complementary nodes are found within the same sensing range to achieve replacement scheduling. The experimental results show that the algorithm performs well in terms of network coverage and scheduling effectiveness, and has strong comprehensive performance.

 Most of the existing detection methods for concept evolution are essentially based on supervised learning and are often used to solve the problem that only one novel class appears in a period of time. However, they can not handle the task of a class disappearing and recurring in streaming data. To address the above problems, an adaptive detection method for concept evolution based on weakly supervised ensemble (AD_WE) is proposed. The weakly supervised ensemble strategy is used to construct an ensemble learner to make local predictions on the training samples in the data block. Similar data with strong cohesion in the feature space are detected and clustered using local density and relative distance. The similarity of the clustering results is then compared to detect novel class instances and distinguish between different novel classes. And a dynamic decay model is established according to the characteristics of data change over time. The vanished class is eliminated in time, and the recurring class is detected through similarity comparison. Experiments show that the proposed method can respond to concept evolution in a timely manner, effectively identify vanished classes and recurring classes, and improve the generalization performance of the learner.

A dual-mode address hopping method is proposed to address security issues faced by the IoT(Internet of Things), such as resource scarcity and low obfuscation of traffic data. Address hopping diversity and unpredictability are enhanced through a dual-mode address selection algorithm, thereby solving the problem of limited address pool resources. Additionally, a dual-virtual address hopping method is introduced to enhance the obfuscation of data packets and reduce the correlation of network data. This method is demonstrated to be effective in reducing network data correlation, conserving IoT resources, increasing network address pool capacity, preventing data theft by attackers, and ensuring IoT security through simulation experiments conducted in an SD-IoT(Software Defined Internet of Things) environment.

Big data clustering processing has the problem of poor clustering effect and long clustering time for different types of data. Therefore, a big data clustering processing method based on the improved PSO-Means (Particle Swarm Optimization Means) clustering algorithm is proposed. The particle swarm optimization algorithm is used to determine the flight time and direction of unit particles during a cluster, preset the selection range of the initial cluster center, and appropriately adjust the inertia weight of unit particles. It eliminates the clustering defects caused by particle oscillation and successfully obtains the clustering center based on large-scale data. Combined with the spanning tree algorithm, the PSO algorithm is optimized from two aspects: sample skewness and centroid skewness. The optimized clustering center is then input into the k-means clustering algorithm to realize the clustering processing of big data. The experimental results show that the proposed method can effectively cluster different types of data, and the clustering time is only 0. 3 s, which verifies that the method has good clustering performance and clustering efficiency.

In order to solve the problem that traditional density peak clustering method without considering similarity of bands in information theory and number of bands in band selection, an adaptive density peak band selection method based on spectral angle mapping and spectral information divergence (SSDPC: Spectral angle mapping and Spectral information divergence Density Peaks Cluster)is proposed. SSDPC combines spectral angle mapping and spectral information divergence for density peak clustering band selection in hyperspectral images, replacing the traditional Euclidean distance to construct a band similarity matrix. By constructing a band scoring strategy, an important subset of spectral bands can be selected automatically and effectively. Using RX(Reed- Xiaoli) algorithm for anomaly detection on three sets of hyper-spectral datasets, the accuracy of anomaly detection is 1. 16% ,1. 18% and 0. 07% higher than that of Euclidean distance measurement under the similarity measure of SSDPC. Under the adaptive SSDPC band selection method, the accuracy of anomaly detection is 6. 49% ,2. 71% and 0. 05% higher than that of the original RX algorithm, respectively. The experimental results show that the SSDPC is robust, can improve the performance of hyper-spectral image anomaly detection and reduce its false alarm rate.

Aiming at the problem of information loss, such as details of remote sensing images reconstructed by a super-resolution algorithm, in order to ensure that remote sensor reconstruction images contain more texture and high-frequency information, a remote-sensitive image super resolution network is proposed based on a pyramid- based attention mechanism and the generation of confrontational networks. Firstly, a new pyramidal dual attention module is designed, including channel attention network and spatial attention network. Pyramid pooling is used instead of average pooling and maximum pooling in the channel attention network structure to enhance the feature representation capability from the perspective of global and local information. The spatial attention network structure adopts large scale convolution to expand the integration capability of local information, which can effectively extract texture, high frequency and other information. Secondly, the dense multi-scale feature module is designed to extract feature information at different scales using asymmetric convolution, and the extraction accuracy of texture, high frequency and other information is enhanced by fusing multi-level scale features through dense connection. Experimental validation is performed on the publicly available NWPU- RESISC45 dataset, and the experimental analysis shows that the algorithm outperforms the comparison methods in both subjective visual effect and objective evaluation metrics, and the reconstruction performance is relatively good. 

Existing industrial time series data anomaly detection algorithms do not fully consider the temporal data on time dependence. An improved HTM(Hierarchical Temporal Memory)-Attention algorithm is proposed to address this problem. The algorithm combines the HTM algorithm with the attention mechanism to learn the temporal dependencies between data. It is validated on both univariate and multivariate time series data. By introducing the attention mechanism, the algorithm can focus on the important parts of the input data, further improving the efficiency and accuracy of anomaly detection. Experimental results show that the proposed algorithm can effectively detect various types of time series anomalies and has higher accuracy and lower running time than other commonly used unsupervised anomaly detection algorithms. This algorithm has great potential in the application of industrial time series data anomaly detection.

Rapidity and accuracy are most important in medical treatment. Traditional blood tests rely on experienced physicians, which leads to low efficiency and accuracy. For the first time, a comprehensive determination method based on image recognition technology, named P-HSV ( Perimeter-Hue, Saturation, Value), is proposed for microfluidic blood sample chips. Size and color are used for integrated interpretation of blood types. Size interpretation is based on the contour perimeter and number of agglutination clusters within the reaction chamber, while color interpretation is based on categorization of the color saturation (S: Saturation) to brightness ( V: Value ) ratio of agglutination clusters within the reaction chamber. The grade of blood agglutination reaction is synthetically determined by size and color results. In this method, machine vision is used to determine the grade of blood agglutination reaction, resulting in accurate and rapid blood type determination. This reduces the subjective judgment of artificial judgment, improving the detection speed and accuracy greatly.

To provide more efficient solutions for handling high-dimensional matrices and applying SVD(Singular Value Decomposition) in the context of big data, with the aim of accelerating data analysis and processing, how to quickly calculate the eigenvalues and eigenvectors ( singular value singular vectors) of high-dimensional matrices is studied. By studying random projection and Krylov subspace projection theory, six efficient calculation methods are summarized, making comparative analysis and related application research. Then, the six algorithms are applied, and the algorithms in related fields are improved. In the application of spectral clustering, the algorithm reduces the complexity of the core step SVD( Singular Value Decomposition), so that the optimized algorithm has similar accuracy to the original spectral clustering algorithm, but significantly shortens the running time. The calculation speed is more than 10 times faster than the original algorithm. When this work is applied in the field of image compression, it effectively improves the operation efficiency of the original algorithm. Under the condition of constant accuracy, the operation efficiency is improved by 1 ~ 5 times.

A football competition risk intelligent warning method based on an improved Copula model is proposed to address the issues of large errors between warning values and actual values, and multiple false alarms in football matches. Based on the fuzzy comprehensive evaluation matrix, the evaluation system for football competition risk indicators is determined. The indicator level status is classified, the Copula function is selected, and an improved Copula football competition risk intelligent warning method is constructed to accurately judge football competition risks and reduce risk losses. The experimental results show that the interference suppression of this method is high, maintained above 20 dB, and have high anti-interference ability. It can effectively suppress interference. This method also reduces the error between the warning value and the actual value, reduces the number of false alarms in the warning, and verifies the practicality and feasibility of this method.

In order to avoid more duplicate data in the encryption process of IoT privacy data, which leads to higher computational complexity and reduces computational efficiency and security, an encryption method of IoT privacy data that combines DES(Data Encryption Standard) and ECC(Ellipse Curve Ctyptography) algorithms is proposed. Firstly, the TF-IDF(Tem Frequency-Inverse Document Frequency) algorithm is used to extract feature vectors from the privacy data of the Internet of Things. They are input into the BP(Back Proragation) neural network and are trained. The IQPSO( Improved Quantum Particle Swarm Optimization) algorithm is used to optimize the neural network and complete the removal of duplicate data from the privacy data of the Internet of Things. Secondly, the Data Encryption Standard and ECC algorithm are used to implement the primary and secondary encryption of the privacy data of the Internet of Things. Finally, a fusion of DES and ECC algorithms is adopted for digital signature encryption to achieve complete encryption of IoT privacy data. The experimental results show that the proposed algorithm has high computational efficiency, security, and reliability.

Aiming at the problem that it is difficult to extract and recognize the dynamic features of facial expression due to local occlusion, a dynamic recognition algorithm of facial expression with local occlusion based on deep learning is proposed, a deep belief network model is established, taking the output value of the previous layer as the input value of the next layer, a feature stacking unit is designed, the distribution of state variables of neurons in the visible layer, and the state variables of hidden neurons are calculated by taking the state value of the visible layer as the input value of the hidden layer according to the dynamic correlation of facial features. The recognition process is divided into two steps: training and forward propagation. The feature change rule is output. In the forward propagation process, the pixel point that conforms to the rule change is found, and the weight of the pixel point is solved. And as a loss function standard, the recognition weight of multiple positions on the face is used to constrain the recognition rate, and the dynamic recognition of facial partial occlusion expression is completed. Experimental data show that the proposed method can reduce image distortion and detail loss, improve image resolution, and achieve high recognition rate. It can complete efficient recognition for different local occlusion situations.

In order to solve the problems of low lip contour detection accuracy and poor visual speech synthesis effect, a Tibetan-driven visual speech synthesis algorithm based on audio matching is proposed. This algorithm extracts short-term energy and short-term zero-crossing rate from Tibetan-language-driven visual speech signal, establishes short-term autocorrelation function of speech signal, and extracts feature information in speech signal, so as to obtain the pitch track of Tibetan speech signal. Secondly, the temporal and spatial analysis model of lip is established to analyze the changing trend of lip contour in the pronunciation process, and the feature of lip contour is extracted by principal component analysis. Finally, the correlation between audio features and lip contour features is obtained through the input-output hidden Markov model, and Tibetan-driven visual speech is synthesized on the basis of audio matching. Experimental results show that the proposed method has high lip contour detection accuracy and good visual speech synthesis effect. 

To help readers quickly find the books they need and avoid overloading digital information, an intelligent recommendation algorithm for digital book resources based on tag similarity is proposed. Firstly, based on the entered user information in the digital library system, the user feature similarity and user interest similarity are obtained and regarded as comprehensive similarity indicators. Then, combined with the tag similarity index, the similarity nearest neighbors of the target user’s book resources are obtained. Finally, the tags of the book resources browsed by the user are put into a tag set, and the digital book resources that the target user likes are formed into a recommendation list through a hybrid recommendation method of user implicit behavior scoring and linear weighted fusion, and recommended to the target user. Experimental results show that the proposed algorithm performs better than traditional recommendation algorithms.

In response to the demand for simulation effects consistent with real hardware, a smooth transition method is proposed to enhance the imaging effects of the entire simulation system. By analyzing visual persistence effects and system imaging delays, a two-point third-order Hermite interpolation is used to handle smooth transition time and imaging color respectively. Through comparative experiments, the results demonstrate that this method can adaptively smooth the imaging of the entire simulation system, thereby having solved issues such as real-time dynamic imaging flicker and instability. The significance of this method lies in enhancing the imaging quality of the virtual 3D simulation experimental system for embedded microcontrollers, improving the visual effects of embedded microcontroller 3D simulation, and mitigating the impact of problems such as abrupt changes and artifacts. The significant application value of a virtual 3D simulation experimental system for embedded microcontrollers in the fields of education and design is presented.

Plaintext transmission is easily tampered with in distributed databases. To address the security risk, a multi-level control algorithm for secure access is proposed to distributed databases based on searchable encryption technology. The algorithm groups the authorized users according to the security level, and uses TF-IDF( Tem Frequency-Inverse Document Frequency) algorithm to calculate the weight of plaintext keywords, then uses AES (Advanced Encryption Standard) algorithm and round function to generate the key of the ciphertext, uses matrix function and inverse matrix function to encrypt the plaintext, and uploads the encryption results to the main server. And the Build Index algorithm is used to generate an index of ciphertext, and whether the user has access to ciphertext is reviewed based on the relevant attribute information of the user’s security level. After the review is passed, the user can issue a request for the number of ciphertext and keyword search. The server sends the ciphertext back to the user and decrypts it using a symmetric key method, achieving multi-level access control. The experimental results show that this method takes a short time in the encryption and decryption processes, and has good security access control performance.

To improve the efficiency of manual detection of illegal and irregular collection of personal information, an APP(Application) personal information evaluation system for illegal and irregular collection is developed based on techniques such as regular expression semantic analysis and machine learning. We conducted illegal and irregular detection on online apps, generated detection algorithms and rules, and focused on solving technical difficulties such as semi automated access to privacy policies, app detection engines, and dynamic sandboxes for custom ROM(Read Only Memory) . The developed prototype system is used to conduct regular technical testing on the apps listed on major application platforms. The testing results show that the system significantly improves the efficiency of comprehensive governance and judgment of illegal and irregular collection of personal information apps, and effectively supports the relevant work of higher-level management departments.

Aiming to the problem that the original manifold learning algorithm only utilizes spectral characteristics without incorporating spatial information, a locality preserving projections algorithm based on spatial-spectral (SS-LPP: Spatial-Spectral Locality Preserving Projections) union is proposed. Firstly, the weighted mean filtering algorithm is used to filter the dataset, fuse the spatial information with the spectral information, and eliminate the interference of noise, to increase the smoothness of similar data. Then, the label set is used to construct intra-graph and inter-graph. Through the intra-graph and inter-graph, identification features can be effectively extracted, and the classification performance can be improved. The effectiveness of the algorithm is verified on the Salinas dataset and the PaviaU dataset. Experimental results show that the algorithm can effectively extract data features and improve the accuracy of classification.

We present an improved target detection algorithm based on Yolov3-Tiny for gas station scene because of the low accuracy of target detection algorithm in gas station scenes. This algorithm takes Yolov3-Tiny model as the basic network, innovates Mosaic image enhancement method proposed in Yolov4 algorithm for data preprocessing, uses dense connection modules to reconstruct the feature extraction network, and adds CBAM (Convolutional Block Attention Module) attention mechanism and Pyramid Pooling Module into the network, finally target detection in the gas station scene is realized. The experimental results show that the improved algorithm improves the overall mAP by 8. 2% compared with the original algorithm, and can be more effectively applied to gas station target detection.

 Due to its low background noise, solar-blind ultraviolet photodetection technology is widely used in fields such as fire monitoring, missile detection, and military communication. Compared to other solar-blind ultraviolet sensitive materials, amorphous gallium oxide offers several advantages, including a bandgap that matches the solar-blind ultraviolet region, structural stability, and good mechanical strength. The horizontal metal-semiconductor-metal structured photodetectors are known for their simple production processes, ease of integration, and suitability for industrialization. Given the non-uniform distribution of the internal electric field and the photo-generated carriers along the thickness direction in horizontal devices, the thickness of the photoreceptive layer plays a crucial role in the performance of the photodetectors. In order to fabricate high- performance solar-blind ultraviolet photodetectors, amorphous gallium oxide thin films were prepared using low- temperature metal organic chemical vapor deposition method. Structural characterization of the films confirmed their amorphous nature, and the film surfaces were found to be relatively flat, with the optical absorption edge located within the deep ultraviolet spectral range. Solar-blind ultraviolet photodetectors were subsequently developed. As the thickness of the photoreceptive layer increased from 33. 2 nm to 133. 6 nm, the dark-current of the photodetector rose from 2. 33*10^-10 A to 2. 12*10^-8 A, and the photo-current under 254 nm illumination increased from 1. 66 * 10^-7 A to 3. 2 * 10^-5 A. Additionally, both the responsivity and the external quantum efficiency of the photodetectors increased by orders of magnitude with the increase in the photoreceptive layer thickness, reaching maximum values of 2. 91 A/ W and 1 419. 12% , respectively. The thickness-dependent characteristics of the photodetectors can be attributed to the interfacial high-defect layers, light absorption intensity, and the geometric parameters of the photodetectors. The photodetectors exhibited excellent wavelength selectivity, the current of each photo-detector under 365 nm illumination and the photo-current under 254 nm illumination differ by more than two orders of magnitude. Moreover, over the tested 5 cycles, the response / recovery behavior of each photodetector consistently demonstrates good repeatability and stability.

To address the problem that RGB ( Red Green Blue ) modal and thermal modal information representations are inconsistent in form and feature information can not be effectively mined and fused, a new joint attention reinforcement network-FCNet ( Feature Sharpening and Cross-modal Feature Fusion Net ) is proposed. Firstly, the image feature mapping capability is enhanced by a two-dimensional attention mechanism. Then, a cross-modal feature fusion mechanism is used to capture the target region. Finally, a layer-by-layer decoding structure is used to eliminate background interference and optimize the detection target. The experimental results demonstrate that the improved algorithm has fewer parameters and shorter operation times, and the overall detection performance of the model is better than that of existing multimodal detection models.