In order to solve the program code similarity detection measurement which ignores the program semantics and the invalid measurement, we present
 an AST(Abstract Syntax Tree) based on the program code similarity measure method. Through the pretreatment redundancy removal in AST generation and the lexical grammar analysis, get the corresponding AST; and then according to the adaptive threshold method,using the AST traversal which include the sequence and process attributes to take the similarity calculation;finally,determine whether plagiarism and generate the test report.The experimental results show that this method can effectively detect a variety of plagiarism java code.

In order to improve the search speed and shorten the search time of robot path planning，the characteristics of various algorithms is summarized． First，the history of mobile robot development and outline the key technologies of path planning are reviewed． Secondly，the mobile robot path planning is classified and summarized． From the perspective of the mobile robot's grasp of the environment，the mobile robot path planning is divided into two categories: global planning and local planning． Then the related algorithms of global planning and local planning are reviewed，and the development status，advantages and disadvantages of related algorithms are summarized． Finally，the future development trend of robot path planning technology in further research，hybrid algorithm， multi-robot collaboration， complex environment and multi-dimensional environment is pointed out．

 Because the request of automobile safety is higher and higher, drivers should clearly ensure the surrounding environment of the car, a kind of car surveying technology is proposed. Video mosaicing technology is the basis of surveying technology. In order to meet the requirement of vehicle instantaneity, this paper uses FPGA to achieve the video mosaicing. The system includes video capture module, video pre-processing module, video mosaicing module and video display module. The paper uses Cyclone IV development board of Altera company to complete the system and uses double DDR2 to complete Ping-Pong buffer. We use sift algorithm to match videos and linear fusion algorithm to mosaic videos. The system is verified by experiments and its feasibility is proved.

 To enharce the service quality of the internet catering industy, to better meet the needs of customers at ang time pointmed, the research is an intelligent ordering system based on Android Studio development environment, C/ S structure, and Gradle and Genymotion development tools. This paper introduces the principle of image loading and the interaction process between activities and the background. The system has realized its functions such as menu browsing, commodity collecting, food ordering, user center and food choosing by shaking. The result of tests show that the system has a good user interaction experience and can be operated easily. And it will have an extensive application prospect.

For the needs of three-dimensional geological project model display, a three-dimensional model network sharing methods based on Unity3D is presented. We have designed and implemented major functions, such as picking up objects and geological information queries, hiding and showing geological objects, real time cross sectioncutting and model blasting. The system is focused on the three-dimensional scale display. It is convenient for researchers to obtain information in the deep geological and achieved good results.

To ensure the authenticity and information safety of the image content, application areas of passive forensics techniques are stated. The ideas and main steps of some typical algorithms for digital image source device detection, splicing detection, copypaste detection, blur detection and JPEG （Joint
Photographic Experts Group）compression detection have been analyzed. The algorithm performance and feasibility have been briefly evaluated, and the future technical route and development direction of passive forensics techniques are prospected.

In order to promote the development of path planning technology，the planning ideas and forms of path planning are analyzed． The path planning algorithms are divided into the traditional classical algorithms and modern intelligent algorithms in two categories，and some commonly used algorithms are analyzed and summarized．And the current research hotspots and future development trends are pointed out from the three aspects of improving the application of modern intelligent algorithms in path planning，amalgamation of multiple algorithms and the research of four-dimensional path planning algorithms for multiple UAVs．

In order to realize the intelligent perception oriented CNC (Computer Numerical Control) system，
based on the characteristics of the network communication and open architecture of the intelligent CNC system，
we design a secondary development platform of the intelligent CNC system and the reconfigurable hardware
structure of the system，and put forward the multi-channel signal processing technology，which ensures the real-
time processing，reduces the interference between the signals and improves the processing efficiency． In order to
satisfy the error caused by nonlinearity and time delay in CNC system，the adaptive control of servo motor of CNC
machine tool is realized based on the improved MFAC(Model-Free Adaptive Control) data driving algorithm．
With the SSB field bus developed by ourselves to collect sensor signals，the design of data frame format and
communication protocol is completed to ensure the low power consumption and high reliability of the information
acquisition system． The improved MFAC data-driven algorithm is tested by the test and verification platform
based on blue sky CNC，and good test results are achieved．

As crowd-sensing networks can efficiently address the key problem of large-scale sensor networks,i. e. large quantities of deployment and maintenance cost, it becomes a research hotspot for the Internet of Things. This paper presents the state-of-the-art of crowd-sensing networks. We first introduce the definition, the origin and the framework of crowd-sensing networks, followed by the analysis of its characteristics and the comparison with traditional sensing networks. And then, recent researches of crowd-sensing networks are surveyed in terms of environment sensing, public infrastructure sensing and social sensing. Finally, we point out three important directions for the future research of Crowd-Sensing Networks and analyze their challenges, to give a reference and help for the future study.

The process of behavior recognition can be regarded as the combination of feature extraction and
classifier to a large extent． Compared to static image object recognition，video feature extraction of human
behavior recognition is more susceptible to such factors as dynamic background，acquisition device motion，
perspective and illumination，so it poses great challenges to researchers． Based on the systematic classification of
behavior recognition feature extraction，according to the different types of behavior recognition feature extraction
methods and common behavior recognition database，the behavior recognition feature extraction is systematically
classified to expound the latest research progress． And the current research difficulties and possible future
research directions are discussed．

The clinical method of blood glucose measurement at present is extracting blood from the patients, which is based on the principle of biochemical reaction. This invasive methord is painful, susceptible and discontinuous. To deal with these problems, we focus on a non-invasive way on the strength of the photoplethysmography detection theory by irradiating the finger with two beams of near infrared lights of 805 nm and 940 nm. The wave of 805 nm is sensitive to glucose, while the 940 nm one acts as the reference. By processing the information of the blood glucose extracted from the transmitted lights, the blood glucose value can be calculated.Experiments have convinced that the continuous non-invasive blood glucose measurement can be achieved.

In order to solve the human intervention problem of existing human target recognition and tracking
system，an automatic human detection and tracking system is designed，which is composed of embedded system，
wireless communication technology and upper computer． The automatic detection and tracking system is divided
into two parts: the upper computer and the lower computer． Using STM32F103ＲCT6 as the control unit，the
lower computer detects the position of the human body through the SHＲAP-GP2Y0A21YK0F infrared ranging
sensor，and then controls the steering gear． The steering gear is equipped with a camera to collect the video
signal which is transmitted to the upper computer using WIFI( Wireless Fidelity) wireless technology． The upper
computer is developed by Eclipse-Android system development platform，which can display the video signal in
real time in the monitoring center． It has been proved that the system is easy to install and operate，can
accurately realize the automatic recognition and tracking process of human body，and can be widely used in the
industry of indoor non-interference infrared work．

According to the image fusion of optical and SAR(Synthetic Aperture Radar), the multimodal and multiscale features including pixel features, texture features and edge features were analyzed in order to improve the traditional homologous image registration and fusion algorithm. Then the improved SURF(Speeded Up Robust Features) operator, texture analysis and contour extraction algorithm were adopted to obtain the multimodal and multiscale features of the heterologous images. By standardization algorithm of the fuzzy scale and dimension, the differences between the feature pairs of the heterologous images were overcome, which made the matching of the feature pairs available. The accuracy of registration and fusion were ensured through the method of fuzzy
correlation coefficient, and the registration and fusion of optical-SAR images were completed. Finally, the modified algorithm was verified and compared with the traditional fusion methods. Experimental results show that the multimodal registration and fusion algorithm can improve the precision and adaptability of optical-SAR registration. The average accuracy rate of registration and fusion can reach to 87. 7%, which can satisfy the requirement of high precision registration and fusion.

In order to achieve to detect a variety of physiological signals form ECG ( electrocarcliogram)monitoring signal, reducing the complexity of the monitoring equipment, according to the impact of breathing motion on the ECG, it presents a respiratory information extracted from the ECG (ECG-derived respiratory signal, EDR) algorithm. First, according to Pan & Tompkins algorithm extracted from the ECG R-wave, and then, using a natural cubic spline interpolation algorithm to estimate the baseline ECG and remove baseline wander, getting clean ECG, finally, get clean ECG R-wave, the use of natural spline interpolation algorithm is applied to obtain R wave amplitude modulation signal which contains respiration information. In paper, it selected Fantasia database, which in addition to providing the ECG signal, it also gave a breathing signal synchronized recording. It used Matlab software to verify the proposed algorithm, associated analyzed and compared. The simulation results showed that the algorithm mentioned in this paper can extract the respiratory
signal from ECG. By comparing with respiratory data in the database simultaneously recorded, it confirmed the validity of the algorithm.

In order to minimize the huge economic losses to people’s living and the country caused by major
natural disasters and to establish an effective disaster response transportation system，we proposed modern
intelligent algorithms such as clustering model based on K-means and multi-person short-circuit model based on
genetic algorithm，and carried out disaster rescue simulation experiment combining with Puerto Ｒico’s urban
data，which improved the traditional low traffic disaster response system limitation，pertinence and low efficiency
of faults． And for hospitals，road network density，population density and plains the model will provide the first
aid． We use drones to quickly inspect the main traffic lines and quickly restore the traffic． Taking Puerto Ｒico as
an example，the experiment results show that the model quickly realizes the traffic recovery，provides great
convenience for the transportation of ground materials，and improves the rescue speed．

In order to improve the precision of remote sensing image for crop prediction and the efficiency of agricultural planting, combined with the innovation and entrepreneurship training program of Jilin University, an experimental project of crop classification algorithm based on satellite remote sensing image is designed. Taking the high-resolution satellite image of Harbin agricultural demonstration base captured by sentinel-2 on July 30, 2018 as the experimental data, the characteristics of rice, soybean, corn and sorghum in the image are extracted and classified by using the maximum likelihood method, support vector machine method and neural network method in different spectral bands ( including red band) , and then the crop classification map is obtained, the statistical results are compared with the real parameters, and then the classification accuracy and reliability of different algorithms are compared. The experimental results show that the neural network method has the highest accuracy and the strongest reliability, and is suitable for nationwide promotion.

Abstract: To complete the ECG signal feature points extraction and the classification of ST segment, we put forward an algorithm based on the discrete wavelet transform, combined with the f derivative and the SVM(Support Vector Machine). The algorithm can accomplish the signal preprocessing, noise elimination, QRS complex detection and extraction of characteristic value, calculating the average ST segment, curve area and the standard deviation, and the simple classification of ST segment by using the SVM combined with the three sets of data. The matlab simulation results show that the wavelet denoising is effective and has no distortion, and completely extract ST segment feature points. The data are downloaded from the MIT-BIT database, the classification results show that cross-validation average accuracy is 80. 70%,the average accuracy of training is 91. 83%, the average testing accuracy was 74. 28%.

Aiming at the disadvantages of contact measurement density instrument, such as detection range limitation, we put forward a kind of non-contact volume measurement method. This method can be used to calculate the density by detecting the inner variation of container air pressure to indirectly measure the non-hollow solid density. A wide range of the measurement, a notable advantage of this device, can nondestructively measure the non-hollow solid matter which does not have chemical reaction with air. This method can also avoid the damage and the error brought by single drainage destruction. Within the range of pressure endurance, the container can increase the pressure of the compressed air chamber by controlling the air compressors properly, improving the measurement accuracy and making the error less than 3% .

The optimal waveform design based on SINR(Signal to Interference plus Noise Ratio) needs to search for the Lagrange multiplier which leads to a large amount of computation. To solve the problem, a method of maximum energy distribution based on SINR is presented. It distributes the discrete energy reasonably to maximize SINR of the echo. The optimal transmitted waveform of cognitive radar can be obtained with this method.The simulation results show that the achieved optimal cognitive radar waveform based on the SINR maximum energy distribution algorithm is close to the waveform based on the Lagrange multiplier algorithm and this method does not need to search for the multiplier. So the amount of computation can be reduced, the computation rate can be improved.

To understand and dig clinical information of CT images, and to study the method of 3D reconstructionand visualization of CT image, we present an algorithm of 3D reconstruction. A group of 16 row spiral CT images of the abdomen of the human body was imported into Mimics 10.01 using the tools thresholding, mask editing, region growing and reconstruction. Spleen was separated from the whole abdomen CT images and reconstructed. It can be o
bserved from any perspective, zoomed in and out. The experiment results show that by using proposed 3D reconstruction algorithm, the spleen reconstructed is closer to its real 3D structure. It can provide further data in detail for medical research.

in order to solve the problem of clustering center improper selection in the traditional K-means algorithm which leads to the clustering result into local optimum, the initial clustering center of K-means algorithm is searched by the bat algorithm. The simulated annealing and the niche technology based on crowding out is added into the bat algorithm, in order to overcome some problems such as slow-speed convergence in later and weak search capability, its validity is verified by test functions. Finally the initial clustering center of K-means algorithm is optimized by the improved bat algorithm. The improved algorithm is compared to the traditional K-means algorithm, and the experimental results show that the improved algorithm of clustering performance has improved greater than the traditional K-means algorithm.

The fault estimation problem is investigated for a class of stochastic time-varying delayed systems subject to channel fadings. A sequence of variables which are mutually independent but obey the Bernoulli distribution is employed to describe the randomly occurring character of communication delays and the modified L-order Rice fading model is used to better reflect the phenomenon of channel fadings. Lyapunov stability theory, stochastic analysis techniques and LMI(Linear Matrix Inequality) techniques are utilized to derive the sufficient conditions for the designed fault estimator. The estimator gains are obtained by solving the feasible solution of the LMI. An illustrative example is provided to demonstrate the effectiveness of the proposed scheme.

In order to increase the accuracy of gradient correction of magnetic measurement and to improve the magnetometer's function, the mathematical feature for the IGRF（International Geomagnetic Reference Field） is described, the complete formula suitable for programming and a program based on Visual C++
is also given. The program can calculate the geomagnetic elements of any position in the world based on the IGRF11 model. Compared with the standard results, the total intensitys mean error of the program is 1.2 nT, which can meet the requirements of the gradient correction. The study also provides a theoretical basis for the development of automatic resonance and PC software.

 In order to aquire accurate and robust positioning results, a low cost monocular camera and an IMU(Inertial Measurement Unit) are combined by sensor fusion for the ego-positioning of MAVs (Micro Air Vehicles). In the visual positioning module, three of the most representative state-of-the-art monocular visual positioning approaches, which are originally designed for vehicles, are evaluated in different conditions. Based on the experiment results, the pros and cons of each method are analyzed and discussed. Considering the limitations of vision-2only approaches, an EKF (Extended Kalman Filter) is used to fuse the visual positioning with an inertial sensor based on a loosely-coupled framework, to compensate for the instability of vision. The experiment results demonstrate that the visual-inertial sensor fusion can effectively reduce the positioning errors in unstable visual cases.

To understand and research comparison algorithm of big trajectory data, an overall and systematical summarization of the trajectory comparison is given under the development and background of the big data research. With the problems of the big data application raised, the trajectory data need deeper research on the computation of the big data, the intelligence of the algorithms and the efficiency and the validity of the algorithm, etc.

Aiming at effectively extracting the event information of seismic signal and solving the issues that the Hemite-Gauss steerable filters have an intrinsic limit which the same amount of smoothing is made in all directions due to the isotropic Gaussian factor, the EHGFs(Elongated Hemite-Gauss Functions) are proposed.This method considers that the desired signal is different from random noise in direction characteristic, generate EHGFs steerable filters by the approximate fitting, which can improve the linearity of the signal and filter along the event to get directional filtered data with less smoothing effect. Then we reconstruct the data on the basis of the digital local features to denoise. Experiment results indicate that this method can significantly suppress random noise and keep the useful signal in seismic processing.

To solve the problem of the difficulty in estimating the parameters of Weibull distribution model, a new algorithm based on FPSO(Fuzzy Particle Swarm Optimization) with SA(Simulated Annealing) for estimating the parameters of Weibull distribution model was presented. In this intelligent algorithm, the inertia factor of PSO was turned by adopting fuzzy logic reasoning to improve the convergence rate of the PSO(Particle Swarm Optimization) according to the characteristic of particle's motion trajectory in longitudinal direction and lateral direction. Then combine the FPSO and SA, use the update formulas of FPSO as the state generating function of SA, and use Metropolis algorithm to accept the new state with probability to get the global optimal parameters' estimations. The parameter estimation method based on the intelligent algorithm above mentioned was used to estimate the parameters of Weibull distribution model, and the parameter estimation accuracy was improved. Practical application in the complex distribution model parameter estimation showed that the method was feasible and effective.

In order to solve the problem that the charging electric appliance safety testing, A test normal ac power detection circuit introduced in this paper, Analyzed the principle of the circuit and the circuit simulation test. And then to the test circuit hardware physical structures, and the test results are recorded. Analysis and data processing through the control chip of A/ D module, and then to determine whether a rechargeable electric complete charging process and the judgment result feedback to the user, To achieve the charging electric safety control. The main characteristics of this circuit is of simple structure design, debug, in the actual test has very high reliability and stability.

 To monitor changes of indoor carbon dioxide concentrations in real time, a carbon dioxide alarm based on a gas sensor with a STC89C52 microcontroller as the core is designed. When the CO2 concentration in the air exceeds the preset value, the sound and light alarm function can be activated, and the indoor CO2 concentration value can be displayed in real-time. The hardware system includes a carbon dioxide sensor, signal conditioning circuit, analog-to-digital conversion circuit, STC89C52 microcontroller, and acousto-optic alarm unit. The software system includes data acquisition, data processing, alarm logic, and other functional units. The alarm can be activated in time when indoor CO2 concentration exceeds 1. 5% . 

In order to improve the tracking precision of radar to low altitude targets, we propose a novel real-time bias compensation algorithm based on the fading memory recursive algorithm of least squares estimation. Different devises are used to track the target simultaneously and different tracking results are obtained. The recursive algorithm of least squares estimation is used to give different weight to the different tracking results. The estimation of the angle measurement bias is obtained. By theoretical analysis, the multipath environment model is achieved, and the performance of the proposed algorithm is checked. The results show that estimation error of the proposed algorithm is relatively small. And it can also reduce the vibration of the angle measurements and give stable estimating results.

 Existing performance prediction research focuses on how to build prediction model, ignoring the importance of prediction time. In order to solve this problem, a prediction model based on BP ( Back Propagation) neural network is proposed to find out the potential relationship between freshmen's grades and graduation grades and realize the principle of early guidance and early effect. Through a random prediction experiment on the grades of 2016 students majoring in information and computing science in a university, it is proved that there is a potential relationship between freshman scores and graduation scores. The proposed prediction model has excellent prediction accuracy and good practicability and popularization, which can become an important part of improving teaching quality and play a greater role in realizing the goal of talent training.

We study the concepts of ACA (Action Coverage Area) and AC (Action Core), and design a multiple-viewpoint action model based on the advanced object recogniton method Latent SVM ( Support Vector Machine). Multi-viewpoint ACA can succinctly describe the non-rigid change and appearance variation due to different viewpoints and multi-viewpoint AC, because the auxiliary of ACA can help to improve the efficiency and robustness of action recognition. We created an independent dataset of human action for model training and testing. Experiments showed that our model had high performance in classifying and detecting human action in still images.

o establish kinematics model D-H parameter method is employed and virtual joints is introduced, according to a variety of joint types of complex manipulator. There are many problems of multi-joint manipulator, such as multiple solution, accuracy and real-time performance. The solution of the inverse kinematics is obtained by using four different optimization algorithm: the particle swarm optimization algorithm with LPSO(Linear Decreasing Weight Particle Swarm Optimization), CBPSO (Crossbreed Particle Swarm Optimization) algorithm, SAPSO (Simulated Annealing Particle Swarm Optimization) algorithm and CPSO(Choas Particle Swarm Optimization) algorithm. These algorithms are tested for the validity of the solution of inverse kinematics. Through randomly selected workspace points, the four algorithms are compared according to the executing time, the end-effector position error and so on. The results show that the improved CBPSO algorithm can be effectively used for the inverse solution for complex multi-joint manipulator, and satisfy the requirement of real-time control in actual work.

In recent years, benefiting from the mature application of communication, big data, cloud computing and other technologies, “ Internet + ” has been widely popularized in people’s livelihood, economy and government affairs. With the rapid increase of equipment and explosive growth of data, the network environment becomes increasingly complex and brings huge hidden dangers to network security. The need for the development of network security technology and how to apply artificial intelligence AI(Artificial Intelligence) to help solve some problems is introduced. And how to apply machine learning ML(Machine Learning) to improve network security performance is analyzed in detail for specific domain or specific network technology. First, we summarize existing research work on using AI to combat cyber attacks, including using traditional machine learning approaches and existing deep learning solutions. We then analyzed the counterattacks that the AI itself might be subjected to, dissected their characteristics, and classified the appropriate defenses. We also provide some advanced concepts of artificial intelligence network security technology i. e. how to better apply artificial intelligence to the field of network security in the future.