Analysis of the Effect of Robots in the Treatment of Pancreatic Cancer Based on Smart Medicine

In order to study the effect of robots in the treatment of pancreatic cancer in the context of smart medical, this paper improves the robot recognition technology and data processing technology and improves the system kernel algorithm through the hash algorithm. Unlike the traditional sequencing method that directly uses the gray average value as a feature, the hash algorithm calculates the gray three-average value of each frame block and uses the difference of the three-average value of adjacent frame blocks to perform detection. Moreover, this paper proposes a detection and localization scheme based on hash local matching, which consists of two parts: coarse matching and fine matching. In addition, this paper designs a control experiment to analyze the effect of robots in the treatment of pancreatic cancer, counts multiple sets of data, and uses mathematical statistics to process and visually display the experimental data. The research shows that the robot has a good clinical effect in the treatment of pancreatic cancer.

Correlation Filters Based on Strong Spatio-Temporal for Robust RGB-T Tracking

In this paper, we propose a strong spatio-temporal mechanism with correlation filters to solve multi-modality tracking tasks. First, we use the features of the previous four frames as spatio-temporal features, then aggregate the spatio-temporal features into the filters learning and positioning of the adjacent frame. Second, we enhance the temporal and spatial characteristics of the current frame filter by learning the previous four frame filters and spatial penalty. From the experimental results on the GTOT, VOT-TIR2019 and RGBT234 datasets, our strong spatio-temporal correlation filters has achieved excellent performance.

Real-time location estimation for indoor navigation using a visual-inertial sensor

Purpose The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data. Design/methodology/approach First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia. Findings In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310. Originality/value This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

Pounding between Adjacent Frame Structures under Earthquake Excitation Based on Transfer Matrix Method of Multibody Systems

In this paper, the case of two adjacent frame structures is studied by establishing a mechanical model based on the transfer matrix method of multibody system (MS-TMM). The transfer matrices of the related elements and total transfer equation are deduced, combining with the Hertz-damp mode. The pounding process of two adjacent frame structures is calculated by compiling the relevant MATLAB program during severe ground motions. The results of the study indicate that the maximum error of the peak pounding forces and the peak displacements at the top of the frame structure obtained by the MS-TMM and ANSYS are 6.22% and 9.86%, respectively. Comparing the calculation time by ANSYS and MS-TMM, it shows that the computation efficiency increases obviously by using the MS-TMM. The pounding mainly occurs at the top of the short structure; meanwhile, multiple pounding at the same time may occur when the separation gap is small. The parametric investigation has led to the conclusion that the pounding force, the number of poundings, the moment of pounding, and the structural displacement are sensitive to the change of the seismic peak acceleration and the separation gap size.

Design on Intelligent Video Surveillance System Based on Target Identification Algorithm

This paper makes study on the adjacent frame difference and algorithm realization of SOM i8dentification after improvement, of which it includes motion detection, target identification; the realized video surveillance module makes up the intelligent video surveillance that can reconstruct platform. Motion detection module adopts algorithm of adjacent frame difference after improvement, which can correctly mark the motion object. Target identification module adopts self-mapping nerve net after improvement, it is easier for hardware realization, and meanwhile the accuracy rate of identification is equal to classical algorithm.