Study on Measurement Information of Bladder Tumor Measurement Based on Ultrasound Imaging Measurement Technology (Preprint)

This work aimed to study the adoption of obturator nerve block (ONB) based on adaptive medical ultrasound imaging under C-means algorithm in transurethral resection of bladder tumor (TURBT). 120 patients with bladder tumors were diagnosed by C-means algorithm-based ultrasound imaging and were enrolled into group A (epidural anesthesia + resection), group B (general anesthesia), and group C (epidural anesthesia + ONB), each with 40 cases. The accuracy of the detection method, noise level, and complications before and after the operation were compared. All patients received TURBT for treatment. There was no significant difference in the general information of patients in each group ( P > 0.05 ). As a result, the correct segmentation rate of the tumor region segmented by ultrasound imaging by C-means algorithm reached 95.6%. The incidence of obturator nerve reflex (ONR) in group A (7.5%) was greatly inferior to groups B and C ( P < 0.05 ). The length of hospital stay in group A was (4.01 ± 1.43) days, which was notably different from groups B and C, with considerable difference among the three ( P < 0.05 ). In short, the adaptive medical ultrasound imaging under C-means algorithm was more accurate in the diagnosis of bladder tumors. Moreover, ONB can effectively reduce the ONR and the incidence of complications in patients.

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Big Data Tracking and Automatic Measurement Technology for Unmanned Aerial Vehicle Trajectory Based on MEMS Sensor

10.21203/rs.3.rs-410175/v1 ◽

2021 ◽

Author(s):

Jun Xing ◽

Xinzhe Wang ◽

Jie Dong

Keyword(s):

Differential Equation ◽

Big Data ◽

Automatic Measurement ◽

Magnetic Data ◽

Discrete Equation ◽

Measurement Technology ◽

Measurement Information ◽

Gradient Descent Method ◽

Mems Sensor ◽

Data Tracking

Abstract Due to the high cost and large error of traditional UAV big data tracking and automatic measurement technology, a method of big data tracking and automatic measurement for UAV trajectory based on MEMS sensor was put forward. The iterative learning control algorithm was used to estimate the repetitive disturbance and modeling error of system based on the simplified dynamics model of four-rotor helicopter and the optimal estimation characteristics of Kalman filter. The discrete equation of quadratic performance function in time domain was selected to compensate the estimated model error disturbance, and then the big data tracking was completed. Based on the data of gyroscope, the quaternion differential equation was established. The differential equation was solved by first-order Picard method, and a set of quaternion data was obtained. The gradient descent method was used to process the acceleration data and magnetic data, and thus to get the optimal quaternion. Finally, the measurement results were obtained by fusing the two quaternions with MEMS sensors. Simulation results prove that the proposed method can obtain the trajectory tracking data and measurement information of UAV accurately.

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