Charged particle therapy is a way for cancer therapy that exploits hadron beams, principally protons and carbon ions. A critical issue is the monitoring of the beam range so to test the proper dose deposition to the tumor and surrounding tissues. The design of a new tracking device for beam range actual-time monitoring in pencil beam carbon ion therapy is introduced. The proposed system tracks secondary charged particles produced by beam interactions within the patient tissue and exploits the correlation of the charged particle emission profile with the spatial dose deposition and the Bragg peak position. The detector, currently underneath development, uses the knowledge provided by 12 layers of scintillating fibers followed by a plastic scintillator and a pixelated Lutetium Fine Silicate (LFS) crystal calorimeter. An algorithm to account and correct for emission profile distortion because of charged secondaries absorption inside the affected person tissue is also proposed. Finally detector reconstruction effectivity for charged particle emission profile is evaluated utilizing a Monte Carlo simulation considering a quasi-life like case of a non-homogenous phantom.

Object detection is widely used in robotic navigation, intelligent video surveillance, industrial inspection, aerospace and lots of other fields. It is a vital department of image processing and pc imaginative and prescient disciplines, and is also the core part of clever surveillance methods. At the same time, target detection is also a primary algorithm in the sector of pan-identification, which performs an important position in subsequent tasks resembling face recognition, gait recognition, crowd counting, and instance segmentation. After the first detection module performs goal detection processing on the video frame to obtain the N detection targets in the video body and the first coordinate information of each detection target, the above technique It also contains: displaying the above N detection targets on a display screen. The first coordinate information corresponding to the i-th detection goal; acquiring the above-talked about video frame; positioning in the above-mentioned video body based on the first coordinate info corresponding to the above-talked about i-th detection target, geofencing alert tool obtaining a partial image of the above-mentioned video body, and figuring out the above-mentioned partial image is the i-th picture above.

The expanded first coordinate data corresponding to the i-th detection target; the above-talked about first coordinate info corresponding to the i-th detection goal is used for positioning in the above-talked about video body, together with: based on the expanded first coordinate data corresponding to the i-th detection target The coordinate info locates within the above video body. Performing object detection processing, if the i-th image consists of the i-th detection object, acquiring place data of the i-th detection object within the i-th picture to obtain the second coordinate data. The second detection module performs goal detection processing on the jth image to determine the second coordinate data of the jth detected target, where j is a constructive integer not larger than N and geofencing alert tool never equal to i. Target detection processing, acquiring a number of faces in the above video body, and first coordinate data of every face; randomly obtaining target faces from the above a number of faces, and intercepting partial photographs of the above video body in response to the above first coordinate info ; performing goal detection processing on the partial picture by means of the second detection module to acquire second coordinate information of the target face; displaying the target face in accordance with the second coordinate data.

Display a number of faces in the above video frame on the display. Determine the coordinate checklist in line with the primary coordinate information of each face above. The first coordinate information corresponding to the target face; buying the video body; and positioning in the video body based on the primary coordinate data corresponding to the target face to acquire a partial image of the video body. The extended first coordinate data corresponding to the face; the above-mentioned first coordinate data corresponding to the above-talked about goal face is used for positioning within the above-mentioned video body, including: in response to the above-talked about extended first coordinate info corresponding to the above-mentioned goal face. Within the detection process, if the partial image contains the goal face, acquiring place data of the goal face in the partial picture to obtain the second coordinate info. The second detection module performs target detection processing on the partial picture to find out the second coordinate info of the opposite goal face.