目的显影室间孔区域静脉及属支,建立三维图像,构建该区静脉网络,探讨室间孔与周围静脉的空间位置关系。方法筛选60名健康志愿者行3.0 T MR头部扫描,利用最小密度投影(mIP)和交互式医学图像控制系统(Mimics)对原始图像进行后处理,构建室...目的显影室间孔区域静脉及属支,建立三维图像,构建该区静脉网络,探讨室间孔与周围静脉的空间位置关系。方法筛选60名健康志愿者行3.0 T MR头部扫描,利用最小密度投影(mIP)和交互式医学图像控制系统(Mimics)对原始图像进行后处理,构建室间孔周围静脉网络,对室间孔及周围静脉的解剖学形态进行观察分析。结果室间孔显示率为65%(78侧);大脑内静脉(2.13±0.30)mm,100%(120侧);透明隔前静脉(0.69±0.19)mm,100%(120侧);丘纹上静脉(1.47±0.38)mm,98.3%(118侧);脉络膜上静脉(0.40±0.18)mm,82.5%(99侧)。根据大脑内静脉属支汇入点与室间孔位置关系分为:ⅠA型,24.2%(29侧),即透明隔前静脉汇入大脑内静脉点位于静脉角且紧邻室间孔的后缘;ⅠB型,13.3%(16侧),即透明隔前静脉汇入大脑内静脉点远离静脉角且远离室间孔的后缘;ⅡA型,45%(54侧),即透明隔前静脉汇入大脑内静脉点位于假静脉角且远离室间孔;ⅡB型,15.8%(19侧),即透明隔前静脉汇入大脑内静脉点远离假静脉角和室间孔;Ⅲ型,1.7%(2侧),即丘纹上静脉缺如型。结论磁敏感加权成像(SWI)技术能清晰成像室间孔及其周围静脉,结合Mimics技术可构建大脑内静脉及其属支、室间孔与主要静脉汇合点三维空间位置数据。大脑内静脉属支汇入点与室间孔位置关系分型对室间孔区手术入路选择有重大意义。展开更多
The article presents multi-pattern formation exchange of mobile robots according to the image signals, programs motion paths using A＊ searching algorithm, and avoids the collision points of motion paths. The system c...The article presents multi-pattern formation exchange of mobile robots according to the image signals, programs motion paths using A＊ searching algorithm, and avoids the collision points of motion paths. The system contains an image system, a grid based motion platform, some wireless Radio Frequency （RF） modules and five mobile robots. We use image recognition algorithm to classify variety pattern formation according to variety Quick Response （QR） code symbols on the user interface of the supervised computer. The supervised computer controls five mobile robots to execute formation exchange and presents the movement scenario on the grid based motion platform. We have been developed some pattern formations according to game applications, such as long snake pattern formation, phalanx pattern formation, crane wing pattern formation, sword pattern formation, cone pattern formation, sward pattern tbrmation, T pattern formation, rectangle pattern formation and so on. We develop the user interface of the multi-robot system to program motion paths for variety pattern formation exchange according to the minimum displacement. In the experimental results, the supervised computer recognizes the various QR-code symbols using image system and decides which pattern formation to be selected on real-time. Mobile robots can receive the pattern formation command from the supervised computer, present the movement scenario from the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots on real-time.展开更多
An intelligent vehicle control system is designed and embedded in a Digital Signal Processing （DSP） platform （eZdspTM F2812）. A golf cart is used as an installation platform for the overall system, including steer...An intelligent vehicle control system is designed and embedded in a Digital Signal Processing （DSP） platform （eZdspTM F2812）. A golf cart is used as an installation platform for the overall system, including steering wheel Alternating Current （AC） serve motor, brake actuator, throttle driving circuit and sensors. Digital image processing technology is also used to enable the autonomous driving system to achieve multi-mode lane-keeping, lane-change and obstacle-avoidance. The overall system is tested and evaluated on a university campus.展开更多
文摘目的显影室间孔区域静脉及属支,建立三维图像,构建该区静脉网络,探讨室间孔与周围静脉的空间位置关系。方法筛选60名健康志愿者行3.0 T MR头部扫描,利用最小密度投影(mIP)和交互式医学图像控制系统(Mimics)对原始图像进行后处理,构建室间孔周围静脉网络,对室间孔及周围静脉的解剖学形态进行观察分析。结果室间孔显示率为65%(78侧);大脑内静脉(2.13±0.30)mm,100%(120侧);透明隔前静脉(0.69±0.19)mm,100%(120侧);丘纹上静脉(1.47±0.38)mm,98.3%(118侧);脉络膜上静脉(0.40±0.18)mm,82.5%(99侧)。根据大脑内静脉属支汇入点与室间孔位置关系分为:ⅠA型,24.2%(29侧),即透明隔前静脉汇入大脑内静脉点位于静脉角且紧邻室间孔的后缘;ⅠB型,13.3%(16侧),即透明隔前静脉汇入大脑内静脉点远离静脉角且远离室间孔的后缘;ⅡA型,45%(54侧),即透明隔前静脉汇入大脑内静脉点位于假静脉角且远离室间孔;ⅡB型,15.8%(19侧),即透明隔前静脉汇入大脑内静脉点远离假静脉角和室间孔;Ⅲ型,1.7%(2侧),即丘纹上静脉缺如型。结论磁敏感加权成像(SWI)技术能清晰成像室间孔及其周围静脉,结合Mimics技术可构建大脑内静脉及其属支、室间孔与主要静脉汇合点三维空间位置数据。大脑内静脉属支汇入点与室间孔位置关系分型对室间孔区手术入路选择有重大意义。
文摘The article presents multi-pattern formation exchange of mobile robots according to the image signals, programs motion paths using A＊ searching algorithm, and avoids the collision points of motion paths. The system contains an image system, a grid based motion platform, some wireless Radio Frequency （RF） modules and five mobile robots. We use image recognition algorithm to classify variety pattern formation according to variety Quick Response （QR） code symbols on the user interface of the supervised computer. The supervised computer controls five mobile robots to execute formation exchange and presents the movement scenario on the grid based motion platform. We have been developed some pattern formations according to game applications, such as long snake pattern formation, phalanx pattern formation, crane wing pattern formation, sword pattern formation, cone pattern formation, sward pattern tbrmation, T pattern formation, rectangle pattern formation and so on. We develop the user interface of the multi-robot system to program motion paths for variety pattern formation exchange according to the minimum displacement. In the experimental results, the supervised computer recognizes the various QR-code symbols using image system and decides which pattern formation to be selected on real-time. Mobile robots can receive the pattern formation command from the supervised computer, present the movement scenario from the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots on real-time.
文摘An intelligent vehicle control system is designed and embedded in a Digital Signal Processing （DSP） platform （eZdspTM F2812）. A golf cart is used as an installation platform for the overall system, including steering wheel Alternating Current （AC） serve motor, brake actuator, throttle driving circuit and sensors. Digital image processing technology is also used to enable the autonomous driving system to achieve multi-mode lane-keeping, lane-change and obstacle-avoidance. The overall system is tested and evaluated on a university campus.
