The recently introduced real-time three-dimensional color Doppler flow imaging （RT-3D CDFI） technique provides a quick and accurate calculation of regurgitant jet volume （RJV） and fraction. In order to evaluate RT...The recently introduced real-time three-dimensional color Doppler flow imaging （RT-3D CDFI） technique provides a quick and accurate calculation of regurgitant jet volume （RJV） and fraction. In order to evaluate RT-3D CDFI in the noninvasive assessment of aortic RJV and regurgitant jet fraction （RJF） in patients with isolated aortic regurgitation, real-time three-dimensional echocardiographic studies were performed on 23 patients with isolated aortic regurgitation to obtain LV end-diastolic volumes （LVEDV）, end-systolic volumes （LVESV） and RJV, and then RJF could be calculated. The regurgitant volume （RV） and regurgitant fraction （RF） calculated by two-dimensional pulsed Doppler （2D-PD） method served as reference values. The results showed that aortic RJV measured by the RT-3D CDFI method showed a good correlation with the 2D-PD measurements （r= 0.93, Y=0.89X＋ 3.9, SEE= 8.6 mL, P〈0.001 ）; the mean （SD） difference between the two methods was - 1.5 （9.8） mL. % RJF estimated by the RT-3D CDFI method was also correlated well with the values obtained by the 2D-PD method （r=0.88, Y=0.71X＋ 14.8, SEE= 6.4 %, P〈0. 001）; the mean （SD） difference between the two methods was -1.2 （7.9） %. It was suggested that the newly developed RT-3D CDFI technique was feasible in the majority of patients. In patients with eccentric aortic regurgitation, this new modality provides additional information to that obtained from the two-dimensional examination, which overcomes the inherent limitations of two-dimensional echocardiography by depicting the full extent of the jet trajectory. In addition, the RT-3D CDFI method is quick and accurate in calculating RJV and RJF.展开更多
Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource effic...Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.展开更多
文摘The recently introduced real-time three-dimensional color Doppler flow imaging （RT-3D CDFI） technique provides a quick and accurate calculation of regurgitant jet volume （RJV） and fraction. In order to evaluate RT-3D CDFI in the noninvasive assessment of aortic RJV and regurgitant jet fraction （RJF） in patients with isolated aortic regurgitation, real-time three-dimensional echocardiographic studies were performed on 23 patients with isolated aortic regurgitation to obtain LV end-diastolic volumes （LVEDV）, end-systolic volumes （LVESV） and RJV, and then RJF could be calculated. The regurgitant volume （RV） and regurgitant fraction （RF） calculated by two-dimensional pulsed Doppler （2D-PD） method served as reference values. The results showed that aortic RJV measured by the RT-3D CDFI method showed a good correlation with the 2D-PD measurements （r= 0.93, Y=0.89X＋ 3.9, SEE= 8.6 mL, P〈0.001 ）; the mean （SD） difference between the two methods was - 1.5 （9.8） mL. % RJF estimated by the RT-3D CDFI method was also correlated well with the values obtained by the 2D-PD method （r=0.88, Y=0.71X＋ 14.8, SEE= 6.4 %, P〈0. 001）; the mean （SD） difference between the two methods was -1.2 （7.9） %. It was suggested that the newly developed RT-3D CDFI technique was feasible in the majority of patients. In patients with eccentric aortic regurgitation, this new modality provides additional information to that obtained from the two-dimensional examination, which overcomes the inherent limitations of two-dimensional echocardiography by depicting the full extent of the jet trajectory. In addition, the RT-3D CDFI method is quick and accurate in calculating RJV and RJF.
文摘Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.
