A CT-based technique to predict optimal projection for self-expanding TAVI in patients with different aortic valve anatomies

Background Optimal projection is essential for valve deployment during transcatheter aortic valve implantation (TAVI). The purpose of this study was to propose an approach to predict optimal projection in TAVI candidates with different aortic valve anatomies. Methods 331 patients undergoing self-expanding TAVI were included and the so-called non-coronary cusp (NCC)-parallel technique was utilized, which generated the predicted projection by connecting NCC commissures on the transverse plane on the pre-procedural computed tomography images. Results 37.8% of the study cohort were bicuspid aortic valve (BAV) patients. Around 80% of both NCC-parallel views and final views were in the right anterior oblique (RAO) and caudal (CAU) quadrant. There was less than 5° change required from the NCC-parallel view to the final implanted view in 79% of tricuspid aortic valve (TAV) patients but only in 27% (13/48) of type 0 BAV patients with coronary arteries originated from the different cusps. After excluding the above mentioned BAV patients, 62.3% (48/77) of BAV patients needed less than 5° change to achieve optimal projection and only in 8 patients, the angular change was larger than 10° in either left/right anterior oblique or cranial/caudal direction. Conclusions The NCC-parallel technique provides reliable prediction for optimal projection in self-expanding TAVI in all TAV and most BAV patients, with a vast majority of views in the RAO and CAU quadrant.

A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

Timely detection and treatment of possible incipient faults in satellites will effectively reduce the damage and harm they could cause. Although much work has been done concerning fault detection problems, the related questions about satellite incipient faults are little addressed. In this paper, a new satellite incipient fault detection method was proposed by combining the ideas of deviation in unsupervised fault detection methods and classification in supervised fault detection methods. First, the proposed method uses dynamic linear discriminant analysis (LDA) to find an optimal projection vector that separates the in-orbit data from the normal historical data as much as possible. Second, under the assumption that the parameters obey a multidimensional Gaussian distribution, it applies the normal historical data and the optimal projection vector to build a normal model. Finally, it employs the noncentral F-distribution to test whether a fault has occurred. The proposed method was validated using a numerical simulation case and a real satellite fault case. The results show that the method proposed in this paper is more effective at detecting incipient faults than traditional methods.

Optimal Projection Pattern for Active Visual Servoing (AVS)

<p>Visual servo control uses images that are obtained by a camera for robotic control. This study focuses on the problem of positioning a target object using a robotic manipulator with image-based visual servo (IBVS) control. To perform the positioning task, the image-based visual servoing requires visual features that can be extracted from the appearance of the target object. Therefore, a positioning error tends to increase especially for textureless objects, such as industrial parts. Since it is difficult to extract differences of the visual features between current and goal images. To solve these problems, this paper presents a novel visual servoing named ``Active Visual Servoing." Active Visual Servoing (AVS) projects patterned light onto the target object using a projector. The design of the projection pattern affects the positioning error. AVS uses an optimal pattern which is theoretically derived and maximizes differences between current and goal images. The experimental results show that the proposed active visual servoing method reduces the positioning error by more than 97% compared to conventional image-based visual servoing.</p>

Optimal Projection Pattern for Active Visual Servoing (AVS)

<p>Visual servo control uses images that are obtained by a camera for robotic control. This study focuses on the problem of positioning a target object using a robotic manipulator with image-based visual servo (IBVS) control. To perform the positioning task, the image-based visual servoing requires visual features that can be extracted from the appearance of the target object. Therefore, a positioning error tends to increase especially for textureless objects, such as industrial parts. Since it is difficult to extract differences of the visual features between current and goal images. To solve these problems, this paper presents a novel visual servoing named ``Active Visual Servoing." Active Visual Servoing (AVS) projects patterned light onto the target object using a projector. The design of the projection pattern affects the positioning error. AVS uses an optimal pattern which is theoretically derived and maximizes differences between current and goal images. The experimental results show that the proposed active visual servoing method reduces the positioning error by more than 97% compared to conventional image-based visual servoing.</p>

Adaptive Fringe Projection for 3D Shape Measurement with Large Reflectivity Variations by Using Image Fusion and Predicted Search

There is always a great challenge for the structured light technique that it is difficult to deal with the surface with large reflectivity variations or specular reflection. This paper proposes a flexible and adaptive digital fringe projection method based on image fusion and interpolated prediction search algorithm. The multiple mask images are fused to obtain the required saturation threshold, and the interpolated prediction search algorithm is used to calculate the optimal projection gray-level intensity. Then, the projection intensity is reduced to achieve coordinate matching in the unsaturated condition, and the adaptive digital fringes with the optimal projection intensity are subsequently projected for phase calculation by using the heterodyne multifrequency phase-shifted method. The experiments demonstrate that the proposed method is effective for measuring the high-reflective surface and unwrapping the phase in the local overexposure region completely. Compared with the traditional structured light measurement methods, our method can decrease the number of projected and captured images with higher modulation and better contrast. In addition, the measurement process only needs two prior steps and avoids hardware complexity, which is more convenient to apply to the industry.