This paper presents a power-and area-efficient front-end ASIC that is directly integrated with an array of 32 × 32 piezoelectric transducer elements to enable next-generation miniature ultrasound probes for real-time 3-D transesophageal echocardiography. The 6.1 × 6.1 mm 2 ASIC, implemented in a low-voltage 0.18 m CMOS process, effectively reduces the number of cables required in the probe's narrow shaft by means of 96 delay-and-sum beamformers, each of which locally combines the signals received by a sub-array of 3 × 3 elements. These beamformers are based on pipeline-operated analog sample-and-hold stages, and employ a mismatch-scrambling technique to prevent the ripple signal associated with mismatch between these stages from limiting the dynamic range. In addition, an ultra-low-power LNA architecture is proposed to increase the power-efficiency of the receive circuitry. The ASIC has a compact, element-matched layout, and consumes less than 230 mW while receiving. Its functionality has been successfully demonstrated in 3-D imaging experiments.

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Abstract. Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.