To simplify the medical ultrasound system and reduce the cost, several techniques have been proposed to reduce the interconnections between the ultrasound probe and the back-end console. Among them, subaperture processing (SAP) is the most straightforward approach and is widely used in commercial products. This paper reviews the most important error sources of SAP, such as static focusing, delay quantization, linear delay profile, and coarse apodization, and the impacts introduced by these errors are shown. We propose to use main lobe coherence loss as a simple classification of the quality of the beam profile for a given design. This figure-ofmerit (FoM) is evaluated by simulations with a 1-D ultrasound subaperture array setup. The analytical expressions and the coherence loss can work as a quick guideline in subaperture design by equalizing the merit degradations from different error sources, as well as minimizing the average or maximum loss over ranges. For the evaluated 1-D array example, a good balance between errors and cost was achieved using a subaperture size of 5 elements, focus at 40 mm range, and a delay quantization step corresponding to a phase of π/4.

The casting process involves pouring molten metal into a mold cavity. Currently, traditional object detection algorithms exhibit a low accuracy and are rarely used. An object detection model based on deep learning requires a large amount of memory and poses challenges in the deployment and resource allocation for resource limited pouring robots. To address the accurate identification and localization of pouring holes with limited resources, this paper designs a lightweight pouring robot hole detection algorithm named LPO-YOLOv5s, based on YOLOv5s. First, the MobileNetv3 network is introduced as a feature extraction network, to reduce model complexity and the number of parameters. Second, a depthwise separable information fusion module (DSIFM) is designed, and a lightweight operator called CARAFE is employed for feature upsampling, to enhance the feature extraction capability of the network. Finally, a dynamic head (DyHead) is adopted during the network prediction stage, to improve the detection performance. Extensive experiments were conducted on a pouring hole dataset, to evaluate the proposed method. Compared to YOLOv5s, our LPO-YOLOv5s algorithm reduces the parameter size by 45% and decreases computational costs by 55%, while sacrificing only 0.1% of mean average precision (mAP). The model size is only 7.74 MB, fulfilling the deployment requirements for pouring robots.

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