Inflammation of the long head of the biceps tendon is a common cause of shoulder pain. Bicipital peritendinous effusion (BPE) is the most common biceps tendon abnormality and is related to various shoulder injuries. Physicians usually use ultrasound imaging to grade the inflammation severity of the long head of the biceps tendon. However, obtaining a clear and accurate ultrasound image is difficult for inexperienced attending physicians. To reduce physicians' workload and avoid errors, an automated BPE recognition system was developed in this article for classifying inflammation into the following categories-normal and mild, moderate, and severe. An ultrasound image serves as the input in the proposed system; the system determines whether the ultrasound image contains biceps. If the image depicts biceps, then the system predicts BPE severity. In this study, two crucial methods were used for solving problems associated with computer-aided detection. First, the faster regions with convolutional neural network (faster R-CNN) used to extract the region of interest (ROI) area identification to evaluate the influence of dataset scale and spatial image context on performance. Second, various CNN architectures were evaluated and explored. Model performance was analyzed by using various network configurations, parameters, and training sample sizes. The proposed system was used for three-class BPE classification and achieved 75% accuracy. The results obtained for the proposed system were determined to be comparable to those of other related state-of-the-art methods.

EcoExec is a host-assisted interactive execution environment for wireless sensing systems. Users can interact with sensor nodes by viewing attributes and invoking functions via a command-line interface. Functions that are not resident in the node's firmware are automatically compiled on the host, packaged up and downloaded to the node, linked, and executed, all seamlessly and transparently to the user. By packaging these features in a dynamically object-oriented programming environment such as Python, EcoExec enables programmers to experiment with features of the wireless sensor nodes and to rapidly develop application software. Most importantly, EcoExec empowers resource-constrained wireless sensor platforms with rich functionalities that would otherwise be prohibitive, thanks to its host-assisted execution feature with code swapping over the network. Experimental results on actual wireless sensor platforms show EcoExec to perform effectively with negligible observed overhead to the user.