Wireless sensor networks (WSNs) are attracting increasing research interest due to their ability to monitor large areas independently. Their reliability is a crucial issue, as it is influenced by hardware, data, and energy-related factors such as loading conditions, signal attenuation, and battery lifetime. Proper selection of sensor node positions is essential to maximise system reliability during the development of products equipped with WSNs. For this purpose, this paper presents an approach to estimate WSN system reliability during the development phase based on the analysis of measurements, using strain measurements in finite element (FE) models as an example. The approach involves dividing the part under consideration into regions with similar strains using a region growing algorithm (RGA). The WSN configuration is then analysed for reliability based on data paths and measurement redundancy resulting from the sensor positions in the identified measuring regions. This methodology was tested on an exemplary WSN configuration at an aircraft wing box under bending load and found to effectively estimate the hardware perspective on system reliability. Therefore, the methodology and algorithm show potential for optimising sensor node positions to achieve better reliability results.

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.

Recently, lung cancer has been paid more and more attention. People have reached a consensus that early detection and early treatment can improve the survival rate of patients. Among them, pulmonary nodules are the important reference for doctors to determine the lung health. With the continuous improvement of CT image resolution, more suspected pulmonary nodule information appears from the impact of chest CT. How to relatively and accurately locate the suspected nodule location from a large number of CT images has brought challenges to the doctor\'s daily diagnosis. To solve the problem that the original DBSCAN clustering algorithm needs manual setting of the threshold, this paper proposes a region growing algorithm and an adaptive DBSCAN clustering algorithm to improve the accuracy of pulmonary nodule detection. The image is roughly processed and ROI (Regions of Interest) region is roughly extracted by CLAHE transform. The region growing algorithm is used to roughly process the adjacent region\'s expansibility and the suspected region in ROI, and mark the center point in the region and the boundary point of its point set. The mean value of region range is taken as the threshold value of DBSCAN clustering algorithm. The center of the point domain is used as the starting point of clustering, and the rough set of points is used as the MinPts threshold. Finally, the clustering results are labeled in the initial CT image. Experiments show that the pulmonary nodule detection method proposed in this paper effectively improves the accuracy of the detection results.

In this study, we aimed to investigate the feasibility of using Materialise\'s interactive medical image control system (MIMICS) to measure urinary calculi volume. We used a cylinder measuring to measure the same polymer clay volume in different groups. Polymer clay was made into an oval shape, an antler type, and a multiple irregular shapes by hand. They are divided into three groups, that is, A, B, and C, each of which has seven polymer clays. The computer tomography (CT) 3D images of each sample were obtained by 256iCT scanning. The CT 3D image was imported into MIMICS to measure the theoretical volume and average CT value of polymer clay. The differences between the volume and CT values measured by MIMICS and 256iCT were evaluated. The volume of each polymer clay that was measured by a measuring cylinder was 34.7 ml. The average CT values of groups A, B, and C measured by 256iCT were 1121.3 ± 35.8, 1071.3 ± 22.2, and 1083.9 ± 6.3 Hu, respectively. The theoretical volume and CT values of the ceramics measured by MIMICS were as follows: the averaged volume of group A was 35.1 ± 0.4 ml, and the average CT value was 1065.7 ± 5.3 Hu. The average volume of group B was 34.5 ± 0.2 ml, and the average CT value was 1008.9 ± 7.7 Hu. The average volume of group C was 34.4 ± 0.5 ml, and the average CT value was 980.9 ± 6.1 Hu. MIMICS was reliable in measuring urinary stone volume. The difference between the CT values measured by MIMICS and 256iCT was statistically significant. MIMICS had a slightly lower CT value than that of 256iCT. However, from the data point of view, the difference between the two methods was small.

To compare the accuracy and reliability of stone volume estimated by ellipsoid formula (EFv) and CT-based algorithm (CTv) to true volume (TV) by water displacement in an in vitro model.
Ninety stone phantoms were created using clay (0.5-40 cm3, 814 HU ±91) and scanned with CT. For each stone, TV was measured by water displacement, CTv was calculated by the region-growing algorithm in the CT-based software AGFA IMPAX Volume Viewer, and EFv was calculated by the standard formula π × L × W × H × 0.167. All measurements were repeated thrice, and concordance correlation coefficient (CCC) was calculated for the whole group, as well as subgroups based on volume (<1.5 cm3, 1.5-6 cm3, and >6 cm3).
Mean TV, CTv, and EFv were 6.42 cm3 ± 6.57 (range: 0.5-39.37 cm3), 6.24 cm3 ± 6.15 (0.48-36.1 cm3), and 8.98 cm3 ± 9.96 (0.49-47.05 cm3), respectively. When comparing TV to CTv, CCC was 0.99 (95% confidence interval [CI]: 0.99-0.995), indicating excellent agreement, although TV was slightly underestimated at larger volumes. When comparing TV to EFv, CCC was 0.82 (95% CI: 0.78-0.86), indicating poor agreement. EFv tended to overestimate the TV, especially as stone volume increased beyond 1.5 cm3, and there was a significant spread between trials.
An automated CT-based algorithm more accurately and reliably estimates stone volume than does the ellipsoid formula. While further research is necessary to validate stone volume as a surrogate for stone burden, CT-based algorithmic volume measurement of urinary stones is a promising technology.