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With the ubiquity of high-speed networks and cloud technologies, hyper-convergence (HC) has become commonplace and satisfies flex allocation requirements. This study compared the VMware virtual storage area network (vSAN), which has the largest market share, with Cisco HyperFlex, released by Cisco for high-level network applications, to assess the performance of various HC technologies in running virtual machines in most virtualized environments. The experiments consider the benchmark (HCIBench) to evaluate the platform’s performance. The benchmark provides objective performance scores. Thus, the performance results derived by HCIBench could be applied to discuss the appropriate scenarios for both VMware vSAN and Cisco HyperFlex. The experiments simulate common application scenarios to discover good HC platforms for real-world requirements. The VMware vSAN demonstrated more robust overall performance (about 38.18% and 22.72% improvement for IOPS and transmission speed, respectively), the HyperFlex Data Platform provided better performance wrote latency (about 20.32% improvement) when few virtual machines were used, and the write load was heavy. The difference in CPU usage between the two platforms is not too much (about 2.565%). The vSAN is recommended for general purposes, while HyperFlex performs better in areas with high write requirements (such as the data center).

During clinical consultations and case training, doctors analyze numerous images and sounds. A high-pressure consultation environment can increase the probability of a doctor making incorrect inferences regarding vocal cord (VC) disease. Therefore, this study applied deep learning to design an edge-based VC disease detection system (EVC-DD) for common VC conditions (e.g., nodules, polyps, and cancer) to assist doctors in conducting consultations and case studies and in verifying the consistency of their disease inferences. Through deep learning, the model extracted and recorded clinically confirmed information in its disease inference model. The experiment data set comprised videos of nodules, polyps, and cancer that were used to evaluate the performance of the proposed model. From 13 cases confirmed by two doctors, 1740 images were extracted from 13 case videos and used in the experiment. In total, 1044 (60%), 348 (20%), and 348 (20%) images were randomly obtained through five-fold cross-validation for training, validation, and testing, respectively. During the model training process, the EVC-DD model achieved 100% accuracy in detecting the three conditions required for optimal experiment results. For the results in the analysis of the independent test data with optimized configuration. the EVC-DD model achieved 99.42%, 99.42%, 99.42%, 99.42%, 98.91%, and 0.9957 for averaged F1 score, averaged recall rate, averaged precision, accuracy, Matthews correlation coefficient, and area under the curve, respectively. The EVC-DD model required only 400 s to complete its training using 1740 images. The results indicate that the inferences of the EVC-DD model were highly consistent with the results of the clinical examination by doctors and that its training was data- and time-efficient, thereby allowing the model to learn new cases quickly. Thus, the EVC-DD model can assist doctors in consultations and case analyses by providing reliable disease inferences and real-time input regarding new case knowledge.

The internet of things provides a convenient life for all of us. Physical devices with locational systems connected to the internet can provide navigation and recommendation services in cooperation with the Location Based Services (LBS). The nearest neighbor query is the most common query type in the LBS research area. On the other hand, due to privacy protection or inaccuracies in measurement instructions, data uncertainties are typical in the IoT environment. This uncertainty will affect the results of nearest neighbor queries. The existence of plane obstacles also brings the challenge to the spatial data query. In this paper, the obstacles and data uncertainties in continuous nearest query are studied. We also designed a pruning strategy that significantly reduced the number of objects to be calculated. In addition, we propose a concept of safe region and an algorithm to generate a safe region. We also designed an index method for saving safe regions. The experimental results show that our approach outperforms the state-of-the-art one in efficiency and scalability.

Unlike graded data of common semiconductor test results storing in relational databases, log data in the standard test data format (STDF) contain millions of test data entries. In a semiconductor packaging and testing factory, a semiconductor wafer or integrated circuit tests generate thousands of STDF files each day; therefore, how to store these massive databases is a crucial topic. Different products correspond to different test items and STDF content; if a relational database is used to store all forms of data, the practical operation becomes challenging. This paper used a NoSQL document-oriented database collocated with a Docker container to build a system, named the scalable STDF data (SSD) framework, for storing semiconductor test data. According to semiconductor test operations, the SSD framework first converts STDF files into an open standard format for data transmission and subsequently transfers them to the database. The use of NoSQL databases allows for flexibility of specifications of STDF content, and a Docker container exhibits features such as rapid deployment and high scalability. The SSD framework meets the requirements of semiconductor testing for throughput, latency, and parallel experimental projects; possesses excellent execution efficiency; and provides flexible data storage services in a semiconductor testing environment where processing a large quantity of data is required. From our simulation results, the major performance of the proposed system depends on the hardware properties. The higher hardware distribution degree provides better performance. Docker container provides more connections and the scalability of storage, but higher software distribution contributes limited performance enhancement.