Intensity-based image registration using multiple distributed agents

Image registration is the process of geometrically aligning images taken from different sensors, viewpoints or instances in time. It plays a key role in the detection of defects or anomalies for automated visual inspection. A multiagent distributed blackboard system has been developed for intensity-based image registration. The images are divided into segments and allocated to agents on separate processors, allowing parallel computation of a similarity metric that measures the degree of likeness between reference and sensed images after the application of a transform. The need for a dedicated control module is removed by coordination of agents via the blackboard. Tests show that additional agents increase speed, provided the communication capacity of the blackboard is not saturated. The success of the approach in achieving registration, despite significant misalignment of the original images, is demonstrated in the detection of manufacturing defects on screen-printed plastic bottles and printed circuit boards

Formation of misfit dislocations in strained-layer GaAs/In_xGa_1–xAs/GaAs heterostructures during postfabrication thermal processing

It is demonstrated that relaxation of GaAs/InxGa1–xAs/GaAs strained-layer heterostructures can be brought about by postfabrication thermal processing. Misfit dislocations are introduced into the structure during thermal processing, even though the thickness of the strained layer is well below the critical value predicted by the Matthews–Blakeslee model. The misfit dislocations are observed to be of both 60° mixed type and 90° pure edge type. As no relaxation occurs at the lower temperatures encountered during fabrication by molecular-beam epitaxy, it can be inferred that the critical condition for the formation of misfit dislocations is not only a function of strained-layer thickness and composition, but also of temperature. This observation cannot be accounted for by differential thermal expansion or diffusion across the strained-layer interfaces, but the temperature-dependent Peierls force may offer an explanation. The high temperature required to produce relaxation of these structures suggests that they are sufficiently thermally stable for most practical applications

Automating Electronic Health Record Data Quality Assessment

Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown