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Tracking organ doses for patient safety in radiation therapy
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Wazir Muhammad, Ying Liang, Gregory R. Hart, Bradley J. Nartowt, David A. Roffman, Jun Deng
Tracking organ doses for all patients and for all the radiation events over the entire course of treatment would produce a large amount of data, which would need to be carefully maintained by a database engine for data storage, update, and query. At the current stage, we choose to use the relational database management system SQLite as a demonstration implementation of the PODA database in our institution. The SQLite is a highly efficient, open-source database engine suitable for small-to-medium sized data (<2 TB). Moreover, it is an embedded database engine, which means the database could be contained in the PODA system itself and not need an independent thread running a database. The advantage of SQLite is that it adds convenience during PODA development and deployment, which compensates for its drawback in concurrency.
Communication systems and network technologies
Published in Kennis Chan, Future Communication Technology and Engineering, 2015
In this paper, handheld a terminal needs to process data information from the tag label and the host computer, so in the end it’s necessary to consider how to store the information in S3C6410. First of all, we need to invoke the function to open the database. Because we have to do a variety of corresponding operations including building, adding, deleting, and updating the database, all the operations are embedded into a click button function. When the system starts up, it is necessary to click the button to read the label data, comparing the data with the one saved in the database, and then to record the device parameters of the corresponding tag label. Finally, the data must be uploaded to the remote server. The embedded database is an essential part of the handheld terminal, and its performance directly affects the terminal’s reliability, stability and efficiency as shown in Figure 8 .
Multimedia Expert Systems
Published in Jay Liebowitz, The Handbook of Applied Expert Systems, 2019
Forms 2d and 2e represent embedded designs that result in a more seamless integration of MM and ES than external calls and loose coupling can accommodate. The essence of these embedded designs, which is important to both developers and users, is that the interface is transparent with control maintained by the dominant system and supported by the other. An example of an embedded system would be one in which either the ES or the MM system contained an embedded database or subprogram that was of only secondary importance to the other.
CityGML goes mobile: application of large 3D CityGML models on smartphones
Published in International Journal of Digital Earth, 2019
Christoph Blut, Timothy Blut, Jörg Blankenbach
The overall outcome was that CityGML is not suited for loading directly using only the limited hardware of smartphones without any pre-selection of objects. To solve this, some specialized (data) architecture is required. As a solution we employ an embedded database with specialized import/export capabilities which enable complex geometric and semantic analysis tasks using SQL. It consists of three parts, the CityGML pull parser to read CityGML files, a SpatiaLite database and the semantic-geometric data selection and export methods (Figure 1). In comparison to, for instance, B3DM the data are not restructured, but rather left in its original form which allows exploiting the semantic relationships.
Experimental application of classification learning to generate simplified model predictive controls for a shared office heating system
Published in Science and Technology for the Built Environment, 2019
Jayson Bursill, William O'Brien, Ian Beausoleil-Morrison
The decision tree developed for the office studied is shown in Figure 7, where is the predicted air temperature (°C) for the next time step using the linear parameter model, is the illuminance (lux), and is the outdoor air temperature (°C). These thresholds were determined by the classification algorithm using the exact method, where all possible classifications are explored and the version with the lowest prediction error is selected (Maji et al. 2008). Other similar forms of decisions that included temperature predictions from the edge of the control horizon (three time steps ahead) were generated but diminishing returns on classification accuracy were found for the required connectivity of weather predictions. Practical commercial building implementation of RE would require a dedicated embedded database object with threshold variables and real-time inputs as shown in the pseudocode of Figure 6. Each threshold corresponds to a value from Figure 7, where the inputs are predicted air temperature one time step ahead (IATp1), celling illuminance (Lux), out door air temperature (OAT), and the output (out) is binary. This decision tree utilizes no weather prediction data and derives prediction information from only the linear model parameters and current time step inputs and states. Slight improvements in classification were observed by utilizing indoor air temperature predictions from the edge of the control horizon using weather predictions, but the incremental benefit of ∼1% classification accuracy was outweighed by the simplicity of the rules selected.