Optimization of a cloud-based biological sample data processing system - Mário Diogo Pinto da Silva Cardoso (2021)
In the context of personalized medicine, progress has been made towards the integration of Photonics and Artificial Intelligence in the creation of a virtual library of disease biomarkers and biological profiles useful to provide quick and accessible mechanisms for screening and stratification of biological samples. In this context, this work addresses (i) the optimization of the existing prototype device responsible for the acquisition of data from biological samples and its transmission to the cloud for further processing and (ii) the design of a remote controlled orbital shaker station for the homogenization of biological samples prior to the data acquisition. In the current state of the prototype, the data throughput from acquisition to transmission does not scale favorably in relation to the increasing number of biological samples needing to be analyzed. The approach taken for the first part of this work consisted in the analysis of each data transmission step in order to find throughput optimization opportunities. Starting from the intra-device communication using the SPI protocol, it was possible to conclude, after careful waveform investigation and waveform quality assessment that the choice of the SPI clock frequency was below optimal levels and could be optimized by close to an order of magnitude. Regarding the codification, processing and wireless transmission of data, details related to both data encoding and the MQTT and Wi-Fi protocol usage were studied and potential bottleneck points were identified through experiment and statistical analysis. This led to the understanding that both the data encoding scheme as well as the Wi-Fi protocol used were sub-optimal and could be optimized by almost 30%. As for the MQTT protocol, no possible improvements have been identified. The second part of the work focused on the development of an orbital shaker for the homogenization of biological samples prior to data acquisition. For this purpose, a suitable brushless DC electric motor and controller were chosen after a proper market search, the required software to control the motor and a suitable user interface using a mobile device were developed to control the operation in time, direction and rotation speed. The design was successfully tested in a 3D printed mechanical mock-up.