The Swiss Medicine & Pharmaceuticals,

Consultation In Applied Research Using Ai & ML @ Institute Of Bio Medical Engineering, University Of Oxford<

    Non Prejudicial Disclosure, Intellectual Property: Solution Design Using Ai in Bio Medical Engineering, To:

    Professor Dr. Alison Noble
    Bio Medical Engineering,
    University Of Oxford
    Institute Of Bio Medical Engineering

    Relevance:Bio Medical Engineering, Ai in Image Sciences
    Oct 2, 2021
    The Hague, Netherlands.European Union,EUC

    Click To see Case Study

    Our Proposal of application of Model driven Streaming Digital system of systems’ application with Ai and Support Vector Machine technologies for the following reasons in present research analysis at its present stage, because :


    It (integratoin) is due to the management of images and streaming technology combined management.

    So a platform and Data and Software provision service (which in industry called PAAS,DASS, SAAS) needs to be established to serve/render the images to multitenant-ed partner interface via RESTful api , under JSR 311, for consuming and rendition to all University Partners for research and clinical diagnostics for internal labs.



    Application of ML, Ai Resaerch Bio Medical Engineering, University Of Oxford, UK :

    Research & Development - Bio Medicla Engineering - Imaging Sciences

    The ultra sound imaging machinery such as GE (provided or others), provide APIs to send the images (can be converted as domain objects/classifiers) to be consumed by a business delegate (POJO) to provision to any interfaces via an adapter class. The streaming image provisioning API in those machines must be integrated via streaming API for continuous stream renditioning to consumer using packages such as open source Apache Kafka within Hadoop ecosystem. why? Because the stream can be synchronous or asynchronous depending on speed and transmission mechanism.

    To manage the load zookeeper instances are required to distribute the streams as it needs to be stored at some backend hbase persistence. The images can be of nested layers, depending on the composition of the image and the aggregation of the image objects or objectification in formations or configurations based on biological events, characteristics, angles etc, the images are classified using a DNN. Unsupervised learning works fine in this aspect for type classification and further if necessary state management (using unsupervised learning) if images tend to be of mutation of biological events as derived from Ultra sound or instruments such as Electron microscope / biomedical imaging devices.

    As it is required to provide a graphic user interface , (from what I understand for your email requirement.,)

    For UX in browser get request or post can retrieve to tier 1 UX, but it has to come thru a stream conversion adapter, which may be customized, in integration converting the Abstract XMLstream base factory which is decorated using JSON Stream factory and XML Stream factory, to finally combine or decompose (read/write or marshaling or un-marshaling) to display image parts.

    Note: A3iNet is a hybrid Deep Learning HDNN, Neural Network Systems, applied to Complex Systems Of Systems' Scientific and Industrial Systems Integration as Solution enforced with Ai for Large Scale Enterprise Integration, LSSI, developed by Swedish World Wide & Aerovition Digital Inc..