Health & Fitness
AI-based applications can improve health outcomes and quality of life for millions of people in the coming years. Applications range from personal wearable monitoring activity and bio-signals in a continuous and daily manner, to clinical decision support for health professionals, patient monitoring and coaching, automated devices to assist in surgery, management of healthcare systems.
On a given signal, teach slices of signal as type A or B and monitor the occurences of these waveforms A or B in real-time. Same principle applies for data and other streaming data. In this example, the feature vectors broadcasted to the neurons are raw data extracted from different slice of signal. In a real application, you may want to use more advanced features such as the histograms of peaks and/or zero crossings, FFTs, Mel Ceptrum Coefficients, etc. The NeuroMem SDKs allows you to practice with these techniques.
- Possible applications of wavefront sensors
- Wavefront sensors : state of the art, problem and specifications
- Real-time embedded approach
- New miniaturised wavefront sensor
- Conclusion and outlook