ThingBrain is a cloud-based machine learning platform for the Internet of Things.
- ThingBrain leverages machine learning to detect anomalies in sensor measurements, search for patterns in sensor data streams, forecast sensor measurements and predict external events using statistical models. Digital Signal Processing algorithms including sensor fusion, noise filters, Auto/crosscorrelation, Convolution, Window functions and others.
- State-of-the-art computer vision for the Internet of Things using deep neural networks. Track objects on video streams, classify images, extract feature points from images, detect faces, text and other objects.
- Unleash the power of machine learning to predict external events using sensor output or other data. Reinforcement Learning turns IoT devices and robots into intelligent agents that learn actions based on rewards. Bayesian optimization of black-box functions for sensor networks and robotics.
- Detect events and sensor measurements that do not conform to expected patterns. Outlier and novelty detection using supervised or unsupervised methods. Supported algorithms include Three Sigma, Seasonal Hybrid ESD, Multi-layer Neural Networks and Support Vector Machines.
- Unleash the power of machine learning to predict external events based on sensor measurements. Trained models are available online as a service and also can be downloaded for offline scoring. Multiple algorithms supported: Deep Learning, Ensembles of Decision Trees and Linear Models.
- Forecast sensor measurements or any Time Series using Autoregressive–moving-average models (ARIMA, ARMAX, ARIMAX, SARIMAX, etc.), Recursive Least Squares or Long short term memory recurrent neural networks (LSTM-RNN).
- Measure similarity between two sensor measurements using Dynamic Time Warping (DTW), Symbolic Aggregate approXimation (SAX), Cosine Distance or Likelihood analysis. Determine whether one sensor output is useful in forecasting another Time Series using Granger causality analysis.
- Turn sensor output into useful information applying algorithms to correct jitter, drift and noise of digital sensors. Untangle noisy signals with high/low/band pass filters including Butterworth, Chebyshev and Elliptic. Filter random variations and other inaccuracies using Kalman filters.
- Combine measurements from multiple sensors (ie. accelerometer and gyroscope) into a better signal using sensor fusion algorithms. Includes support for 3-, 6- and 9-axis fusion options, programmable sampling, fusion rates, frame of reference and more.
- Wide variety of Digital Signal Processing tools including Frequency / Amplitude / Phase Estimation, Fast Fourier Transforms, Hamming, Hanning and Rectangular windowing functions, Autocorrelation, Crosscorrelation, Convolution, Particle Filter and Hidden Markov Models.
Mora info: http://www.thingbrain.com