Gluon and Apache MXNet have been great for deep learning, especially for newbies like me. They added a Deep Learning Toolkit that is easy to use and has a number of great pre-trained models that you can easily use to do some general use cases around computer vision. So, I have used a simple well-documented example that I tweaked to save the final image and send some JSON details via MQTT to Apache NiFi. GluonCV makes this even easier! Again, let's take a simple Python example, tweak it, run it via a shell script, and send the results over MQTT.

At CES in January, Double Robotics announced the Double 2, a major upgrade to their super skinny telepresence platform that features better stability and turbo speed. It looked cool, but we didn't get super excited about it, because like most telepresence robots, it's designed to work very well in some very specific, usually business or education-focused environments. We've tested these things out before, and once you get past some hiccups and quirks, they generally do what they're supposed to do, which is provide you with a mobile embodied presence somewhere that you're not. When Double Robotics asked us if we wanted to test out a Double 2, we said sure, with two conditions: 1. it had to come with an LTE cellular data connection, allowing us to use the robot free of Wi-Fi; and 2. we could take it anywhere we wanted. To their credit, the company didn't even hesitate, and they shipped us a brand new Double 2, along with the camera and audio kit accessories and charging dock. Cool, now we can see what this robot can do--and maybe what it can't. Where are we taking it?

Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.

The ability how they can be combined. Thus the user would be to program in natural language instead of traditional taxed more heavily with a natural language system programming languages would enable people to use than with a traditional system. A second argument familiar constructs in expressing their requests, thus against natural language programming relates to its making machines accessible to a wider user group.

American Journal of Computational Linguistics, Volume 6, Number 2, April-June 1980