Updated NeuronRain FAQ - http://neuronrain-documentation.readthedocs.…

…io/en/latest/

index.rst

@@ -296,7 +296,7 @@ Psutils Dictionary Encoding of a process and Diff edit distance between two proc
 
 **Who can deploy NeuronRain?**
 
-Anyone interested in dynamic analytics driven kernel. For example, realtime IoT kernels operating on smart devices, autonomous driverless vehicles, robots, drones, embedded systems etc.,. There are already linux distros for drones and unmanned aerial vehicles (https://www.dronecode.org/) and automotives (Automotive Grade Linux - https://www.automotivelinux.org/). For example autonomous vehicles and drones have linux kernel drivers for LIDAR sensors for navigation which can be analytics driven. Linux kernel tree has support for LIDAR sensors and GARMIN GPS USB drivers (pulsedlight LIDAR driver - https://github.com/torvalds/linux/commits/master/drivers/iio/proximity/pulsedlight-lidar-lite-v2.c, GARMIN GPS USB drivers - http://elixir.free-electrons.com/linux/latest/source/drivers/usb/serial/garmin_gps.c). LIDAR sensor and GPS drivers can import kernel_analytics exported variables - from UAV autopilot, drone navigation for example. Present implementation of kernel_analytics driver in VIRGO32 and VIRGO64 reads /etc/kernel_analytics.conf by VFS kernel functions. In autonomous driving this file has to be overwritten in high frequency by machine learning userspace code. Intense File I/O in kernel modules is strongly advised against. Some realtime alternatives to this have been minimally implemented e.g perpetual reading of analytics variables from a streaming socket in a local or remote cloud node in kernelspace - something similar to Spark Streaming in Kernelspace. This would remove disk latency and necessity for storage of analytics variables - kernel_analytics driver reads the variables from socket and exports them kernelwide in an infinite loop. VIRGO64 kernel_analytics module has an optional function implemented to read stream of config variable-value pairs connecting to an analytics server and stored in a circular buffer exported kernelwide. 
+Anyone interested in dynamic analytics driven kernel. For example, realtime IoT kernels operating on smart devices, autonomous driverless vehicles, robots, drones, embedded systems etc.,. There are already linux distros for drones and unmanned aerial vehicles (https://www.dronecode.org/) and automotives (Automotive Grade Linux - https://www.automotivelinux.org/). For example autonomous vehicles and drones have linux kernel drivers for LIDAR sensors for navigation which can be analytics driven. Linux kernel tree has support for LIDAR sensors and GARMIN GPS USB drivers (pulsedlight LIDAR driver - https://github.com/torvalds/linux/commits/master/drivers/iio/proximity/pulsedlight-lidar-lite-v2.c, GARMIN GPS USB drivers - http://elixir.free-electrons.com/linux/latest/source/drivers/usb/serial/garmin_gps.c). LIDAR sensor and GPS drivers can import kernel_analytics exported variables - from UAV autopilot, drone navigation for example. Present implementation of kernel_analytics driver in VIRGO32 and VIRGO64 reads /etc/kernel_analytics.conf by VFS kernel functions. In autonomous driving this file has to be overwritten in high frequency by machine learning userspace code. Intense File I/O in kernel modules is strongly advised against. Some realtime alternatives to this have been minimally implemented e.g perpetual reading of analytics variables from a streaming socket in a local or remote cloud node in kernelspace - something similar to Spark Streaming in Kernelspace. This would remove disk latency and necessity for storage of analytics variables - kernel_analytics driver reads the variables from socket and exports them kernelwide in an infinite loop. VIRGO64 kernel_analytics module has an optional function implemented to read stream of config variable-value pairs connecting to an analytics server and stored in a circular buffer exported kernelwide. For realtime low latency requirements viz., autonomous vehicles, patching linux kernel with realtime PREEMPT_RT (https://git.kernel.org/pub/scm/linux/kernel/git/rt/linux-rt-devel.git/tree/) is suggested (though this has not been tested).
 
 **How does NeuronRain compare against other Cloud IoT platforms?**
 
@@ -362,9 +362,9 @@ emails: ka.shrinivaasan@gmail.com, shrinivas.kannan@gmail.com, kashrinivaasan@li
 
 Name "Krishna iResearch" is not commercially registered but only a profile name registered in SourceForge and later in GitHub and GitLab. Because of certain cybercrimes, mistaken identity and copyleft violation problems in the past (and possibility of a signature forgery too which I neither confirm nor deny), sumptuous id proofs of the author have been uploaded to https://sourceforge.net/projects/acadpdrafts/files/ and https://github.com/shrinivaasanka/Krishna_iResearch_DoxygenDocs/blob/master/ProductOwnerProfile_With_FunctionalityDescription.pdf.
 
-**How applicable is NeuronRain for Drones?**
+**How applicable is NeuronRain for Drones/Robots?**
 
-Drones have distinct software and hardware for mission plan (route map), flight and ground control often different from mainstream linux kernel. Mission plans are uploaded to drone by special protocols like MAVlink and userspace SDKs are available for it. Drone control userspace C++ code example in https://github.com/Dronecode/DronecodeSDK/blob/develop/example/fly_qgc_mission/fly_qgc_mission.cpp uses DronecodeSDK in userspace and there is no necessity for kernel_analytics kernel module to read analytics variables into kernelspace from userspace Machine Learning code. Application code can directly instantiate /etc/kernel_analytics.conf File locally/Socket Streaming Iterable in https://gitlab.com/shrinivaasanka/asfer-github-code/blob/master/python-src/Streaming_AbstractGenerator.py on a remote host and at port 64001 (in Python) and read analytics variables for drone navigation augmenting flight plan - Quite useful when static mission plans require dynamic changes after upload to drone e.g Military Reconnaissance, Autonomous Combat, Autonomous Online Shopping Delivery.
+Drones have distinct software and hardware for mission plan (route map), flight and ground control often different from mainstream linux kernel. Mission plans are uploaded to drone by special protocols like MAVlink and userspace SDKs are available for it. Drone control userspace C++ code example in https://github.com/Dronecode/DronecodeSDK/blob/develop/example/fly_qgc_mission/fly_qgc_mission.cpp uses DronecodeSDK in userspace and there is no necessity for kernel_analytics kernel module to read analytics variables into kernelspace from userspace Machine Learning code. Application code can directly instantiate /etc/kernel_analytics.conf File locally/Socket Streaming Iterable in https://gitlab.com/shrinivaasanka/asfer-github-code/blob/master/python-src/Streaming_AbstractGenerator.py on a remote host and at port 64001 (in Python) and read analytics variables for drone navigation augmenting flight plan - Quite useful when static mission plans require dynamic changes after upload to drone e.g Military Reconnaissance, Autonomous Combat, Autonomous Online Shopping Delivery. For robots, there are already linux add-on operating systems in development e.g ROS - http://www.ros.org/ which could benefit by kernel_analytics and VIRGO32/VIRGO64 system calls and drivers (kernel_analytics).
 
 **Can NeuronRain be deployed on Mobile processors?**