Data In Motion

IoT Series:  MiNiFi Agent on Raspberry Pi 4 with Enviro+ Hat For Environmental Monitoring and Analytics
Summary:  Our powerful edge device streams sensor readings for environmental readings while also performing edge analytics with deep learning libraries and enhanced edge VPU.   We can perform complex running calculations on sensor data locally on the box before making potentially expense network calls.  We can also decide when to send out data based on heuristics, machine learning or simple if-then logic.

Use Case:   Monitor Environment.   Act Local, Report Global.


Stack:   FLANK


Category:   AI, IoT, Edge2AI, Real-Time Streaming, Sensors, Cameras, Telemetry.


Hardware:  Intel Movidius NCC 2 VPU (Neural Computing), Pimoroni Enviro Plus pHAT, Raspberry Pi 4 (4GB Edition).


Software:  Python 3 + Java + MiNiFi Agents + Cloudera Edge Flow Manager (EFM/CEM) + Apache NiFi.   Using Mm... FLaNK Stack.


Keywords:  Edge2AI, CV, AI, Deep Learning, Cloudera, NiFi, Raspberry Pi, Deep Learning, Sensors, Io…

Custom Processor for Deep Learning

Happy Mm.. FLaNK Day!
I have been experimenting with the awesome new Apache 2.0 licensed Java Deep Learning Library, DJL.   In NiFi I was trying to figure out a quick use case and demo.   So I use my Web Camera processor to grab a still shot from my Powerbook webcam and send it to the processor.   The results are sent to slack.

Since it's the holidays I think of my favorite holiday movies:   The Matrix and Blade Runner.   So I thought a Voight-Kampf test would be fun.   Since I don't have a Deep Learning QA piece built yet, let's start by seeing if you look human.  We'll call them 'person'.   I am testing to see if I am a replicant.  Sometimes hard to tell.   Let's see if DJL thinks I am human.

See:   http://nautil.us/blog/-the-science-behind-blade-runners-voight_kampff-test



Okay, so it least it thinks I am a person.   The classification of a Christmas tree is vaguely accurate.






It did not identify my giant french bread.
Build…