Week 6

Week 6 11/5/2018

Software Team

Consolidate Codes
Create Repository
.txt file to data array conversion using panda
Github created: https://github.com/Drexel-VIP-IOT
Upload Rest of Data from Dropbox to GitHub
Benchmark FileReader.py with Rakeen’s previous python code

Next Steps

Input Clustering Code into Cloud
Train Linear SVM
Input Data Projections into a Computer on the FOG
Input Linear SVM and Data Processing Code to Raspberry Pi

Hardware Team

Finished full Cloud Set up

Note: Use Raspberry Pi chain for beowulf clusters to increase computing power of EDGE

Start Raspberry Pi Mapping

Read latest Cornell Cup Proposal to better undertstand the scope of the project

Research about methods to share data over a network using Raspberry Pi and External Drive 

Next Steps

To complete raspberry pi network mapping
Setup remote connectivity to the cloud
MongoDB can now be remotely accessed outside the lab
Ran into errors for raspberry pi mapping
Looking into more documentation
Equipment has arrived
Integrate and setup the multi-raspberry pi environment
Use same process to map all pi's
Complete network attached storage between raspberry pis

Week 1-4 Review of Previous Work and Timeline

Previous work began with a Data Mining Approach to NDE Data:

Fatigue Damage Assessment Leveraging Nondestructive Evaluation Data

Initial Frameworks:

Timeline:

Week 5

HARDWARE
-Map DIC and XP PC document folders to Raspberry Pi
-Various document sharing methods
-Raspbian OS to Samba to Network Map
-Adding external disk to Raspberry Pi and sharing it over Network
-continue connection of raspberry pi with computers
Creat NAS
-Revamp of IoT framework

-MongoDB Server is live on the Cloud

Next Steps
-Prove different data stream types beside .txt
-Finish Mapping Raspberry Pi
-Test Mongo DB server

SOFTWARE
Matthew
-successfully produced PCC for correlation comparison

-Principal Component Analysis sci-kit and Matlab use normalization factors differently

-Same eigenvectors are used

-Successfully produced Gaussian Mixture Model for AE Clustering

-Data Trend Analysis for Monotonic Testing


Next Steps
-interpolative data fusion
- SVM development
-Data Quality
-combine machine learning algorithm