 |
Smart
Sensor Networks |
Large-Scale
Health Monitoring Data Analytics and Visualization |
| SYSTEM
MODEL |
• Distributed
Intelligent Health Monitoring (DIHM) Architecture (using CRE-SSN
as a distributed platform)
• Smart Sensor Control and Automated Logistics Support
by Layered Model with Compliance to IEEE 1451.0
• Optimized Routing Algorithms
• Sensor Design for Optimized Size, Weight, and Power
(< 100 ?A for Wired Communication)
• Power Consumption Models
• Small Footprint of Data-Driven Diagnostics & Prognostics
using High Performance Real Time Artificial Neural Networks
(ONGFE and Collaborative Learning Engine)
• Failure Characterization (Navigation Sensors and Subsystem)
• Prognostics based on Complex System Modeling
• Incremental Learning based on Collaborative Learning
Engine and Artificial Neural Networks
|
• Enterprise Infrastructure
(databases, client-server, smart devices, IT, and PHM distributed
architectures)
• Distributed Intelligent Health Monitoring (DIHM) Architecture
(using the CRE-SSN as a distributed platform)
• Smart Sensor and Smart Actuators Control for Automated
Logistics Support by Layered Model with Compliance to IEEE 1451.0
• Failure Characterization (Navigation Sensors and Subsystem)
• Prognostics based on Complex System Modeling
• Data-Driven Diagnostics using High Performance Real
Time Artificial Neural Networks (ONGFE and Collaborative Learning
Engine).
• Incremental Learning based on Collaborative Learning
Engine and Artificial Neural Networks
|
| RELATED
TECHNOLOGIES |
• Automated
Learning
• Ruggedized Hand-Held Devices Design for Data Collection
• Embedded Low Power Networking Interfaces
• Distributed Hardware Architectures
• MIL-STD-810G
• IEEE 1451.0 Smart Sensor Standard
• Energy Harvesting
• Sensor and System Design for Optimized Size, Weight,
and Power
• Regression by ANNs
• Pattern Recognition by Supervised, Unsupervised, and
Hybrid Schemes
• Fast Embedded Learning
• Real Time Processing
• Incremental Learning
• Automated Feature Selection
• Man Machine Interfaces and Visualization |
• Automated Learning
• Data Correlation Analysis and Feature Selection
• Cloud Technologies
• Data Mining
• Multi-Sensor Data Integration
• Smart Devices (smart phones, tablets)
• Man Machine Interfaces and Visualization
• Ruggedized Hand-Held Devices Design for Data Collection
• Distributed Hardware Architectures
• MIL-STD-810G Safety Standards
• Hardware in-the-Loop Integration by IEEE 1451.0 Smart
Sensor Standard |
| PATENTS |
•ONGFE: US Patent
2011/0167024 A1
•eCLE: US Provisional Application #61/633,374
•CRE-SSN: U.S. Provisional Application #61/849,108
|
•ONGFE: US Patent 2011/0167024
A1
•eCLE: US Provisional Application #61/633,374
•CRE-SSN: U.S. Provisional Application #61/849,108 |
| COMMUNICATIONS |
•Wired Communications
(I2C, SPI, and UART)
•Wireless Communications (Zigbee, IEEE 802.11.x)
Options
•Thru-metal Communication (Ultrasound) |
•Zigbee (IEEE 802.15.4)
•Bluetooth
•Wired Communications with compliance to MIL-STD-810G
•High Speed Wireless Data Links
•LAN/WAN
•Smart Devices Integration (android, iPhone, and tablets),
Power PC, and Ruggedized Mobile Computers
• IEEE 1451.0 (optional, in low level layers for hardware
in-the-loop Integration |
| HARDWARE |
•CRE-SSN Baseline
(Customizable)
•Ultra-low Power Processors (Microcontroller, Microprocessors,
and DSPs)
•Access to Hand-Held Ruggedized Devices with MIL-STD-810G
Compliance
•MicroElectroMechanical Systems
•PC104, PC104-Plus, Single Board Computer, Smart Devices
(android, iPhone, and tablets), Power PC, and Ruggedized Mobile
Computers
•ASIC-Analog/Mixed-mode
•Electromechanical Design
•PCB Layout
•Circuit Simulation/ Analysis
•EMI
•Firmware (FPGA) Development |
• Multi-core Processors
for Server Implementation
• Networking Infrastructure (IEEE 802.11.x interfaces,
routers, and servers)
• Clients based on Standard PC Computers with Optional
Hardware in-the-Loop (through network of distributed sensors)
• iPhone and Tablets
• Ruggedized Hand-Held Computers |
| SOFTWARE |
• Distributed
Intelligent Health Monitoring (DIHM)
•Optimized Neuro Genetic Fast Estimator (ONGFE)
• coremicro® Real-Time Structure Health Monitoring
Kernel (RTSHM-Kernel)
• Machine Evolutionary Behavior by Embedded Collaborative
Learning Engine (eCLE).
• Automated Feature Selection Toolbox
•Unsupervised clustering toolbox
•Proprietary Collaborative Learning Toolbox
•Real Time Interface Software
•Windows CE
•Unix
•Embedded OS |
• Smart-EI
Web Server based upon Windows, Apache, MySQL and PHP (WAMP)
• Distributed Intelligent Health Monitoring (DIHM)
• HTTP web server (Apache)
• Relational Database Management and query system based
on MySQL
• JavaScript Object Notation Interface Based Software
•Optimized Neuro Genetic Fast Estimator (ONGFE)
• Machine Evolutionary Behavior by Embedded Collaborative
Learning Engine (eCLE).
• Automated Feature Selection Toolbox
•Unsupervised clustering toolbox
•Proprietary Collaborative Learning Toolbox
•Real Time Interface Software
•Windows CE
•Unix
•Embedded OS |
| SENSOR
SUITE |
•Accelerometers
•PZT sensors
•Temperature
•Pressure
•Flow
•Humidity
•MEMS sensors
|
•Customizable in the Low
Level Layers according to system specifications |
