NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

NEURAL NETWORKS NEURAL NETWORKS FOR SENSORS FOR SENSORS

AND MEASUREMENT SYSTEMSAND MEASUREMENT SYSTEMS

Part IIIPart III

Vincenzo PiuriVincenzo Piuri

University of Milan, ItalyUniversity of Milan, Italy

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

OUTLINE

• Sensor enhancement by soft computing

• Sensor linearization

• Sensor fusion

• Virtual sensors

• Remote sensing

• High-level sensors

• Distributed intelligent sensing systems

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

SENSOR ENHANCEMENT

• Advanced sensors

• Higher accuracy

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

IMAGE SENSOR

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

ARTIFICIAL RETINA

Class-E Power Amplifier

ASK Modulator

PWM Encoder

Image Processor

Video Camera

ASK Demodulator

Clock/Data Recovery

Power Recovery

Configuration & Control

Electrode Stimulator 1

Electrode Stimulator N

…

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

VISUAL SENSOR

Image Sensor Output

Enhanced Output by NN

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

HEARING SENSOR: ARTIFICIAL COCLEA

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

sensor array

pre-processing

pattern database

classifier

ODOR SENSOR: ARTIFICIAL NOSE

linear or quasi-linear problems

non-linear problems

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

TASTE SENSOR: ARTIFICIAL TONGUE

sensor array

pre-processing

pattern database

classifier

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

TACTILE SENSOR

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

TACTILE SENSOR FOR SLIPPAGE

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

PRESSURE SENSOR

T

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

DISTANCE SENSOR

no noise 10dB noise

MLPNN

thresholddetector

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

SHORT DISTANCE & ROUGHNESS SENSOR

coarse ground surface

fine ground surface

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

TEMPERATURE SENSOR

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

UMIDITY SENSOR

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

GAS SENSING

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

• Physical quantities (velocity, angular velocity, flow, force, torque, strain, …)

• Electrical quantities• Dielectrical quantities• Magnetic quantities• Optical quantities• Chemical quantities• Biological quantities• …

OTHER SENSORS

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

SENSOR LINEARIZATION

• Linear sensor output

• Simplify the analysis and the use of sensor data

• Monitoring and control systems with • simpler structure• higher performance

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

SENSOR LINEARIZATION (2)

Temperature Sensor Continuous-valued implementation

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

SENSOR LINEARIZATION (3)

Digital implementation: 8 bits Digital implementation:

16 bits

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

SENSOR FUSION

Data generated by group of sensors

are merged to produce

combined information as a single output

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

SENSOR FUSION (2)

• Sensors for the same physical quantity» accuracy enhancement» drift compensation»fault tolerance

SS

S

merged sensor data

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

• Sensors for different physical quantities» to integrate and combine partial information

into comprehensive views» to remove the influence

of interdependent physical quantities

SENSOR FUSION (3)

S1S2

S3

merged sensor data

S4

O1

O2

S1

S2

S3

merged sensor data

S4

O1

O2

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

VIRTUAL SENSING SYSTEMS

To measure quantities – without direct sensing the measurand quantity,

when direct sensing is not technically feasible or convenient

– by using indirect techniques, when the desired quantity is difficult to be measured while other strictly related quantities can be measured

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

VIRTUAL SENSING SYSTEMS (2)

Virtual electrode to measure the neural activity in vivo by using a MEG sensor array

Measured auditory field Virtually-measured auditory nerve activity

by NN

Virtually-measured auditory nerve activity by minimum-variance

method

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

VIRTUAL SENSING SYSTEMS (3)

Virtual sensors for noxious emission monitoring in a chain grate stoker

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

REMOTE SENSING

• Virtual measurement system

• The quantity to be measured is remote from the measurement system

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

REMOTE SENSING (2)Measurement of Earth surface parameters from satellite observations

Inputs: surface brightness at various Hz

Canopy T Soil T Canopy-water content

Soil-moisture content

6 channelsno noise

4 channelsno noise

6 channels2K noise

4 channels2K noise

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

REMOTE SENSING (3)

Rainfall measurement from satellite microwave observation

Inputs: cloud brightness at various Hz

Validation Real measurements

NN

regression

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

REMOTE SENSING (4)

CO measurement from satellite images

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

HIGH-LEVEL SENSORS

• Abstract sensors without any physical direct implementation

• Merge physical data to extract abstract measurement

• Classification

• Clustering

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

HIGH-LEVEL SENSORS (2)Detection of defects in mechanical parts by eddy current analysis

Calculated output NN output

Output error

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

HIGH-LEVEL SENSORS (3)

Detection of fires by image analysis

NIMIA 2001- 9 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy

DISTRIBUTED INTELLIGENT SENSING SYSTEMS

• Networked sensors

• Cooperating sensors

• Agencies of measurement agents

• Neural networks to enhance the outputs of individual sensors

• Neural networks to merge/enhance the multi-sensor observations

• Neural networks for distributed remote sensing • Neural networks to create high-level views from

distributed measurements