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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