NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy NEURAL NETWORKS FOR SENSORS AND MEASUREMENT SYSTEMS Part III Vincenzo Piuri University of Milan, Italy

NIMIA 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 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SENSOR ENHANCEMENT Advanced sensors Higher accuracy

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

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

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy VISUAL SENSOR Image Sensor Output Enhanced Output by NN

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HEARING SENSOR: ARTIFICIAL COCLEA

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy ODOR SENSOR: ARTIFICIAL NOSE linear or quasi-linear problems non-linear problems

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy TASTE SENSOR: ARTIFICIAL TONGUE sensor array pre-processing pattern database classifier

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

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy TACTILE SENSOR FOR SLIPPAGE

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy PRESSURE SENSOR T

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy DISTANCE SENSOR no noise10dB noise MLP NN threshold detector

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SHORT DISTANCE & ROUGHNESS SENSOR coarse ground surface fine ground surface

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

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

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

NIMIA 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 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 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SENSOR LINEARIZATION (2) Temperature SensorContinuous-valued implementation

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy SENSOR LINEARIZATION (3) Digital implementation: 8 bits Digital implementation: 16 bits

NIMIA 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 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 S S S merged sensor data

NIMIA 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) S1 S2 S3 merged sensor data S4 O1 O2 S1 S2 S3 merged sensor data S4 O1 O2

NIMIA 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 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 fieldVirtually-measured auditory nerve activity by NN Virtually-measured auditory nerve activity by minimum-variance method

NIMIA 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 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 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 TSoil T Canopy-water content Soil-moisture content 6 channels no noise 4 channels no noise 6 channels 2K noise 4 channels 2K noise

NIMIA 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 ValidationReal measurements NN regression

NIMIA October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy REMOTE SENSING (4) CO measurement from satellite images

NIMIA 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 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 October 2001, Crema, Italy - Vincenzo Piuri, University of Milan, Italy HIGH-LEVEL SENSORS (3) Detection of fires by image analysis

NIMIA 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