intelligent sensors and sensor networks

INTELLIGENT SENSORS AND SENSORNETWORKS

SURINDER KAUR2012CS13

M.TECH(1st Year)

July 16, 2013

Department of Computer Science and EngineeringM.N.N.I.T. Allahabad,India

SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS

INTELLIGENT SENSORS

Sensors that are capable of sensing and transforming thesensed data into a structured symbolic description thatsupports reasoning by artificial intelligence processes.


NEURAL NETWORK APPROACH

Use of Neural Network in intelligent sensors for decision makingand learning in the context of the following applications:

Human localization

Discrimination of material type using:

Radial Basis Function Neural Network RBFNN.Multi-Layer Perceptron Neural Network MLPNN.


HUMAN LOCALIZATION-SYSTEM ARCHITECTURE

Figure : System Architecture


HUMAN POSITION MEASUREMENT

Two complementary measurement are involved:

Global Measurement System:

Apply Laser Range Finders(LRFs).Measure distance based on time of flight principle by usinglaser.

Specific Measurement SystemSun Small Programmable Object Technology(Sun SPOT) isused.Sun SPOT has 3 sensors:

AccelerometerIlluminance sensorTemperature sensor


HUMAN LOCALIZATION

Spiking Neural Network(SNN) is used.

SNNs have the capability of memorizingSpatial contextTemporal context


OUTPUT PULSE OF THE NEURON

pi(t) =

{1 if hi (t) ≥ qi0 otherwise

where pi (t) : output pulse of the i th neuron at time thi (t) : internal state of i th neuron at time t

qi : threshold


INTERNAL STATE OF NEURON

hi(t) = tanh(hrefi (t) + hsyn

i (t) + hexti (t))

where hrefi (t) : is the refractoriness factor of the neuron at time thsyni (t) : includes the output pulse from the other neurons

at time thexti (t) : input to the i th neuron from the environment at

time t


REFRACTORINESS FACTOR OF NEURON

hrefi (t) =

{γref .hrefi (t − 1)− R if pi (t − 1) = 1

γref .hrefi (t − 1) otherwise

where hrefi (t) : is the refractoriness factor of the neuronat time t

γref : discount rateR : constant such that R > 0


OUTPUT FROM OTHER NEURONS

hsyni (t) = γsyn.hi(t− 1) +

N∑j=i,j 6=i

wji.hPSPj (t)

where hsyni (t) : the output pulse from the other neurons at time thi (t − 1) : internal state of i th neuron at time t-1

γsyn: temporal discount ratewji : connection weight from j th neuron to i th neuron

hPSPj (t) : presynaptic action potential of j th neuron at time t


PRESYNAPTIC ACTION POTENTIAL OF NEURON

hPSPj (t) =

{1 if pj(t) = 1

γPSP .hPSPj (t − 1) otherwise

where hPSPj (t) : presynaptic action potential at time t

γPSP : discount rate, such that 0< γPSP < 1pj(t) : output pulse at time t


ENVIRONMENTAL INPUT TO NEURON

hextij (t) = tanh(β.|dij(t)− dLTM

ij |)

where hextij (t) : input to the j th neuron of the i th LRFβ : constant such that 0 < β < 1

dij(t) : current distance value at time tdLTMij : Long-Term-Memory of the distance

It is updated using the following equation:

dLTMij = (1− α).dLTM

ij + α.dij(t)

α : constant such that 0 < α < 1


FEATURE POINT EXTRACTION

sij(t) =

1 if pij(t) = 1 or

hPSPij (t) > H or

rUij (t) = 1

0 otherwise

where sij(t) : feature point from i th LRF to j th neuron at time tpij(t) : pulse output of j th neuron on i th LRF at time t

H : thresholdrUij (t) : flag, it is set if the distance of the j th measurement

point form the i th LRF can be measured


HUMAN POSITION DETECTION

It uses nearest neighbour approach.Human position detection is based on 10 neighbourhood featurepoints.

gij(t) =

1 if5∑

k=−5si ,j+k ≥ S

0 otherwise


HUMAN POSITION DETECTION

Figure : SNN for Human Detection by LRF


WEIGHT UPDATION

If

hPSPj (t− 1) < hPSP

i (t)

Then

wji = tanh(γwhtwji + ξwgt.hPSPj (t− 1).hPSP

i (t))

where wji : connection weight from j th neuron to i th neuronγwht : Hebbian discount rateξwgt : learning rate


SPECIFIC MEASUREMENT

Each furniture or equipment is attached with sensor.

The difference of current position from base value is input toSNN.

If neuron fires, it means a person uses or moves its corres--ponding furniture.

The firing pattern indicates the time− series of humanposition in the room.

The human position can be approximated to that of the corr--esponding furniture or equipment.


TIME-SERIES OF HUMAN POSITION IN A ROOM

Figure : Transition of Human Position by SNN


CONNECTION WEIGHTS AFTER LEARNING

Figure : Connection Strength after Learning of SNN


DISCRIMINATION OF MATERIAL TYPE-SYSTEMARCHITECTURE

Figure : The Sensor with Plunger Based Probe and Optical Mouse


NEURAL NETWORK BASED CLASSIFIER

(A) Multi− Layer Perceptron Neural Network(MLP NN)

Feed-Forward Neural Network

Figure : MLP NN


MLP-NN

yi = xi

where yi : output of i th neuron in input layerxi : input signal

yj = fa(∑

wij.xi)

where yj : output of j th neuron in hidden layer or output layerfa : the activation functionwij : connection weight from i th neuron to j th neuronxi : the input from i th neuron to j th neuron


NEURAL NETWORK BASED CLASSIFIER

(B) Radial Basis Function Neural Network(RBF NN)

Hidden layer applies non-linear transformation from inputspace to hidden space.

The transform function is radial− symmetrical on centrepoint.

In the paper Gaussian function is chosen as basis function.


RBF NN

yi = xi

where yi : output of i th neuron in input layerxi : input signal

yj =∑

iwi.G(|x− xi|)

where yj : output of j th neuron in hidden layer or output layerwi : connection weightxi : the input from i th neuron in the lower layer and the

centre point.x : the real valued vector

The Gaussian function is given as:

G(z) = exp(−z2/(2 ∗ σ2))

where σ : variance


COMPARISON OF MLP NN AND RBF NN

The comparison between the two is made on the basis of followingperformance measures:

Mean Square Error(MSE)

Percentage Classification Accuracy(PCLA)

Area under Receiver Operating Characteristic curve(AROC)

RBF NN is found better than MLP NN on following grounds:

Less training iterations are required.

It is more noise tolerant.

Topology optimization is easy.


USIC

Universal Sensor Interface Chip

Device that has high degree of in built analogue and digitalflexibility combined with an integrated micro controller.

It includes all of the processing elements needed to producemany intelligent sensor systems.

The local intelligence is provided by the integrated RISCprocessor.


BLOCK DIAGRAM OF USIC

Figure : Block Diagram of USIC


CONCLUSION

The proposed method of on− line human localization basedon SNN in intelligent sensor network can be combined withvoice recognition and visual perception for better naturalinteraction between the human and the robot caregiver.

The proposed material classifier shows satisfactoryperformance and RBF NN is proved better than MLP NN.

The proposed method is able to classify materials, attemptscan be made to classify the surface roughness of differentmaterials.

USIC provides a cost− effective solution to develop variousintelligent sensor applications using a common chip.


REFERENCES

T. Obo, N. Kubota, K. Taniguchi and T. Sawayama, “Humanlocalization based on spiking neural network in intelligentsensor networks”,IEEE Workshop on,Robotic Intelligence InInformationally Structured Space,2011.

Nadir N. Charniya, Sanjay V. Dudul, “Intelligent sensorsystem for discrimination of material type using neuralnetworks”, Journal Applied Soft Computing archive Volume12 Issue 1, Pages 543-552, January, 2012.

P. D. Wilson, S.P. Hopkins, R.S. Spraggs, I. Lewis, V. Skardaand J. Goodey, “Application of a universal sensor interfacechip(USIC) for intelligent sensor applications”, in Proceedingsof the IEE Colloquium on Advances in Sensors, no. 232, pp.3/13/6,December 1995.

Dekneuvel, E. and H. Medromi, “An ultrasonic intelligentsensor for a mobile robot perceptron system. Principles,design and experiments,” In IEEE Conference, 1999.


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