intelligent sensors and sensor networks

INTELLIGENT SENSORS AND SENSOR
NETWORKS
SURINDER KAUR
2012CS13
M.TECH(1st Year)
July 16, 2013
Department of Computer Science and Engineering
M.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 the
sensed data into a structured symbolic description that
supports reasoning by artiﬁcial intelligence processes.

NEURAL NETWORK APPROACH
Use of Neural Network in intelligent sensors for decision making
and 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 using
laser.
Speciﬁc Measurement System
Sun Small Programmable Object Technology(Sun SPOT) is
used.
Sun SPOT has 3 sensors:
Accelerometer
Illuminance sensor
Temperature sensor

HUMAN LOCALIZATION
Spiking Neural Network(SNN) is used.
SNNs have the capability of memorizing
Spatial context
Temporal context

OUTPUT PULSE OF THE NEURON
pi(t) =
1 if hi (t) ≥ qi
0 otherwise
where pi (t) : output pulse of the ith neuron at time t
hi (t) : internal state of ith neuron at time t
qi : threshold

INTERNAL STATE OF NEURON
hi(t) = tanh(href
i (t) + hsyn
i (t) + hext
i (t))
where href
i (t) : is the refractoriness factor of the neuron at time t
hsyn
i (t) : includes the output pulse from the other neurons
at time t
hext
i (t) : input to the ith neuron from the environment at
time t

REFRACTORINESS FACTOR OF NEURON
href
i (t) =
γref .href
i (t − 1) − R if pi (t − 1) = 1
γref .href
i (t − 1) otherwise
where href
i (t) : is the refractoriness factor of the neuron
at time t
γref : discount rate
R : constant such that R > 0

OUTPUT FROM OTHER NEURONS
hsyn
i (t) = γsyn.hi(t − 1) +
N
j=i,j=i
wji.hPSP
j (t)
where hsyn
i (t) : the output pulse from the other neurons at time t
hi (t − 1) : internal state of ith neuron at time t-1
γsyn: temporal discount rate
wji : connection weight from jth neuron to ith neuron
hPSP
j (t) : presynaptic action potential of jth neuron at time t

PRESYNAPTIC ACTION POTENTIAL OF NEURON
hPSP
j (t) =
1 if pj (t) = 1
γPSP.hPSP
j (t − 1) otherwise
where hPSP
j (t) : presynaptic action potential at time t
γPSP: discount rate, such that 0< γPSP < 1
pj (t) : output pulse at time t

ENVIRONMENTAL INPUT TO NEURON
hext
ij (t) = tanh(β.|dij(t) − dLTM
ij |)
where hext
ij (t) : input to the jth neuron of the ith LRF
β : constant such that 0 < β < 1
dij (t) : current distance value at time t
dLTM
ij : Long-Term-Memory of the distance
It is updated using the following equation:
dLTM
ij = (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
hPSP
ij (t) > H or
rU
ij (t) = 1
0 otherwise
where sij (t) : feature point from ith LRF to jth neuron at time t
pij (t) : pulse output of jth neuron on ith LRF at time t
H : threshold
rU
ij (t) : ﬂag, it is set if the distance of the jth measurement
point form the ith LRF can be measured

HUMAN POSITION DETECTION
It uses nearest neighbour approach.
Human position detection is based on 10 neighbourhood feature
points.
gij (t) =



1 if
5
k=−5
si,j+k ≥ S
0 otherwise

HUMAN POSITION DETECTION
Figure : SNN for Human Detection by LRF

WEIGHT UPDATION
If
hPSP
j (t − 1) < hPSP
i (t)
Then
wji = tanh(γwhtwji + ξwgt.hPSP
j (t − 1).hPSP
i (t))
where wji : connection weight from jth neuron to ith neuron
γwht: Hebbian discount rate
ξwgt: learning rate

SPECIFIC MEASUREMENT
Each furniture or equipment is attached with sensor.
The difference of current position from base value is input to
SNN.
If neuron fires, it means a person uses or moves its corres-
-ponding furniture.
The firing pattern indicates the time − series of human
position 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-SYSTEM
ARCHITECTURE
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 ith neuron in input layer
xi : input signal
yj = fa( wij.xi)
where yj : output of jth neuron in hidden layer or output layer
fa : the activation function
wij : connection weight from ith neuron to jth neuron
xi : the input from ith neuron to jth neuron

NEURAL NETWORK BASED CLASSIFIER
(B) Radial Basis Function Neural Network(RBF NN)
Hidden layer applies non-linear transformation from input
space to hidden space.
The transform function is radial − symmetrical on centre
point.
In the paper Gaussian function is chosen as basis function.

RBF NN
yi = xi
where yi : output of ith neuron in input layer
xi : input signal
yj =
i
wi.G(|x − xi|)
where yj : output of jth neuron in hidden layer or output layer
wi : connection weight
xi : the input from ith 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 following
performance 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 digital
ﬂexibility combined with an integrated micro controller.
It includes all of the processing elements needed to produce
many intelligent sensor systems.
The local intelligence is provided by the integrated RISC
processor.

BLOCK DIAGRAM OF USIC
Figure : Block Diagram of USIC

CONCLUSION
The proposed method of on − line human localization based
on SNN in intelligent sensor network can be combined with
voice recognition and visual perception for better natural
interaction between the human and the robot caregiver.
The proposed material classifier shows satisfactory
performance and RBF NN is proved better than MLP NN.
The proposed method is able to classify materials, attempts
can be made to classify the surface roughness of different
materials.
USIC provides a cost − effective solution to develop various
intelligent sensor applications using a common chip.

REFERENCES
T. Obo, N. Kubota, K. Taniguchi and T. Sawayama, “Human
localization based on spiking neural network in intelligent
sensor networks”,IEEE Workshop on,Robotic Intelligence In
Informationally Structured Space,2011.
Nadir N. Charniya, Sanjay V. Dudul, “Intelligent sensor
system for discrimination of material type using neural
networks”, Journal Applied Soft Computing archive Volume
12 Issue 1, Pages 543-552, January, 2012.
P. D. Wilson, S.P. Hopkins, R.S. Spraggs, I. Lewis, V. Skarda
and J. Goodey, “Application of a universal sensor interface
chip(USIC) for intelligent sensor applications”, in Proceedings
of the IEE Colloquium on Advances in Sensors, no. 232, pp.
3/13/6,December 1995.
Dekneuvel, E. and H. Medromi, “An ultrasonic intelligent
sensor for a mobile robot perceptron system. Principles,
design and experiments,” In IEEE Conference, 1999.

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