intelligent sensors and sensor networks

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intelligent sensors and sensor networks:
in context of human localization and material classification

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intelligent sensors and sensor networks

  1. 1. 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
  2. 2. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  3. 3. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  4. 4. HUMAN LOCALIZATION-SYSTEM ARCHITECTURE Figure : System Architecture SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  5. 5. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  6. 6. HUMAN LOCALIZATION Spiking Neural Network(SNN) is used. SNNs have the capability of memorizing Spatial context Temporal context SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  7. 7. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  8. 8. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  9. 9. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  10. 10. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  11. 11. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  12. 12. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  13. 13. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  14. 14. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  15. 15. HUMAN POSITION DETECTION Figure : SNN for Human Detection by LRF SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  16. 16. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  17. 17. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  18. 18. TIME-SERIES OF HUMAN POSITION IN A ROOM Figure : Transition of Human Position by SNN SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  19. 19. CONNECTION WEIGHTS AFTER LEARNING Figure : Connection Strength after Learning of SNN SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  20. 20. DISCRIMINATION OF MATERIAL TYPE-SYSTEM ARCHITECTURE Figure : The Sensor with Plunger Based Probe and Optical Mouse SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  21. 21. NEURAL NETWORK BASED CLASSIFIER (A) Multi − Layer Perceptron Neural Network(MLP NN) Feed-Forward Neural Network Figure : MLP NN SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  22. 22. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  23. 23. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  24. 24. 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 SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  25. 25. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  26. 26. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  27. 27. BLOCK DIAGRAM OF USIC Figure : Block Diagram of USIC SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  28. 28. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS
  29. 29. 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. SURINDER KAUR 2012CS13 M.TECH(1st Year) INTELLIGENT SENSORS AND SENSOR NETWORKS

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