1. 15-April-14 Dept of ECE, JCE Belgaum 1
Under the guidance of: Prof. PraveenY.Chitti
by:
Abhishek Nandawdekar

2. List of contents:-
1. Introduction
2. WINS System for area detection
2.1 WINS Node architecture and identification of event
2.2 Signal transmission
2.3 Signal processing architecture
3. WINS MICRO SENSORS
4. GPS-GPRS BASED OBJECT TRACKING SYSTEM
5. CAPTURING AND PROCESSING OF IMAGES
6. Object Detection In Images By Components
7. 6.1 System Details
8. DETECTION OF METALS AND BOMBS
8. Conclusion
9. References
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3. Introduction
 Wireless Integrated Network Sensors (WINS) provide a new
monitoring and control capability for monitoring the borders of the
country. It combines sensing, signal processing, decision capability,
and wireless networking capability in a compact, low power system.
 Using this concept we can easily identify infiltrates or suspicious
vehicles entering the border.
 Using the satellite communication and GPS tracking, the area will be
identified and by Object identification system we will be able to get
the pictures of that particular area where the strangers have come
as well as the details of objects or people who are present there.
 The border area is divided into number of nodes. Each node is in
contact with each other and with the main node.
 Here first, it identifies the node where the harmonic signals are
produced by the unknown objects and the intensity of the signal will
be collected .This signal will be sent to the main node.
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4. WINS SYSTEM FOR AREA DETECTION
 The WINS network supports large numbers of sensors in a local area
within short range and has low average bit rate communication of
about 1kbps or even less.
 The network design must consider the requirement to service dense
sensor distributions with an emphasis on recovering environment
information.
 Multi hop communication yields better output and has scalability
advantages for WINS networks. Also, Multi hop Communication
networks permit large power reduction and the implementation of
dense node distribution. Multi hop communication, therefore, provides
an immediate advance in capability for the WINS narrow Bandwidth
devices.
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5. Figure 1. Multi Hop Communication System
(WINS nodes)
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Node
5
Node
2
Node
4
Node
3
Node
1
Conventional
network service
Main
server

6. WINS node architecture and identification of event
 The WINS node architecture as shown in Fig. 2 is developed to enable
continuous sensing, event detection and event identification at low
power.
 Since the event detection process must occur continuously, the
sensor, data converter, data buffer and spectrum analyzer must all
operate at micro power levels. In the event that an event is detected,
the spectrum analyzer output triggers the microcontroller.
 The microcontroller then issues commands for additional signal
processing operations for identification of the event signal. Protocols
for node operation then determine whether a remote user or
neighboring WINS node should be alerted. The WINS node then
supplies an attribute of the identified event.
 The WINS sensor systems must operate at low power, sampling at low
frequency and with environmental background limited sensitivity.
 The micro power interface circuits must sample at dc or low frequency
where “1/f” noise in these CMOS interfaces is large. The micro power
signal processing system must be implemented at low power and with
limited word length.
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7.  The WINS node event recognition may be delayed by 10 – 100 m
sec.
Continuous operation Low duty cycle
Figure 2. The wireless integrated network sensor (WINS) architecture.
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ADC
Sensor
Spectrum
Analyzer
Buffer
Control
Wireless
Control
Interfac
e

8. Signal transmission
 The sensed signals are routed to the major node. This routing is done
based on the shortest distance. This distance between the nodes is
not considered, but the traffic between the nodes is considered.
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9. Signal processing architecture
 If a stranger enters the border, his footsteps will generate harmonic
signals. It can be detected as a characteristic feature in a signal power
spectrum analyzer. Thus, a spectrum analyzer must be implemented in
the WINS digital signal processing system.
 The spectrum analyzer resolves the WINS input data into a low
resolution power spectrum.
 Here the corresponding frequency levels of the signals will be
examined and with this data the direction of their movement will be
identified later, the corresponding node addresses will be submitted to
the satellite communication network for identification of the location of
the node.
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10. WINS MICRO SENSORS
 Source signals (seismic, infrared, acoustic and others) all decay in
amplitude rapidly with radial distance from the source. To maximize
detection range, sensor sensitivity must be optimized.
 In addition, due to the fundamental limits of background noise, a
maximum detection range exists for any sensor. Thus, it is critical to
obtain the greatest sensitivity and to develop compact sensors that may
be widely distributed.
 Micro Electro Mechanical systems (MEMS) technology provides an
ideal path for implementation of these highly distributed systems.
 The sensor-substrate or “Sensorstrate” is a platform for support of
interface, signal processing, and communication circuits.
 Example of WINS Micro Seismometer and infrared detector devices are
shown in Figure 3. The detector shown is the thermal detector.
 This detector captures the harmonic signals produced by the foot-steps
of the stranger entering the border. These signals are then converted
into their Power Spectral Density (PSD) values and are then sent to
control system where these signals are compared with the reference
values set by the user.
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Figure 3. Thermal Infrared Detector

12. GPS-GPRS BASED OBJECT TRACKING
SYSTEM
 Location and positioning information can be obtained through the global
positioning system (GPS) or local positioning algorithms. This
information can be gathered across the network and appropriately
processed to construct a global view of the monitoring phenomena or
objects.
 The system allows a user to view the present and the past positions
recorded of a target object on Google Map through the internet. The
system reads the current position of the object using GPS. The object’s
position data is then stored in the database for live and past tracking.
 Here the node which has identified the particular signals will act as GPS
tracking device. So the tracking device is referred as the node which
has detected the signals. The system has two parts viz. the tracking
device and the database server as shown in Fig. 4.
 The device is attached with the moving object and gets the position
from GPS satellite in real-time. It then sends the position information
with the International Mobile Equipment Identity (IMEI) number as its
own identity to the server. The data is checked for validity and the valid
data is saved into the database.
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13.  When a user wants to track the device, he/she can log into the service
provider’s website and gets the live position of the device on Google
Map. A report is also generated which includes a detailed description of
the vehicles status. Users can also see the previous positions of the
device.
Figure.4 GPS TRACKING SYSTEM
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Get GPS
Location
from
Satellite
GPRS
Data base
server
location
GPS
WEB
server
END
USER
Laptop
with
internet
Vehicle
with GPS
device

14. CAPTURING AND PROCESSING OF IMAGES
 Based on the results given by this WINS system we will collect the
corresponding images of that particular area where the exact node has
detected and the images of the surrounding areas of that particular
node will also be collected then the images will be processed.
 Here the images will be taken by the satellite and with personnel system
that automatically collects the images from a long distance.
 A common operation is to do a "pan merge", which combines low-
resolution color band information with high-resolution panchromatic
images. This gives a color image with better resolution than would
otherwise would not have be possible.
 We get the true images that give us proper details of the objects. Now
the processed images will be submitted to the object detection system
which will detect the objects which are present in the images.
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15. OBJECT DETECTION IN IMAGES
 Here we will examine the images by satellite as well as with our personnel
system. Technique is demonstrated by developing a system that locates
people in cluttered scenes.
 The system is structured with four distinct example-based detectors that
are trained to separately find the four components of the human body: the
head, legs, left arm, and right arm.
 After ensuring that these components are present in the proper geometric
configuration, a second example-based classifier combines the results of
the component detectors to classify a pattern as either a person or a
nonperson.
 We call this type of hierarchical architecture, in which learning occurs at
multiple stages, an Adaptive Combination of Classifiers (ACC) system is
used. This system performs significantly better than a similar full-body
person detector.
 The algorithm is more robust than the full-body person detection method in
manner that it is capable of locating partially occluded views of people and
people whose body parts have little contrast with the background. We are
using a component-based person detection system for static images that is
able to detect frontal, rear, slightly rotated (in depth) and partially occluded
people in cluttered scenes without assuming any a priori knowledge
concerning the image. The framework described here is applicable to other
domains besides people, including faces and cars
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16. System Details
 The system starts detecting people in images by selecting a 128 X 64
pixel window from the top left corner of the image as an input.
 This input is then classified as either a person or a nonperson, a
process which begins determining where and at which scales the
components of a person, i.e., the head, legs, left arm, and right arm,
may be found within the window.
 All of these candidate regions are processed by the respective
component detectors to find the strongest candidate components.
 Thus object/person in the images are detected.
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17. DETECTION OF METALS AND BOMBS
 The people from the other enemy countries may carry explosives or
weapons with them and cross the border area. We need to detect the
kind of weapons and explosives that they have with
 Here we will detect and identify radioactive materials used in making
bombs and weapons of mass destruction (WMD) hidden in moving
vehicles, or on a person, even when heavily shielded.
 Nuclei within the explosive are momentarily aligned with the radio
waves. A transmitter emits pulses of low-intensity radio waves. After
each pulse, the nuclei emit a characteristic radio signal, like an echo.
The signal is picked up, amplified and analyzed.
 A computer issues a warning if it identifies a signal that is emitted only
by explosives. It provides a high probability of correct detection and
identification of suspect materials and very low probability of false
results.
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18. CONCLUSION
 WINS require a Microwatt of power. But it is very cheap when compared
to other security systems such as RADAR under use. It is even used for
short distance communication less than 1 Km. It produces a less
amount of delay. Hence it is reasonably faster.
 On a global scale, WINS will permit monitoring of land, water, and air
resources for environmental monitoring. On a national scale,
transportation systems, and borders will be monitored efficiently and for
safety, and security.
 Here our system not only monitors the area but also recognizes the
area and collects the images and process them. After the identification
of the object and detection of metals we will have an idea about animals
or human beings or other objects like tankers etc crossing the border
area.
 So, finally we can have a clear idea of the strangers or strange objects
which are entering into the border area.
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19. REFERENCES
 G. J. Pottie, W. J. Kaiser, L. P. Clare, and H. O. Marcy, “Wireless
integrated network sensors,” submitted to IEEE J. Selected Areas in
Communications
 Text book “Information Processing and Routing in Wireless Sensor
Networks”
 G. Asada, M. Dong, T. S. Lin, F. Newberg, G. Pottie, H. O. Marcy, and
W. J. Kaiser, "Wireless Integrated Network Sensors: Low Power
Systems on a Chip”, Proceedings of the 24th IEEE European Solid-State
Circuits Conference, 1998
 G.J. Pottie and W.J. Kaiser, “Wireless integrated network sensors,”
Comm. ACM, vol. 43, No. 5, May 2000, pp. 51-58.
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