sensor seminar

8/7/2019 SENSOR SEMINAR

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INTRODUCTION

The development of sensor network technology is of great national importance

by virtue of critical applications directly related to the Australian environment. Sensor

networks provide the ability to gather accurate and reliable information, enabling early

warnings and rapid coordinated responses to potential threats. This encompasses the

ability to enhance national security from hostile threats as well as the ability to save lives

through environmental monitoring of natural disasters. Environmental sustainability can

also be improved through sensor network monitoring, by protecting valuable resources

from overuse or damage, as well as being able to collect valuable information previous

considered too difficult and too costly.

Applications of distributed sensor networks in habitat monitoring serve to provide

An Environmentally Sustainable Australia , by offering the potential for cost effective

solutions for monitoring environmental conditions. The feedback obtained from sensor

networks, provide valuable scientific information enabling a greater understanding of the

environmental impact of development, climate change and water usage for domestic use

as well as farming. In addition, the data collected by the appropriate sensors can be

applied directly for irrigation control, thus supporting Australian industries while

ensuring that steps are taken to implement sustainable practices. Further information may

be extracted pertaining to soil conditions, assisting in agriculture management for the

prevention of soil degradation. Such is the potential sensor development that the

monitoring and understanding of delicate eco-systems can be a viable proposition where

it is now often prohibitive. The potential cost effectiveness of intelligent sensors may

allow for large scale monitoring of emissions and the subsequent modification of

industrial processes. . One of the key research themes of the network is based on the

principle of smart information use. Efficient data management is a fundamental problem

faced in sensor network development in terms of the volume of data (particularly in

imaging), but also in terms of the value of the associated information. The outcomes of

this work will not only advance sensor network development but also related

applications .

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ABOUT SENSOR NETWORKS :

Recent emergence of very large number of diverse sensors and sensor networks

has the potential to impact on the quality of all areas of life. Scientific challenges in

realizing this potential is significant because of the multidisciplinary nature and

complexities involved. This research network builds on the best scientific talent

available in the interdisciplinary areas (biology, mathematics, statistics, computing,

electrical engineering and mechanical engineering) with the best overseas scientific teams

to solve the underlying scientific problems to enable the Australian industry to exploit

and apply this technology in areas of defence, health care and environment .

There are various focus of the sensor networks

1. Micro and Nano Sensors

2. Distributed Sensor Networks

3. Surveillance and Monitoring

4. Sensor Fusion and Tracking

5. Scheduling and Optimisation

6. Machine Intelligence

A unique feature of sensor networks is the cooperative effort of sensor nodes fitted

with onboard processors. Monitoring of activities overseas, bush fires, health, homes,

streets, airports, ports and the internet will be revolutionized using multiple interlinked

cooperative sensors enabling coordinated and timely response systems. Researchers of

this initiative are recipients of ARC special research centers, international grants,

federation fellowships and other major international initiatives focusing on aspects of the

central theme of this Network. This initiative facilitates to bring these isolated efforts to

fulfill the vision of the network. The aim of the sensor networks is the new initiative will

address the constrained multi-sensor problems to develop intelligent, economically viable

solutions of value to defence, homeland security, health sciences and environment.

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By drawing inspiration from biological multi-sensor systems and practical

insights from their artificial counterparts, this initiative will address the fundamental

problems relevant to the central theme of the network with an interdisciplinary approach.

By drawing inspiration from biological multi-sensor systems and practical insights from

their artificial counterparts, this initiative addresses the fundamental problems relevant to

the central theme of the network to develop intelligent, economically viable solutions of

value to defence, home land security, health sciences and environment. Specifically,

1. Australia to be the leading country in emerging field of intelligent sensor networks.

This has a huge potential for spin-off serving both local and global markets in the

business of defence, homeland security, bio-medical and environmental protection.

2. Development of Cost-effective, scalable surveillance solutions using unmannedaerial vehicles with ad hoc sensor networks for defence and home land security

enhancing the security capabilities of Australia.

3. Development of Revolutionary networked bio/nano sensors integrated with living

organisms for breakthrough applications in medicine further advancing the

Australia's leading position in health care industry.

4. Early disaster monitoring e.g., bush fires and other sensitive environments of

immense value to Australia, using large number of cheap, geographically

distributed sensors with limited communication capabilities saving billions of

dollars in damages.

5. A unique, leading research network with distributed resources leveraging

off high priority research initiatives in the field of intelligent sensors, networks and

information processing will be established .

6. By enabling a closer interaction between graduate students, post doctoral

fellows, leading researchers, and industry representatives, it produces a new

generation of entrepreneurs creating wealth for the nation.

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RESEARCH PROGRAMS

The following research themes emerged after 3 successful meetings with experts in

different field over a period 2 months. The list covers most of the major areas in the field

but is incomplete. A couple of theme areas would be added in the due course.

Intelligent Sensors

o Nanotechnology-Enabled Sensors

Sensor Networks

o Sensor Scheduling and optimisation

o Data Fusion & Tracking

Information Processing

o Surveillance and Monitoring

o Machine Learning

o

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INTELLIGENT SENSORS :

To support the requirements of distributed sensor networks, sensors must possess

greater functionality than simply gathering data and blindly transmitting the data to a

centralized sensor node. Intelligent sensors are an extension of traditional sensors to those

with advanced learning and adaptation capabilities. The system must also be re-

configurable and perform the necessary data interpretation, fusion of data from multiple

sensors and the validation of local and remotely collected data. Intelligent sensors

therefore contain embedded processing functionality that provides the computational

resources to perform complex sensing and actuating tasks along with high level

applications. The functions of an intelligent sensor system can be described in terms of

compensation, information processing, communications and integration. The combination

of these respective elements allow for the development of intelligent sensors that can

operate in a multi-modal fashion as well conducting active autonomous sensing.

Compensation is the ability of the system to detect and respond to changes in the

network environment through self-diagnostic routines, self-calibration and adaptation. An

intelligent sensor must be able to evaluate the validity of collected data, compare it with

that obtained by other sensors and confirm the accuracy of any following data variation.

This process essentially encompasses the sensor configuration stage.

Information processing encompasses the data related processing that aims to

enhance and interpret the collected data and maximize the efficiency of the system,

through signal conditioning, data reduction, event detection and decision making. This

may involve a collection of filtering and other data manipulation techniques together with

advanced learning techniques for feature extraction and classification in order to provide

the most relevant data in an efficient representation to the communications interface.

Communications component of intelligent sensor systems incorporates the

standardized network protocol which serves to links the distributed sensors in a coherent

manner, enabling efficient communications and fault tolerance.

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Traditional task specific sensor systems often contain a number of limitations in

terms of complexity, cost and flexibility. Intelligent sensors aim to overcome these

limitations through the utilization of standardized transducer interfaces and

communications protocols, resulting in autonomous, distributed, re-configurable sensors.

Integration in intelligent sensors involves the coupling of sensing and

computation at the chip level. This can be implemented using micro electro-mechanical

systems (MEMS), nano-technology and bio-technology. A hierarchical structure can be

used to describe the functionality of the system, where the lower layer performs the

signal processing functions, the middle layer performs the information processing and the

upper layer performs the knowledge processing and communications.

Validation of sensors is required to avoid the potential disastrous effects of the

propagation of erroneous data. This is different problem than overcoming individual

sensor failure. A control system operating decisions made on faulty data can lead to

unpredictable behaviour or even complete system failure. The impact of such errors may

be reduced through the use of a dense sensor network. The incorporation of data

validation into intelligent sensors increases the overall reliability of the system. So an

effective means for performing this function is required.

Data fusion techniques are required in order combine information from multiple

sensors and sensor types and to ensure that only the most relevant information is

transmitted between sensors. Consequently, the load on network bandwidth is kept at an

acceptable level. The area of sensor fusion can be approached from a variety of

perspectives. Biological science has been used to consider how sensor fusion is

accomplished, while cognitive science has explored why sensor fusion is an integral part

of perception.

There are various benefits for intelligent sensors, Intelligent sensors operating in a

task specific manner with effective data collection techniques enable the development

and application of more flexible sensor networks that efficiently utilize and coordinate

the limited resources of each individual sensor.

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By focusing resources according to the state of the surrounding environment and

on the immediate task, more efficient operation of the sensor and is ensured .

Accuracy: An intelligent sensor will incorporate features that enable it to

compensate for systematic errors, system drift and random errors produced due to system

parameters or the characteristics of the sensor.

Reliability: The incorporation of data and sensor validation techniques to detect

corrupted data, self-testing of network path connections and sensor operation, as well as

calibration of sensor drift, provides yet another level of system reliability in addition to

techniques already applied in the network design.

Adaptability: The processing parameters of an intelligent sensor system should

be determined automatically and adopted by a higher level in the system architecture.

This enables the optimization of the measuring and processing operations, as well as

enabling the sensor to adequately respond to changing environmental conditions.

The following are some existing research applications:

Automatic Assembly : Engineering components and assemblies could be made

more like organisms in there ability to self-assemble, thus having a significant impact

upon production speed, capacity and complexity. Self-repairing abilities are an obvious

side-effect of such abilities.

Microendoscopy : The ability to navigate micro or nano-structures through the

human body has the potential to make a significant impact on modern medicine.

Understanding the mobility and sensory systems of parasites, worms and insects, may

provide the necessary design information to realize this objective.

Intelligent suspension for automotive applications : A basic tuned suspension

unit requires a spring and damper unit. Tuning the suspension to a particular frequency is

generally overlooked. Vertebrate muscle maybe mimicked to construct a high

displacement and high damping spring system.

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Gels : The principle of a fluid enclosed in a membrane being made to do useful

work can be seen in our own muscular system, plants and in the skin of worms.

Contracting the muscles in the body wall and increasing its internal pressure the worm is

able to change shape. Controlling the swelling and contracting of a polymer gel

appropriately encased, enables a system to work as an artificial muscle.

Smart fabrics : Analyzing the insulation layers of animals and other natural

responses to temperature fluctuations may contribute to the development responsive

clothing, with properties based on the state of activity of the wearer. This would reduce

the number layers required by the wearer while remaining suitable for a variety of

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weather conditions.

Nano technology- enable sensors:

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Current nanotechnology permits the operation on the scale of atoms and

molecules. This promises to have a dramatic impact on sensor design and capabilities.

Nanotechnology has become a key technology in sensor development. Sensors can now

exploit novel properties of materials at the nano-scale. Chemical and biological materials

operate at the nano-scale, hence nanotechnology is well suited to design of chemical and

biological sensors.

Initial research in nanotechnology involved ministration of the macro techniques.

The small size of these sensors leads to reduced weight, low power requirements and

greater sensitivity. In recent years there has been a large increase in the amount of

funding put into nanotechnology. Over the last 5 years there has been a 5 fold increase.

Industries befitting from Nanotechnology include transportation, communications, building, medicine, safety and security.

The new possibilities now available are endless. The ability for atomic

bricklaying lets the designer be precise when designing new sensors. This ability will

reduce the amount of defects in new devises. At the atomic level the materials have new

properties which can be exploited like surface and quantum effects. Nanotubes have been

shown to have a number of uses in sensor technologies. They are extremely narrow

hollow cylinders made of carbon atoms. The orientation of the carbon atoms can affectthe conducting and semi-conducting properties. These can be used to integrate electrical

circuits for the design of sensors. These nanotubes can be grown on existing structures.

The existing IC technologies can be used to integrate these nanosensors into integrated

electronic circuits. The sensor chips can be used as building blocks to build new more

complex sensors.

Nanotechnology has a vast number of applications. IBM is working on data storage proximal probes. These can make and read nanometer-scale indentations in polymers.

The current densities are around 1x1012 bits per square inch which is greater then the

current magnetic based recording devices.

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Advances in nano-manufacturing have been from the top-down approach and

bottom-up approach. Conventional microelectronics (lithography, etching, and

deposition) has approached the nanometer scale. The current line widths in chips are near

the 100 nm. Manufacturing from the bottom-up is also possible using individual atoms

and molecules to build useful structures. IBM has managed to write IBM using individual

atoms. Designers can also combine micro and nanotechnologies to develop new sensor

systems.

Using computers for the design of new nanotechnology is important. Understanding the

interactions of atoms and molecules is required when simulating using powerful

computers and algorithms.

Working at such a small level also has it's own problems. The new sensors are very

sensitive. These sensors are prone to degradation from the effects of foreign substances,

heat, and cold. At such a small scale the micro effects become more significant. This

problem can be partially overcome by installing hundreds of sensors in a small space.

This allows malfunctioning devices to be ignored in favor of good ones.

The amount of applications of nano-sensors can be applied to is only limited

by the imagination. Physical sensors, electro-sensors, chemical sensors and bio-sensorscan all benefit from nano-technology.

Walter de Heer has devised a Balance . This balance is the smallest in the world. A

particle to be weighed is placed on a nanotube. The mass of the particle was calculated

from changes in the vibrational resonance frequency with and without the particle. The

balance can be used to weigh signal molecules.

Measurement of electricity is important and the bases for a large number of sensors. A

submicron mechanical electrometer demonstrated charge sensitivity below a single

electron charge.

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SENSOR SCHEDULING AND OPTIMISATION

A sensor network is an array of sensors of diverse type interconnected by acommunications network. Sensor data is shared between the sensors and used as input to

a distributed estimation system which aims to extract useful information from the

available sensor data. When all the data from all the sensors in the network is available at

one place, conventional centralized estimation techniques can be employed. Centralized

multi-sensor estimation theory critically assumes that the choice of which data to send

from any particular sensor to the centralized node is fixed. If the network bandwidth

changes during operation then such a system design can at best respond in a pre-planned

manner. The centralized approach is typically only feasible for certain non-critical static

sensor situations, as it fails to adequately address the issues of scalability or of

survivability under real world degradation. Destruction of the central node (or an

associated critical communications link) results in total network failure. A scalable

system is required, in which new sensors or network links can be easily added, that

continues to provide useful information even when some sensors and parts of the network

fail or are destroyed, and can continue to operate within performance bounds while the

communications bandwidth is varying (perhaps due to environmental conditions or electronic jamming).

In order to realise these practical requirements, a distributed estimation

architecture as shown in Figure 1 is needed. Distributed data fusion overcomes many of

the limitations of centralized fusion but also introduces new problems. To date distributed

data fusion systems have been heavily reliant upon having very high communication

bandwidths, due to the large amount of sensor data that must be transferred in real time

between sensor nodes. The acquisition cost of such high bandwidth communications

systems is usually very significant. Network scalability is also affected by the amount of

allocated bandwidth. Hence to ensure that the sensor network operates and is scalable to

some reasonable degree, very high bandwidths must typically be allocated.

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In order to solve the bandwidth problem, the system must make use of

dynamically changing partial information from remote sensors by requesting sensor data

in an optimal manner so that the most useful sensor data is sent over the currently

available network bandwidth (no matter how small the available bandwidth is).

The time sequence which specifies the best sensor data to utilise is called the

optimal sensor schedule . Many currently implemented sensor scheduling algorithms for distributed sensors employ ad hoc sensor scheduling techniques. The problem with such

approaches is the difficulty in quantifying system performance in multi-target or dynamic

sensor and bandwidth conditions. Therefore, there is a need to develop a well founded

analytic approach to the distributed sensor scheduling problem based on stochastic sensor

scheduling and control. The theory can be applied to the distributed multi-sensor

estimation problem where there are time-varying communication bandwidth constraints.

The underlying problem of stochastic sensor scheduling with system constraints,

however, presents a computational burden.

The significance of the sensor scheduling and optimization are

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Sensing accuracy: The utilization of a larger number and variety of sensor

nodes provides potential for greater accuracy in the information gathered as compared to

that obtained from a single sensor. The ability to effectively increase sensing resolution

without necessarily increasing network traffic will increase the reliability of the

information for the end user application.

Area coverage: A distributed wireless network incorporating sparse network

properties will enable the sensor network to span a greater geographical area without

adverse impact on the overall network cost.

Fault tolerance: Device redundancy and consequently information redundancy

can be utilized to ensure a level of fault tolerance in individual sensors.

Connectivity: Multiple sensor networks may be connected through sink nodes,

along with existing wired networks (eg. Internet). The clustering of networks enables

each individual network to focus on specific areas or events and share only relevant

information with other networks enhancing the overall knowledge base through

distributed sensing and information processing.

Minimal human interaction: The potential for self-organizing and self-

maintaining networks along with highly adaptive network topology significantly reduce

the need for further human interaction with a network other than the receipt of

information.

Operability in harsh environments: Robust sensor design, integrated with high

levels of fault tolerance and network reliability enable the deployment of sensor networks

in dangerous and hostile environments, allowing access to information previously

unattainable from such close proximity.

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Dynamic sensor scheduling: Dynamic reaction to network conditions and the

optimization of network performance through sensor scheduling. This may be achieved

by enabling the sensor nodes to modify communication requirements in response to

network conditions and events detected by the network, so that essential information is

given the highest priority.

Current sensor network applications include military sensing, air traffic

control, video surveillance, traffic surveillance industrial and manufacturing

automation, robotics, infrastructure monitoring and environment monitoring. Future

applications and capabilities may include the following :

Surveillance: A major current challenge for border surveillance is to deliver the

benefits associated with intelligent sensor networks using the existing low bandwidth

communications infrastructure. However, even very simple sensor networks have

proven to be very complex to properly control resulting in a number of ad hoc design

procedures yielding severely limited realizable benefits from the data fusion system.

The development of techniques for distributed data fusion within such sensor networks

is thus required.

Supply chain management: An important application arises in the well known problem of supply chain management in a warehouse. Several tens of mobile Personal

Digital Assistants (sensors capable of transmitting images, text and voice) interact with

central sophisticated servers provide command and control solutions for smooth

delivery of products and maintenance of inventory. Sensor scheduling algorithms are

immediately applicable to this application.

Data fusion and tracking :

Data fusion is one the fundamental elements of modern tracking techniques,utilising information from a variety of sources and combining the information in a way

that meets the desired application constraints and objectives.

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Data fusion is a framework describing the process of combining data originating

from different sources. The objective of data fusion is maximization of useful

information, such that the fused information provides a more detailed representation with

less uncertainty than that obtained from individual sources. While producing more

valuable information, the fusion process may also allow for a more efficient

representation of the data. Another by-product of information fusion may be the

observation of higher-order relationships between respective entities.

The selected method for performing the data combination will depend on the

original data format produced by the various sensor types. Data fusion is, in general,

conducted using one of the following frameworks:

Pixel level fusion

Feature level fusion

High-level data fusion

Pixel level fusion describes the combination of multiple images into a single

image, where raw data is robustly and redundantly merged. Each location in the resulting

image is an algorithmic combination of the vector of measurements from each of thesensors.

Feature level fusion refers to the extraction of features from each of the sensor

data. Registration of detected features is performed for regions of interest or image

segments containing more than one pixel. A detection/classification algorithm can then

be applied on the combined feature vector.

High-level data fusion or decision fusion occurs where sensor data, with or without pre-processing, is combined with other data or a priori knowledge. Each sensor

makes an independent decision based on its own observations and passes these decisions

to a central fusion module where a global decision is made.

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Alternatively, in a decentralized multi-sensor system each node functions

performs data fusion based on local observations and the information communicated

from neighboring nodes.

The significance of the data fusion and tracking are

Maximization of useful information

More reliable information than possible from individual sources

More efficient data and information representation

Detection of higher-order relationships between different dedicated sensor types

Shown below (Figure 1) are some of the techniques applied to the various elements of the

data fusion process.

Practical applications of data fusion have necessarily been those areas in which the

required output of an analysis may not be measured directly.

This is particularly important such as:

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Medical imaging

Non-destructive testing

Remote sensing applications such as target identification and tracking

Condition monitoring for the detection of faults and degradation of machinery

Landmine detection

INFORMATION PROCESSING

Information processing encompasses the data related processing that aims to

enhance and interpret the collected data and maximize the efficiency of the system,

through signal conditioning, data reduction, event detection and decision making. This

may involve a collection of filtering and other data manipulation techniques together withadvanced learning techniques for feature extraction and classification in order to provide

the most relevant data in an efficient representation to the communications interface.

The development of new techniques is required to process the large volumes of

information produced by sensor networks and adaptively implement the necessary

response.

This may encompass:

1. sensor scheduling

2. decision theory

3. feedback theory

4. state estimation

5. tailored supervisory control systems control systems

6. optimal sensor location

7. pattern recognition8. data mining

9. network flow control

10. multi-resolution data transmission integrated with data fusion and reconstruction.

There are various applications for the information processing

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Surveillance and Monitoring Surveillance and monitoring is an application

which places an ever increasing demand on the advancement and development of new

information processing techniques.

Data analysis : The operation of surveillance and monitoring systems implies the

detection and tracking of an event or target. Based on the sensor type and resulting data

output, appropriate algorithms and processing techniques are required to extract this

information in real time, in order for other systems to be able to react in some way, either

by providing and alert to a sub-system or modifying the behaviour of the surveillance

system, such as tracking a given target following positive identification.

Data reduction : With the continual introduction of new sensor technologies and

the influx of information previously unavailable, considerable processing of the gathered

data is required to enable efficient dissemination of the data.

Data interpretation: In order to obtain meaningful interpretations of the large

volumes and various forms of data obtained from various sensor type and networks,

additional processing of the information is required.

Tracking , fusion and vision systems : Neuro biology:

1. Moving target detection by insects - figure/ground discrimination (dragonflies,hoverflies)

2. Motion coding / adaptation (at least another 10 years basic physiology remains inthis area even to complete present project directions): This is the source for algorithms that feed following projects

3. Computer modeling of insect vision algorithms4. Natural image coding/ natural time series analysis

VLSI Robotic sensor fusion:

1. Adaptive motion chips (based on both of the above): Collaboration with Tanner Research Inc

2. Spatial imagers (low pixel count imagers for feedback control systems)

APPLICATIONS OF SENSOR NETWORKS

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Bushfire response using a low cost, typically dormant, distributed sensor

network early warning and localisation of bush fires can be achieved, hence saving life

and property, whilst reducing the cost of monitoring

Intelligent transportation low cost sensors build into roads and road signs can

assist to manage traffic flow and inform emergency services of traffic problems

Real-time health monitoring a nano-technology based bio-sensor network can

assist in monitoring an ageing population, and inform health care professionals in a

timely manner of potential health issues

Unmanned aerial vehicle surveillance swarms of low cost unmannedautonomous and co-operating aerial vehicles could be deployed to conduct surveillance

and monitoring in remote or hostile environments

Water catchment and eco-system monitoring and management sensor

networks that keep track of water quality, salinity, turbity and biological contamination,

soil condition, plant stress and so on could be coordinated to assist environmentally

sustainable management of entire water catchment areas

Robotic landmine detection A sensor network for the detection and removal or

deactivation of landmines. A reliable sensor network will enable the safe removal of

landmines in former war zones, reducing the risk to those involved in the removal

process. The cost effectiveness of the network will aid in the its application throughout

third world nations where the after effects of war continue to take a toll on people living

in areas still containing live explosives. The utilization of advanced sensor technology to

detect explosives, will overcome difficulties in detection of un-encased landmines.

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CONCLUSION

The University of Melbourne has experience in managing a variety of ARC-

funded and other major programs (utilizing a comprehensive network of financial,

ethical, management and resource systems) over many years. The department where the

lead researcher comes from has a long history of strong track record in securing

competitive grants including several ARC special research centers, Cooperative Research

Centers, and State Government funded Initiatives, and collaborative research centers. An

effective and efficient financial management system is already in place to provide highquality support for the management of research centers and networks

There has been a well established track record in running workshops, short

courses and conferences in areas related to this network. For example, the department

hosted the 6th International conference in Data Fusion 2003, will host the Asian control

conference in 2004, and will host the International conference in Intelligent Sensors in

December 2004. Several research assistants in the centre are well versed in web

authoring and on-line management of resources.

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REFERENCE LISTS

www.sensornetworks.net.au

www.research.rutgers.edu

www.jimpinto.com/sensornetworks.
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