Microsensors are particularly buoyant sector in the industry of
man-made complex machines. Traditionally, the main sensor requirements
(linearly transferred from the macro sensors industry to the micromachining
technologies) were in terms of metrological performance, i.e.
the (most often) electrical signal produced by the sensor needed
to match relatively accurately the measurand. Such basic sensor
functionality is no longer sufficient. The nature of industry
demand, and therefore the research goals of the sensing community
are presently shifting, away from aiming to design perfect mono-function
transducers towards the utilization MEMS based sensors as system
components. A new set of requirements for sensing systems and
more generally for measurement systems is therefore being generated.
Such requirements ultimately imply that components are enhanced
with increasingly autonomous functional capabilities. It is here,
in the area of data processing and extraction of information,
that the author proposes to situate the core of the tutorial,
expanding both ways:
- towards the sensing devices themselves and the MEMS technology
which enables their production and
- towards the application end of the enhanced sensing systems.
The presentation clarifies the strands of development in sensing,
some of which are linked with the industry demand for “replacement
products” ( process/instrumentation sensors designed for
high accuracy or cheap/minimum size& weight/minimal electronics
sensors for liberal use in appliances and automotive industry
for example), whilst other strands are under development either
to enable new applications or to support the dreams of future
machines ( for example large networks of sensors exhibiting collective
behaviour and ultimately cogent sensing to enable cogent actuation
and eternal vehicles). The evolution process is discussed from
a system requirements perspective and supported by an analysis
of the components which make a sensor/sensor system, from the
simplest such sensor performing straight forward metrology through
the self-testing sensor to the fully fledged cogent sensor which
can autonomously make informed decisions on the data and perform
complex information transformations. The hardware and software
requirements of the sensors along this line will be discussed
and example implementations will be shown.
The newer pool of potential “big” sensors applications
need more than MEMS device technology perfection - the inherent,
natural MEMS properties of size and potentially low cost encouraged
the liberal usage of these devices in applications (smart skin
with thousands of devices embedded, deployable sensor webs, etc)
which in turn lead to the need to rely on/add efficient and clever
processing of data generated by the sensing device, before such
data reaches the outer world. Technology perfection might not,
therefore, be, in the new light, the primary aim in developing
successful MEMS sensors and particularly sensor systems. Since
signal processing is needed anyway by the sensing application,
most imperfections could also be, potentially, compensated for
in the software/hardware associated/integrated with the sensor,
as long as the integration of sensor and processing is resolved.
There are many current proposals for hugely ambitious information
gathering systems based on very large networks of autonomous intelligent
sensors. Typically, the proposals envisage thousands or millions
of such sensors, collaborating together to make an overall system
with highly advanced functionality (although, often, the precise
nature of this functionality is not explored). This tutorial addresses
such applications and presents a view on the specification and
design of such large sensor systems. The architecture proposed
in this tutorial provides standardised interfaces at appropriate
levels of abstraction in the system, which allow the initiation
and continuation of design and development at those levels of
Attendees will gain the perspective and context of the field in
order to make design decisions which optimally utilize current
and forthcoming developments in these technologies.
The tutorial will consist of four sessions (50 minutes/session).
Sessions will be run as a mixture of presentation and open discussion.
1. Oversold dreams and practical potentials
what was promised by MEMS and what was actually delivered –
current sensor markets
new dream applications (Smart Dust, GEMS, the ageless machines)
MEMS attributes, technological requirements and integration challenges
transducers: mechanical components and electronic interfaces
signal conditioning and integration
2. Advanced Microsystems - where the top down meets the bottom
enhanced sensor functionality – compensation, calibration,
self-test, power management
cogent sensors – achieving autonomy
3. Top down system functions in sensor systems
the data to information translation – what, where and how
self contained and information delivering systems
systems framework for very large networks of cogent sensors
a review of potential applications of large networks of sensors
skills and expectations of the design communities with respect
to such systems.
4. Concepts which contribute to a generic and adaptable system
design strategy for large sensor networks.
architectural model to locate the system components required for
building large microsensor networks
possible implementations of some system level components and additional
constraints that these place on the overall
topology establishment and management
initialisation and syncronization
application level functions such as data fusion
Elena Gaura selected publications:
. Gaura, E. Rider, R.J. Steele, N. (2000) Closed-loop
neural network controlled accelerometer, Proceedings of the I.
Mech. E, Part I, Journal of Systems and Control Engineering, vol.
214, no.I2, pp.129-138.
. Gaura, E.I.. Rider, R.J Steele N. and Naguib
R.N.G. (2000) Neural Network Based Smart Accelerometers for Use
in Telecare Medicine Journal of Systems Science, Vol. 26, No.
3, pp. 83-96.
. Steele, N. Gaura, E. Godjevac, J. (2001).
Neural Networks and Control. Int. Journal of Comp. Research, 10
(2) pp. 237-250
. Gaura, E.I. Rider, R.J. Steele, N. Naguib,
R. N. G. (2001). A Microsystem Approach to Acceleration Measurements
in Telecare Applications. IEEE Trans. on Information Technology
in Biomedicine, vol. 5, no. 3, pp. 248-252.
. Lewis, C. P. Gaura, E. (1998). The Sigma-Delta
Modulator - A Control Engineering Perspective, Symposium on Electronics
and Telecommunications, Proceedings, vol. 2, pp. 110-118, Timisoara,
. Gaura, E. Rider, R. J. Steele, N. Hesketh,
T. G. (1998). Neural Networks Approach for the Design of Closed-loop,
Micromachined Transducers, Microsystems - Sensors & actuators
for industrial applications, Technological Meeting at MICRONORA’98,
. Gaura, E. Kraft, M. Steele, N. Rider, R. J.
(1999). A comparison of approaches for the design of closed-loop
micromachined accelerometers, Thirteenth International Conference
on Systems Engineering, ICSE’99, Proceedings, pp.163-168,
Las Vegas, USA.
. Gaura, E.I. Rider, R.J. Steele, N. (2000).
Intelligent model based control of acceleration sensors. Third
International Conference on Modelling and Simulation of Microsystems,
MSM’2000, Proceedings, pp.513-516, San Diego, USA.
. Gaura, E.I. Rider, R.J. Steele, N. Beaumont,
A. (2000) Development of a Prototype Neural Network Controlled
Accelerometer. IFAC Mechatronic 2000, Proceedings, pp. 1039-1044,
. Gaura, E. Rider, R.J. Steele, N. (2000).
Developing smart micromachined transducers using feed-forward
neural networks: a system identification and control perspective
Proceedings of the IEEE International Joint Conference on Neural
Networks, IJCNN’2000, ISBN 0-7695-0619-4, Vol. IV, pp. 353-358,
. Gaura, E. Kraft, M. (2001). Comparison of
two novel control strategies for a closed loop micromachined tunnelling
accelerometer. Proceedings of the Fourth International Conference
on Modelling and Simulation of Microsystems, MSM’2001, Proceedings,
pp. 100-103, Hilton Head, USA.
. Kraft, M. Gaura, E. (2001). Intelligent Control
for a Micromachined Tunnelling Accelerometer Proc. Int. MEMS Workshop
(IMEMS), pp. 738-742, Singapore.
. Gaura, E. Kraft, M. (2002). Noise Considerations
for Closed Loop Digital Accelerometers, Proceedings of the Fifth
International Conference on Modelling and Simulation of Microsystems,
MSM’2002, Proceedings, pp.154-157, Puerto Rico, USA.
. Gaura E., Newman, R. (2003) Microsensors
and arrays: measurement validation, monitoring and automatic diagnosis
of sensor faults, Proceedings of the Fifth International Conference
on Modelling and Simulation of Microsystems, MSM’2003, San
. Elena Gaura, Robert M. Newman (2003) Microsensors,
arrays and automatic diagnosis of sensor faults, Proceedings of
the IEEE International Conference on Advanced Intelligent Mechatronic
AIM2003, Kobe, Japan, pp.360-366.
. Newman R. M., Gaura E. I. (2003) Using very
large arrays of intelligent sensors, Proceedings of the IEEE International
Conference on Advanced Intelligent Mechatronic AIM2003, Kobe,
. Gaura, E. Newman, R.M. (2004) Smart, Intelligent
and Cogent Microsensors – Intelligence for Sensors and Sensors
for Intelligence, Proc. of the 2004 Nanotechnology Conference
and Trade Show, Nanotech04, Boston, USA, vol.1, Chapter9 –
Smart MEMS and sensor systems, pp. 443-446.
. Newman, R.M. Gaura, E. I. (2004) Systems
issues in arrays of autonomous intelligent sensors, Proc. of the
2004 Nanotechnology Conference and Trade Show, Nanotech04, Boston,
USA, vol.1, Chapter9 – Smart MEMS and sensor systems, pp.