[attachment=1293]
ABSTRACT
This report discusses general architecture of smart sensors. Here we use the silicon technology in the architecture of smart sensors. Smart sensor utilizes some transduction properties and some electronic properties of silicon. Smart sensors are the sensors with integrated electronics that can perform one or more of the following functions logic functions, two way communications, make decisions. This report also pays attention to the importance and adoption of smart Sensors in addition to this an effort is made to present the design consideration of smart sensor as per the functions performed. The discussion will conclude with some examples of smart sensors developed at different research institutes.


1. Introduction
The advent of integrated circuits, which became possible because of the tremendous progress in semiconductor technology, resulted in the low cost microprocessor. Thus if it is possible to design a low cost sensor which is silicon based then the overall cost of the control system can be reduced .We can have integrated sensors which has electronics and the transduction element together on one silicon chip. This complete system can be called as system-on-chip .The main aim of integrating the electronics and the sensor is to make an intelligent sensor, which can be called as smart sensor. Smart sensors then have the ability to make some decision. Physically a smart sensor consists of transduction element, signal conditioning electronic and controller/processor that support some intelligence in a single package. In this report the usefulness of silicon technology as a smart sensor, physical phenomena of conversion to electrical output using silicon sensors, characteristics of smart sensors. A general architecture of smart sensor is presented.
2. Definition
Smart sensors are sensors with integrated electronics that can perform one or more of the following function logic functions, two-way communication, make decisions.
3. Usefulness of Silicon Technology in Smart Sensor
There are very convincing advantages of using silicon technology in the construction of smart sensor. All integrated circuits employ silicon technology. A smart sensor is made with the same technology as integrated circuits. A smart sensor utilizes the transduction properties of one class of materials and electronic
properties of silicon (GaAs). A transduction element either includes thin metal films, zinc oxide and polymeric films. Integrating electronics circuits on the sensor chip makes it possible to have single chip solution. Integrated sensors provide significant advantages in terms of overall size and the ability to use small signals from the transduction element. The IC industry will get involved in smart sensor if a very large market can be captured and the production of smart sensor does not require non-standard processing steps.
3.1 Signal conversion effects
We know that silicon shows a suitable physical signal conversion effect. Many of the physical effects of silicon can be used in making sensors. Based on these effects, different types of sensors can be constructed which can be used for measuring different physical and chemical measurand.
Table1 below shows how different non electrical signal in which we can classify different measurand and Table 2 shows the physical effects for sensors in silicon.


One problem with silicon is that its sensitivities to strain, light and magnetic field show a large crosssensitivity to temperature. When it is not possible to have silicon with proper effect, it is possible to deposit layers of materials with desired sensitivity on the top of a silicon substrate. Thus we can have a magnetic field sensor by depositing Ni-Fe layer on the top of a silicon substrate.
3.2 Different Silicon Sensors Employing Above Effects
Radiant Signal Domain
Silicon can be used to construct a sensor for sensing wide range of radiant signal from gamma rays to infrared. Silicon can be used for the fabrication of photoconductors, photodiode, and phototransistor or to detect nuclear radiation.
Mechanical Signal Domain
Silicon can be used for measuring force and pressure because of the piezo resistance effect. This effect is large because the average mobility of electrons and holes in silicon is strongly affected by the application of strain. Silicon can also be used for the measurement of air or gas velocities. If we slightly heat a silicon structure having two temperature measuring devices, and is brought into airflow then the resulting a temperature difference is proportional to the square root of the flow velocity. Combining a piezo resistor, diffused in a cantilevered beam or a piezoelectric layer with silicon can make a miniature accelerometer. By photoelectric principle one can find angular position by employing two photodiodes (i.e. one for X co-ordinate and other for Y).
Thermal Signal Domain
We know that all electron devices in silicon show temperature dependence, this property of silicon can be used for the measurement of temperature. This can be achieved by using two bipolar transistors with a constant ratio of emitter current. Another way of measuring temperature is to integrate thermocouples consisting of evaporated aluminium films and diffused p-type and n-type layers. This is possible because Seebeck in silicon is very large.
Magnetic Signal Domain
Silicon is a non “magnetic material but it can be used for the construction of Hall plates and transistor structures that are sensitive to magnetic fields. These sensors are constructed by depositing a thin magnetic Ni-Fe film on top of silicon chip that also contains electronic circuits.
Chemical Signal Domain
The demand for the better process control for bio-medical, automotive and environmental applications has encouraged many laboratories to undertake silicon chemical sensor. The ion-sensitive FET (ISFET) is best suitable for such application. When an ISFET with properly chosen ion-sensitive gate insulators is immersed in an electrolyte,the change of the drain current is a measure of the concentration of the ions or the pH.Chemical sensors can be used as humidity sensor or gas sensor.
3.3 Suitable Silicon Processing Circuit Using Silicon
The silicon sensor can produce output as voltage, current, resistance or capacitance, output format can be analog or digital. Suitable signal conditioning circuits along with processor can easily designed using silicon technology.
4. Importance and Adoption of Smart Sensor
The presence of controller/processor in smart sensor has led to corrections for different undesirable sensor characteristics which include input offset and span variation, non-linearity and cross-sensitivity. As these are carried in software, no additional hardware is required and thus calibration becomes an electronic process. Thus it is possible to calibrate the batches of sensor during production without the need to remove the sensor from its current environment or test fixture.
4.1 Cost improvement
In case of smart sensor inside hardware is more complex in the sensor on the other hand it is simpler outside the sensor. Thus the cost of the sensor is in its setup, which can be reduced by reducing the effort of setup, and by removing repetitive testing.
4.2 Reduced cost of bulk cables and connectors
Use of smart sensor has significantly reduced the cost of bulk cables and connectors needed to connect different blocks (i.e. electronic circuits).
4.3 Remote Diagnostics
Due to the existence of the processor with in the package, it is possible to have digital communication via a standard bus and a built in self-test (BIST). This is very helpful in production test of integrated circuits. This diagnostic can be a set of rules based program running in the sensor.
4.4 Enhancement of application
Smart sensor also enhances the following applications:
Self calibration
Computation
Communication
Multisensing
Self calibration:
Self-calibration means adjusting some parameter of sensor during fabrication, this can be either gain or offset or both. Self-calibration is to adjust the deviation of the output of sensor from the desired value when the input is at minimum or it can be an initial adjustment of gain. Calibration is needed because their adjustments usually change with time that needs the device to be removed and recalibrated. If it is difficult to recalibrate the units once they are in service, the manufacturer over-designs, which ensure that device, will operate within specification during its service life. These problems are solved by smart sensor as it has built in microprocessor that has the correction functions in its memory.
Computation:
Computation also allows one to obtain the average, variance and standard deviation for the set of measurements. This can easily be done using smart sensor. Computational ability allows to compensate for the environmental changes such as temperature and also to correct for changes in offset and gain
Communication:
Communication is the means of exchanging or conveying information, which can be easily accomplished by smart sensor. This is very helpful as sensor can broadcast information about its own status and measurement uncertainty.
Multisensing:
Some smart sensor also has ability to measure more than one physical or chemical variable simultaneously. A single smart sensor can measure pressure, temperature, humidity gas flow, and infrared, chemical reaction surface acoustic vapor etc.
4.5 System Reliability
System reliability is significantly improved due to the utilization of smart sensors. One is due to the reduction in system wiring and second is the ability of the sensor to diagnose its own faults and their effect.
4.6 Better Signal to Noise Ratio
The electrical output of most of the sensors is very weak and if this transmitted through long wires at lot of noise may get coupled. But by employing smart sensor this problem can be avoided.
4.7 Improvement in characteristics
Non-linearity:
Many of the sensors show some non-linearity, by using on-chip feedback systems or look up tables we can improve linearity.
Cross-sensitivity
Most of the sensors show an undesirable sensitivity to strain and temperature. Incorporating relevant sensing elements and circuits on the same chip can reduce the cross-sensitivity.
Offset:
Offset adjustment requires expensive trimming procedures and even this offsets tend to drift. This is very well reduced by sensitivity reduction method.
Parameter drift and component values:
These are functions of time. This can be solved by automatic calibration.
5. General Architecture of smart sensor:
One can easily propose a general architecture of smart sensor from its definition, functions. From the definition of smart sensor it seems that it is similar to a data acquisition system, the only difference being the presence of complete system on a single silicon chip. In addition to this it has on“chip offset and temperature compensation. A general architecture of smart sensor consists of following important components:
Sensing element/transduction element, Amplifier, Sample and hold, Analog multiplexer, Analog to digital converter (ADC), Offset and temperature compensation, Digital to analog converter (DAC), Memory, Serial communication and Processor
The generalized architecture of smart sensor is shown below:

5.1 Description of Smart Sensor Architecture
Architecture of smart sensor is shown. In the architecture shown A1, A2¦An and S/H1, S/H2¦S/Hn are the amplifiers and sample and hold circuit corresponding to different sensing element respectively. So as to get a digital form of an analog signal the analog signal is periodically sampled (its instantaneous value is acquired by circuit), and that constant value is held and is converted into a digital words. Any type of ADC must contain or proceeded by, a circuit that holds the voltage at the input to the ADC converter constant during the entire conversion time. Conversion times vary widely, from nanoseconds (for flash ADCs) to microseconds (successive approximation ADC) to hundreds of microseconds (for dual slope integrator ADCs). ADC starts conversion when it receives start of conversion signal (SOC) from the processor and after conversion is over it gives end of conversion signal to the processor. Outputs of all the sample and hold circuits are multiplexed together so that we can use a single ADC, which will reduce the cost of the chip. Offset compensation and correction comprises of an ADC for measuring a reference voltage and other for the zero. Dedicating two channels of the multiplexer and using only one ADC for whole system can avoid the addition of ADC for this. This is helpful in offset correction and zero compensation of gain due to temperature drifts of acquisition chain. In addition to this smart sensor also include internal memory so that we can store the data and program required.
6. Block Level Design Considerations for Smart Sensor
Design choice of smart sensor depends on the specific application for which the sensor is required and also related to specific industry. Normally a smart sensor will utilize inputs form one or more sensor elements either to
generate an output signal or to generate a correction signals which are applied to the primary output. This includes design of circuitry to take output of raw sensor elements and generate compensated and linearized sensor output.
6.1 Functions within electronics:
The smart sensor contains some or all of the following functions
Sensor Excitation:
Many a times it is required to alter the sensor excitation over the operating range of a sensor. An example of this is a silicon wheat stone bridge, where the drive voltage is increased with increasing temperature. This is done to compensate for the reduction in sensitivity of the piezoresistors with increase in temperature. A drive stage with temperature dependence can be used which is control by a microprocessor. This will also reduce the calibration time.
Analogue Input:
Multiplexing of inputs can be done to avoid duplication of circuit. In multiplexing inputs of same type and range are switched to a common front end. The outputs of sensors are normalized before they are switched and a variable gain stage is included after the multiplexer. This allows the sensitivity variations between the different sensors to be accounted for by a common front-end. In addition to this an offset adjustment is also included in the common front end. The variable gain stage also offers an additional advantage where the input signals are to be sampled by analog to digital converter (ADC) with fixed reference points. Under such situation gain can be increased at the lower end to increase the sensitivity.
Data Conversion:
In case of smart sensor most of the signal processing is done in digital form. This is possible only when we have an ADC along with an anti-aliasing filter. This is because most of the sensor output is in the analog form. Choice of ADC depends on the resolution, bandwidth and complexity of anti-aliasing filter.
Digital data bus interface:
The controller embedded in the smart sensor supports communications by digital data bus. The advantages of this are:
Wiring is reduced considerably
Automatic calibration at production can be simplified.
Monitoring and diagnostic functions:
In many applications self-test is required. This self-test includes connectivity checking and long-term offset correction.
Control processor:
To provide greater flexibility and reduced complexity, a control processor can be used. Control processor can do digital filtering. Another important point is software development. Processor must allow writing codes in higher language as it reduces the development time.
6.2 Level of integration:
Though it is possible to integrate smart sensor on a single piece of silicon it is unattractive due to cost and performance. Analog processing, digital logic and non-volatile memory (NVRAM), can all be done on same piece of silicon. But compromise must be made that limit the performance of at least one of these functions.
7. Summary of different smart sensors:
Some of the smart sensors developed at different research institutes are as follow:
Optical sensor:
Optical sensor is one of the examples of smart sensor, which are used for measuring exposure in cameras, optical angle encoders and optical arrays. Similar examples are load cells silicon based pressure sensors.
Infrared detector array:
Integrated sensor is the infrared detector array developed at the solid laboratory of the University of Michigan. The Infrared-sensing element was developed using polysilicon“Au thermocouples and thin film dielectric diaphragm to support the thermocouples. On-chip multiplexer was fabricated by using silicon gate MOS processing. This detector operates over a temperature range of 0 to100 degree centigrade with a 10msec response time. It has a responsiveness of 12V/W.It is a 16*2 element staggered linear array with one
lead of each detector connected to a common ground line and other connected to one of the input of 16*1 analog multiplexer. This chip also contains a separate calibration thermopile, polysilicon resistors, and diodes and MOS transistors to allow direct measurements of the cold junction temperature both and the thermoelectric power of the polysilicon lines.
Accelerometer:
Accelerometer fabricated at the IBM Research laboratory at San Jose California, which consists of the sensing element and electronics on silicon. The accelerometer itself is a metal-coated SiO2 cantilever beam that is fabricated on silicon chip where the capacitance between the beam and the substrate provides the output signal.
Integrated multisensor:
Integrated multisensor chip developed at the electronics research Laboratory University of California. This chip contains MOS devices for signal conditioning with on chip sensor, a gas flow sensor, an infrared sensing array, a chemical reaction sensor, a cantilever beam, accelerometer, surface acoustic wave vapor sensor, a tactile sensor array and an infrared charge coupled device imager. This chip was fabricated using conventional silicon planer processing, silicon micromachining and thin deposition techniques.

8. Conclusion
In conclusion, silicon is very suitable material for fabrication of smart sensors. But still a lot of research is required to get benefits of the smart sensor, but from the experience of already existing devices, we can expect that in the coming decade a large number of successful smart sensors will emerge.

9. References
¢ J. M. Giachino, Smart sensors, Sensors and actuators, 10(1986) 239-248.
¢ S. Middelhoer and A.C. Hoogerwerf, Smart sensors when and where, Sensors and Actuators, 8(1985) 39-48.
¢ M. Bowen, G. Smith, Considerations for the design of smart sensors, Sensors and Actuators, A 46-47(1995) 516-520.
¢ J.M. Riviere, D. Luttenbacher, M. Robert, J.P. Jouanet, Design of smart sensors: towards an integration of design tools, Sensors and Actuators A46-47 (1995) 509-515.
¢ J.G.Rocha, C. Couto, J.H. Corria, Smart load cells: an industrial application, Sensors and Actuators,85 (2000) 262 “266.
¢ G.D. Graaf, R.F. Wolffanbuttel, Smart optical sensor systems in CMOS for measuring light intensity and color, Sensors and Actuators, A67 (1998) 115-119.
¢ daneprairie.com

ACKNOWLEDGMENT

I express my sincere thanks to Prof. M.N Agnisarman Namboothiri (Head of the Department, Computer Science and Engineering, MESCE), Mr. Sminesh (Staff incharge) for their kind co-operation for presenting the seminars.
I also extend my sincere thanks to all other members of the faculty of Computer Science and Engineering Department and my friends for their co-operation and encouragement.
JUBIN SAM GEORGE

CONTENTS
1. INTRODUCTION
2. DEFINITION
3. USEFULNESS OF SILICON TECHNOLOGY IN SMART SENSOR
4. IMPORTANCE AND ADOPTION OF SMART SENSOR
5. GENERAL ARCHITECTURE OF SMART SENSOR:
6. BLOCK LEVEL DESIGN CONSIDERATIONS FOR SMART SENSOR
7. SUMMARY OF DIFFERENT SMART SENSORS:
8. CONCLUSION
9. REFERENCES

[attachment=1724]


ABSTRACT :
Smart sensors represent the next evolutionary tools for studying the environment..The smart environment relies first and foremost on sensory data from the real world. Sensory data comes from smart sensors of different modalities in distributed locations. Smart sensor systems are capable of prediction, interpretation,communication and intelligent interaction with the environment & hence will leverage new fault management of devices and control for distributed resources. Tremendous advances in digital signal processing and laser capabilities in recent years have enabled many new sensor developments ,one of these being smart sensors.
Fundamental research has already been carried out to develop smart sensors to monitor and control robotics, mobile vehicles, cooperative autonomous systems, mechatronics and bio-engineering systems. Emerging sensors and instrumentation technology can be exploited for enhanced research and operational capabilities. Such smart information technology manifests the potential for varied applications. It is envisioned that concepts of smart sensors and information technology can be transferred and applied to numerous system. The implementation of large networks of interconnected smart sensors can monitor and control our world. Better understanding of smart sensors perform satisfactorily in real-world conditions and can help improve efficiency and reliability.A sensor network consisting of a large number of smart sensors, enabling the collection, processing analysis and dissemination of valuable information gathered in a variety of environments is being implemented quickly .

integrating sensors and
sensor systems
INTRODUCTION
Smart 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.These 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 smart 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 these 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. A smart sensor must be able to evaluate the validity of collected data, compare it with that obtained by other sensors and confirm the accuracy
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.
Communications component of sensor systems incorporates the standardized network protocol which serves to links the distributed sensors in a coherent manner, enabling efficient communications and fault tolerance.
Integration in smart 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.
Validation of sensors is required to avoid the potential disastrous effects of the propagation of erroneous data. The incorporation of data validation into smart sensors increases the overall reliability of the system .
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.
SMART SENSOR NETWORKS :
Wireless sensor networks are potentially one of the most important technologies of this century. A sensor network is an array of sensors of diverse type interconnected by a communications network. Sensor data is shared between the sensors and used as input to a distributed estimation system which aims to extract as much relevant information from the available sensor data. The fundamental objectives for sensor networks are reliability, accuracy, flexibility, cost effectiveness and ease of deployment.
A sensor network is made up of individual multifunctional sensor nodes. The sensor node itself may be composed of various elements such as various multi-mode sensing hardware (acoustic, seismic, infrared, magnetic, chemical, imagers, microradars), embedded processor, memory, power-supply, communications device (wireless and/or wired) and location determination capabilities .
A sensor network can be described by services, data and physical layer respectively.
SIGNIFICANCE OF SENSOR NETWORK :
¢ 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.
¢ Area coverage: A distributed wireless network 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).
¢ 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.
CHALLENGES :
¢ Changing network topology:Advanced communication protocols are required to support high level services and real-time operation, adapting rapidly to extreme changes in network conditions.
¢ Resource optimization: Optimised sensor scheduling for distributed networks, through accurate determination of the required density of sensor nodes in order to minimize cost, power and network traffic loads, while ensuring network reliability and adequate sensor resolution for data accuracy.
¢ Limitations: Power, memory, processing power, life-time.
¢ Failure prone: Individual sensors are unreliable, particularly in harsh and unpredictable environments. Addressing sensor reliability can reduce the level of redundancy required for a network to operate with the same level of reliability.
¢ Network congestion resulting from dense network deployment: The quantity of data gathered may exceed the requirements of the network and so evaluation of the data and transmission of only relevant and adequate information needs the be performed.
Security is a critical factor in sensor networks, given some of the proposed applications. An effective compromise must be obtained, between the low bandwidth requirements of sensor network applications and security demands .
APPLICATIONS OF SMART SENSORS:
¢ SMART SENSOR FOR TIRE PRESSURE MONITORING:
Recent reports of accidents involving sport utility vehicles have led to tire recalls and finger-pointing at vehicle design, tire quality, tire pressure, or driver error as the underlying cause of the problem. The information must be wirelessly transmitted to the driver, typically via RF, and displayed in the cabin of the vehicle. The remote sensing module consists of a pressure sensor, a signal processor, a temperature sensor that compensates pressure variations due to temperature changes, and an RF transmitter. The system is powered by a battery with embedded intelligence that prolongs its operating life. Because battery replacement is out of the question, and replacing the entire module
is not a cost-effective option for the average car owner, most of the existing specifications require up to 10 years of battery life.
TPMS(Tire Pressure Monitoring Sensor):
The receiver can either be dedicated to TPM use or shared with other functions in the car. For instance, the receiver controller could be the existing dashboard controller or the body controller. Or the receiver itself could be shared with the remote keyless entry (RKE) system since both systems are using the same frequency range. This functional sharing feature helps with the system cost, reduces design cycle time, and makes the TPMS easier to integrate into the automobile.
TPMS SENSOR MODULE
THE OTHER APPLICATIONS INCLUDE:
¢ 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 unmanned autonomous 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 smart sensor technology to detect explosives, will overcome difficulties in detection of un-encased landmines.

CONCLUSION:
Thus we conclude that the smart sensors are cost effective, highly accurate and reliable small in size and have a varied future scope beneficial to mankind. The sensor revolution are entirely practical applications that are just coming on the market.
Reference:
WEBSITES :
sensorsetworks.com
complexibotics.com
opengis.org
businessweek.com
wikipedia.com
sensors-research.com
sensorsportal.com
sentix.org
sensorsynergy.com

DIRTFT
Do It Right The First Time

Concept:
All parameters needed for measurement and validation are “local” to the sensor
Sensor sends only high quality data to DCS for process control

Hardware
UOP Guided Wave 300P Spectrometers
Multiplexed applications
~200 analyzer years experience
Bi-directional MODBUS page link to DCS
Analyzer returns MEASUREMENT and STATUS value for control action
DCS sends commands and key process parameters


Example Applications
Measurement Validation
Adaptive Sensor
Auto Calibration Maintenance
Measurement Validation

Sensor validates EACH measurement by combining:
Calibration Model Statistics
Outlier detection, fit parameters . . .
Hardware parameters
System voltages, scan time, detector noise . . .
Process (environmental) Data
Pressures, temperatures, flows . . .

For more information about this article,please follow the link:
http://googleurl?sa=t&source=web&cd=2&ve...ensors.ppt&ei=-fm7TI6hOYemcJ79hO0M&usg=AFQjCNFiijSIaai9urconCWmvogvWouYpw

Presented By
Shaistha Jabeen

---

[attachment=10666]
INTRODUCTION
A sensor that includes a microprocessor that conditions the signals before transmission to the control network.
 It filters out unwanted noise and compensates for errors before sending the data.
 Some sensors can be custom programmed to poduce alerts on their own when critical needs are reached.
 Smart sensors are an extension of traditional sensors to those with advanced learning and adaptation capabilities.
 These are sensors with integrated electronics that can do one or more functions such as making decisions, 2way communications.
There is one more form of smart sensor called smart plug and play transducer electronic data sheet (TEDS) sensors.
 This iteration adds a limited amount of smartness to a sensor by embedding the TEDS into the device in a cost effective manner.
“A Smart sensor is a sensor version of smart transducer”.
 A Smart transducer is “A transducer that provides functions beyond those necessary for generating a correct representation of a sensed or controlled quantity”.
The functions of an smart sensor system can be described as:
 Compensation: Ability of the system to detect and respond to changes in the network environment.
 Information processing : encompasses the data related processing to enhance and interpret the collected data and maximize the efficiency of the system.
 Communications: component of sensor system incorporates the standardized network protocol which serves to links the distributed sensors in a coherent manner.
 Integration: It involves the coupling of sensing and computation at the chip level.
Validation: required to avoid the potential disastrous effects of the propagation of the erroneous data.
Data fusion: techniques are required in order combine information from multiple sensors.
Smart Sensor Developer Kit
The Smart Sensor Developer Kit provides the first-ever user-configurable, active microsensor technology that can be easily and cheaply incorporated into a wide range of instruments for many applications

to get information about the topic smart sensor networks full report ,ppt and related topic please refer page link bellow

http://studentbank.in/report-bluetooth-b...ars-report

http://studentbank.in/report-bluetooth-b...ull-report

http://studentbank.in/report-bluetooth-b...orks--2318

http://studentbank.in/report-smart-senso...wth--28144

http://studentbank.in/report-smart-sensors-full-report

http://studentbank.in/report-smart-senso...port--8962

http://studentbank.in/report-bio-smart-sensors

---