INTELLIGENT WIRELESS VIDEO CAMERA

CHAPTER-I INTRODUCTION1.1 INTRODUCTION TO TOPICWireless security camerasare closed-circuit television(CCTV)camerasthat transmit a video and audio signal to a wireless receiver through a radio band. Many wireless security cameras require at least one cable or wire for power; "wireless" refers to the transmission of video/audio. However, some wireless security cameras are battery-powered, making the cameras truly wireless from top to bottom.

Wireless cameras are proving very popular among modern security consumers due to their low installation costs (there is no need to run expensive video extension cables) and flexible mounting options; wireless cameras can be mounted/installed in locations previously unavailable to standard wired cameras. In addition to the ease of use and convenience of access, wireless security camera allows users to leverage broadband wireless internet to provide seamlessvideo streaming over-internet.Wireless security cameras are becoming more and more popular in the consumer market, being a cost-effective way to have a comprehensive surveillance system installed in a home or business for an often less expensive price. Wireless cameras are also ideal for people renting homes or apartments. Since there is no need to run video extension cables through walls or ceilings (from the camera to the receiver or recording device) one does not need approval of a landlord to install a wireless security camera system. Additionally, the lack of wiring allows for less "clutter," avoiding damage to the look of a building.A wireless security camera is also a great option for seasonal monitoring and surveillance. For example, one can observe a pool or patio in summer months and take down the camera in the winter.

1.2 LITERATURE SURVEY

The intelligentwireless videocamera describedin thisis designedusing wireless video monitoring system, for detecting the presence of a person who is inside the restrictedzone.This type ofautomatic wirelessvideo monitors is quite suitable for the isolated restricted zones, where the tight security is required the principle of remote sensing is utilized in this, to detect the presence.

Any personwhoisveryneartoreferencepointwithinthezone.Avideocameracollectsthe images fromthe reference points andthen converts into electronicsignals.The collected images are converted from visible light into invisible electronic signals inside a solid-stateimager.These signals are transmitted to the monitor. In thisforthe demonstrationpurpose three referencepoints aretaken.Each reference point is arranged with two infrared LEDs and one lamp.This arrangement is madeto detectthe presence ofa personwho isnear thereferencepoint.The reference point is nothing but restricted area, when any person comes near to any reference point, then immediately that particular reference point output will become high andthis highsignal isfed tothe computer.1.3 MISSION OBJECTIVES

The computer energies the that particular reference pointlamp androtatesthe videocameratowardsthatreferencepointforcollectingtheimagesatthatparticularreferencepoint.Torotatethevideo camera towards interrupted reference point, stepper motor is used. The present wireless video camera described in this is designed using wireless video monitoring system, for detecting the presence of a person who is inside the restricted zone.This type of automatic wireless video monitors is quite suitable for the isolated restricted zones, where the tight security is required. Once upon atime much importanceis notgiven forthe securitysystem.But as we see today lot of terrorism has grown up across the country and need has aroused to develop different types of security systems for various applications to safe guard the zones of various types like, military zones, railway yards, scrap yards, borders etc., this kind of automatic video monitoring systems can be installed at indo-pak borders, where the terrorists arecrossing borders.In factour countryis spendinglot ofits revenueto safe guard the borders. CHAPTER-II

APPROACHES AND METHODS

Although these tools might be useful for local damage detection and analysis but they are not suitable for a global monitoring system. To implement a SHM successfully, we would like to propose monitoring the civil structures globally as well as locally, using sensors and cameras, and finally detect any abnormal behavior in real time to mitigate any catastrophic failures. Of course, this monitoring system has excellent potential to improve the regular operation and maintenance of the structures.

Comprehensive systems use a large number of sensors to monitor structures health. Typically these systems comprise of vibration sensors, strain gauges and other similar sensors that are wired through cables to a central data acquisition system or a network of data acquisition systems. The data acquisition systems not only record all the data but also facilitate data interpretation. Problems with these systems include high initial cost, large complicated mesh of cables, difficult installation and maintenance, etc. SHM can benefit from the recent research and development in the area of Micro-Electro-Mechanical System (MEMS) and Wireless Sensor Network (WSN). MEMS technology has greatly improved the miniaturized sensors of different types including the accelerometers and tilt-meters that are extensively used in civil SHM. These new sensors consume lesser energy, are relatively inexpensive and have been shown to give promising responses when compared with the conventional wired sensors. A WSN is composed of a large number of small nodes that have sensing, processing and wireless communication capabilities. Use of WSN for SHM gives us many advantages such as:

System setup & maintenance cost is remarkably reduced.

No cables are required for data transfer because the communication is wireless.

Dataprocessing &interpretation canbe distributed across the network nodes.

System becomes more fault-tolerant. In case of a partial system failure the rest of the system is capable of performing its task independently.

Overall system response time improves due to anomaly detection through data processing on the nodes instead of central base station.

WSN based framework does not come only with the advantages, there are some limitations that have to be considered and further evaluated.

Processing power & communication bandwidth available on the nodes are very limited.

Each node has restricted battery life that has to be preserved by efficient consumption.

A multi-hop protocol is to be established for communicating with the central base station.

Along with these, there are some other drawbacks in the use of WSN based system for SHM applications. The research continues to address these problems and to make WSN based solution more robust and reliable for this domain. The system has to perform the regular sensing operation not only under the normal circumstances but also during extreme weather and environmental conditions as well as after hazards occur. Another very important consideration is

the maintenance cycle span and overall life of the system. In this paper, we propose the basic characteristics of an efficient framework that can be employed to perform SHM for bridges. We use a WSN (MICA motes) that exploits the distributed processing available on its nodes. Distributed data interpretationis used to intelligently detect local data trends (events) instead of just sending raw data to the base station in a dumb fashion. Examples of these events are normal

Vibration detected, abnormal vibration detected or large structure tilt detected. Using this technique we can also reduce network traffic and improve the battery life of the WSN. When a critical event is detected on the bridge, we can prompt a video camera to pan/tilt/zoom into the local area and monitor activity. Camera is attached to a video processing module that can provide video frames of the area of interest, synchronized with other sensor data. This way we can improve the conventional SHM system utilizing new technology. This framework has been tested in the laboratory environment.

The area of WSN has greatly benefited from the research carried out towards Smart Dust by Pisteretal. . The application of WSN has been shown in many different areas. Due to the limitation of the resources available on the WSN nodes, only the applications that have low sampling rates and energy requirements have been reported. The examples of these applications are habitat monitoring, product quality monitoring through atmosphere control. On the other hand, the data rate is much higher in SHM application. Some researchers have developed new platforms to overcome the limitations of sensor node hardware .

In the area of WSN, researchers have been working on the services that are required in most of the applications. Time synchronization between the nodes of the network is one such service. Data from different sensor nodes can be accurately synchronized if a good time synchronization service is functional. Ganeriwaletal. Propose a hierarchical approach for

time synchronization among nodes. It is called Timing-sync Protocol for Sensor Networks (TPSN). Similarly, there are other key issues related to system deployment and operation. These are discussed in . The research community in SHM has been in the process of evaluating and exploring the features of monitoring systems with WSN. We are mainly interested in addressing real life problems using novel technologies within context of a complete SHM system as discussed in .

As part of this effort, first, we would like to address the issue of efficiently handling large amount of vibration data on resource-constrained WSN. In this paper, we propose techniques that improve data traffic on the network. At the same time, we emphasize the usefulness of the proposed framework for interpreting data to trigger certain events related to the data trend. Another novel contribution of this study will be the integration of video processing unit that enhances the capability of the SHM. Autonomous camera control and data synchronization between the video and sensor data are the key features of our framework.

For testing and evaluation, MICA series motes have been used for this study. This platform is equipped with Atmels microcontroller running on 8MHz, 868/916 MHz multi-channel RF transceiver with effective data rate of 4-10Kbps, 512KB of serial flash memory for data storage and can run up to one year on AA Batteries (using sleep modes). A sensor board, with different sensors, is attached to the main mote. Acceleration and temperature sensors are used in this framework. Tiny OS, a component based operating system, runs on motes. Information about these motes shows that they have limited computing resources available. Motes build a network and send data to the base station. It records this data into a local data store and also transmits to a remote observatory through wireless internet access. Fig: 2.1 Apparatus deployment on the bridgeThe video cameras are used to monitor the traffic and other activities on the bridge from a remote observatory. The set of cameras should be deployed so that using pan/tilt/zoom complete area of the bridge can be covered. For simplification, we have used only one camera in our experiments in the lab. A standard PC was used as the video processing unit. It can send & receive messages from the WSN nodes on the bridge. These messages are routed through the base station of the WSN. This is further explained.

2.1 NETWORK ARCHITECTURE The network of motes is organized into a group of clusters. Each cluster has one functional Gateway Mote (GM) at a given time and about 4-8 Sensing Mote (SM). Responsibility of a GM is to coordinate the communication between the SMs and base station. At the same time, GM is responsible for passing the messages between the GMs of neighboring cluster, thus completing the route to the base station. Certainly the GMs will have to cope up with the network traffic from within the cluster and from the other neighboring clusters. This means that the GM should have higher networkbandwidth.Consideringthe current availability of the hardware, we suggest the use of MICAZ Motes. They have 2.4GHz radio transceiver giving network bandwidth nearly 6 times that of MICA2 Motes. The top view of suggested WSN layout. The dotted line shows the cluster boundary and arrows show the direction of data flow. Backup Motes are available for redundancy in case of a node failure. The backup mote normally stays in sleep mode but periodically sends a heartbeat message to the GMs in range and confirms their availability. In case of an absent GM, it takes its responsibilities in the cluster and tries to continue the normal operations.

Fig: 2.2 Network architecture on the bridge2.2 SYSTEM OPERATION There are some essential services that have to be incorporated for a WSN to operate in a meaningful fashion. We cover sensor localization and time synchronization here. GM occupies a critical position in the network as explained in the previous section. Localization is carried out through a handshaking protocol. In the first phase, base station initiates a localization message for the GMs, which propagate through the GMs and each one of them establishes an eighbor-list. Since this message is initiated from base station, each GM knows which neighbors to use for

reaching base station.

In the second phase, GMs build up their local SM-list through a similar handshake with SMs Every GM uses an assigned time-slice for this phase. This ensures that an SM lying in area of coverage of more than one GMs only gets first cluster assignment. But this SM can store a list of GMs that are in communication range, which will help in the future in case of primary GM failure. Later during normal operation of WSN some other housekeeping messages are periodically transferred which help in both time synchronization

and sensor localization. Time synchronization is one of the critical Services of a WSN when it is being used for SHM. The data from different sensors has to be synchronized precisely. A scheme proposed by Ganeriwaletal works fairly well for this application.

2.3 EVENT DETECTIONWSN can start sensing operation once the initial setup services have completed. The sampling rate of the temperature data is very low and only one mote from a cluster has to send the temperature once after every 5 minutes. On the other hand, the accelerometer measures vibration and can also be used for tilt detection. The sampling frequency on accelerometer may be over 100Hz for SHM applications depending on the frequency band of interest. If all the SMs in the network sense simultaneously at this rate, we will have too large a data to handle efficiently. We propose an event detection technique that also helps in reducing unnecessary network traffic. We assume that vibration data is unnecessary if the activity metric value is below a low threshold (TL) value. We have used mean shift vector as the activity metric. M ( ) = 1 / nx ( xi o)

assumed that we are not interested in any vibration readings below TL, therefore, we can reduce the sampling frequency but we do not want to go too low so as to miss high frequency data in the start. We have used values close to 80Hz for the Passive Mode sampling frequency. Once a SENSE_EVENT has been detected by the sensing SM in a cluster, it sends the message in its cluster to the other SMs to switch to wakeup mode and start sensing at a higher frequency (we used 150Hz).All the SMs in that cluster switch to Active Mode now. They remain in Active Mode for a limited time but before going back to Passive Mode the mean shift value will be checked and the decision will be taken accordingly. During the Active Mode SMs use local flash to store the data and start sending the data to the local GM during the allotted time slice. Figure 3 shows single and two mote case of SENS_EVENT and change in mode is depicted through color coding. Where ois initial mean value, nxs t e number data posandint axis one data point. In our of experiments, we have used 15 for nx and 10 for TL but these are parameters that can be manually tweaked tos uite noise conditions or user preference. The values can also be changed on run-time throughout the network. According to the tests, this metric performs reasonably well in the lab setup the vibration data from one sensor. Using above mentioned method we detect two events in the vibration data.

First one is normal vibration detected. SENSE_EVENT label is assigned to this event, which is triggered when mean shift vector magnitude is higher threshold value of TL.

Second one is abnormal vibration detected. CAMERA_EVENT label is used for this event,

Shows that mean shift value is higher than another threshold TH. We have used TH = 25 in our

experiments. During the normal mode of operation, with no vibrations in a cluster, the SMs are in Passive Mode. Only one SM is performing sensing and others are in sleep mode. This SM uses a lower sampling frequency for conservation of the battery life. Assumed that we are not interested in any vibration readings below TL, therefore, we can reduce the sampling frequency but we do not want to go too low so as to miss high frequency data in the start. We have used values close to 80Hz for the Passive Mode sampling frequency.

Once a SENSE_EVENT has been detected by the sensing SM in a cluster, it sends the message in its cluster to the other SMs to switch to wakeup mode and start sensing at a higher frequency (we used 150Hz).All the SMs in that cluster switch to Active Mode

now. They remain in Active Mode for a limited time but before going back to Passive Mode the mean shift value will be checked and the decision will be taken accordingly. During the Active Mode SMs use local flash to store the data and start sending the data to the local GM during the allotted time slice. Single and two mote cases of SENS_EVENT and change in mode is depicted through color coding.

Fig:2.3 Vibration detection of Single mote data

Fig: 2.4 Two time synchronized motes

CAMERA_EVENT is triggered when the activity Metric is higher than some threshold value TH. It means that there is significant vibration in that section,

Therefore, it should be visually observed. This event message is passed to the base station through the intermediate GMs, then the base station uses the location information about the sending mote and requestscamerato pan/tilt/zoom into the corresponding section of the bridge. A controller at a remote location can look at the live video if he/she wishes; otherwise the video clip related to an activity can be recorded into the database. Since we have the data about the SM that triggered this event, we can synchronize this data with the video frames. Figure above shows a camera overlooking the structure being used in the lab. Fig: 2.5 Lab setup2.4 OPTIMIZATION TECHNIQUESTo improve the performance of the WSN, we propose some optimizations. To uniformly use the resources within a cluster ,we switch the passive sensing responsibility

among the local SMs in a round-robin manner.

To reduce the amount of data being transmitted, a packet of data gets a common time-stamp instead of individual ones for each data values. Timestamp for individual data values can be recovered on the base station using the current sampling frequency information.

Each value of accelerometer data is in 10-bit digital form. By default the Tiny OS component returns this data in a 16-bit variable and about 37.5% of the bit-space is wasted. We recover this bit space through data packing on the sending and then unpacking on the receiving end.Experiments show that effective distributed processing can enhance the effectiveness of the SHM system. To further enhance the efficiency of this system domain specific data compression on the nodes could be helpful. Many factors affect the performance of a SHM system. This includes environmental conditions, which have to be observed in a real-life outdoor scenario over a longer period of time. It also remains to be seen that how well these algorithms perform in real-world situation with problems like node failures, complex civil structures, extreme weather conditions etc. Only then a complete transition from the conventional SHM to the WSN based SHM can be made.

We have tested the triggering of a video camera using event detection on mote data. Another

Wireless Video Data Acquisition System Wired Actuator possibility is doing the reverse of this, i.e. triggering sensors in an area if unusual activity is observed in the video. There is interesting work in computer vision community for activity recognition and detection of abnormal activities in videos . For example, the normal activity on a bridge is the traffic in a continuous flow but if there is a heavy truck broken down on the bridge that will be abnormal activity. This might induce heavy loading of the bridge structure which could affect its health. We will further revaluate system trigger based on real time processed streaming video data.We consider a wireless surveillance network composed of a set of stationary, battery-powered, sensor nodes. For the sake of simplicity, we assume that the area under observation is an orthogonal surface with respect to the plane where sensors are located. All sensors are alike, each of which is equipped with a camera, a low-power microprocessor, and a radiofrequency (RF) circuitry that allows sensors to communicate over the wireless channel. Data observed by the sensors are collected at a node, denoted by , which is either a central controller or a gateway to the xed network.

Let denote the output power necessary for transmitting one information bit and denote the power consumption of the RF circuitry per bit, which is the same in transmit mode as in receive mode. Let a be the battery capacity, expressed as Ah, that is initially available at each sensor, and let be the maximum power that can be drained from the battery. In our network scenario, all sensors have same processing capability nyand initial energy resources. The system characteristics and parameters values are presented .We assume that the network architecture is as shown below where the highest layer of the architecture is rep- resented by node . Nodes at the lowest layer, indicated by (seconds) and then, between two consecutive reference frames, transmit only differences (if any) between the current acquired frame and the reference one. In this way, since the scene should be still unless an anomaly occurs, no energy consuming motion estimation is performed, whereas temporal redundancy is cap- tured. Also, since scene changes are supposed to occur rarely, very few refresh data have to be transmitted allowing for a very are in charge of monitoring the area of inter- high compression ratio.est and are positioned along a line in such a way that the target In order to select a suitable encoder for the reference im- area is fully covered. Each of the sensors observes a portion of ages, we have considered several existing image compression of the target area and forwards the video signal towards the screen.Assume that sensors camera produce grayscale QCIF (Quarter Common Intermediate Format) images (i.e., 144 lines of 176 pixels each), which are fragmented at the link layer into Protocol Data Units (PDUs) and transmitted over the wireless link. The radio channel is modeled as a Gilbert channel , with two states, good and bad, that represent the channel conditions during the transmission time of one PDU. The average data unit error probability is denoted by 2 and is taken as a variable of the system, while the average burst of error over the channel is set to 5.

We consider an ARQ scheme implemented at the link layer, so that the sender transmits a PDU until either the PDU is correctly received or a maximum number of transmission attempts is reached. We denote the maximum number of transmissions per PDU by 34. A PDU is discarded if it is not successfully transmitted within attempts. In the following, we assume

that a non-compressed image is lost if more than 5PDUs are discarded, while a compressed image is lost as soon as one PDU is discarded. We denote the image loss probability by 6.

The effect of PDU losses on the uncompressed and compressed data is discussed in the section

In the WVN application, an important issue is the selection of the coding framework that best matches the characteristics of the video-surveillance data and the energy constraints of the application. The most popular video coders dened by the ISO (International Standardization Organization) and ITU (Inter- national Telecommunication Union), i.e., MPEG-2, MPEG-4,

H.261, H.263, and the novel H.26L, are usually referred to as hybrid motion compensated coders, since they perform motion estimation in order to capture temporal redundancy by follow-ing object motion within the scene. This approach is known to yield very good results but is also very time-consuming, especially in the motion estimation stage. On the other hand, it is worth noticing that, unlike typical multimedia video, surveillance video sequences are often characterized by low motion, in particular when no object is expected to move within the scene but in case of anomalies. Thus, such coders do not provide a favorable energy/performance trade-off in the WVN application.

In the context of surveillance of a low-motion scene, the following approach can be pursued. Each sensor can periodically encode and transmit a reference frame (e.g., one scene every 15 seconds) and then, between two consecutive reference frames, transmit only differences (if any) between the current acquired frame and the reference one. In this way, since the scene should be still unless an anomaly occurs, no energy consuming motion estimation is performed, whereas temporal redundancy is captured. Also, since scene changes are supposed to occur rarely, very few refresh data have to be transmitted allowing for a very high compression ratio.In order to select a suitable encoder for the reference images, we have considered several existing image compression algorithms, including JPEG and JPEG2000, and we have evaluated the trade-off that they provide between energy consumption and compression ratio. We have employed a simulator of the ARM architecture, along with an accurate energy estimation tool for the Strong Arm 1110 processor, in order to characterize the energy cost of each algorithm. We have found that, in general, algorithms that employ a oating point computational engine, such as JPEG2000 and JPEG with the oating-point discrete cosine transform (DCT) kernel, exhibit a very high energy consumption. Thus, their actual usefulness for the WVN application is fairly questionable. On the other hand, when xed-point kernels are used, as in JPEG with the integerkernel, the energy cost becomes as low as to provide a favor-able energy-compression trade-off. As an example, encoding a grayscale QCIF image at 1 bit-per-pixel, employing JPEG withobtained by using video compression. We consider that, in order to provide a good image quality, 6has to be below @XWG.By xing the desired 6and looking at the plots .wecan obtain the required value of 3as the error probability, 2,changes. Clearly, the image loss probability decreases with the increasing of 34and the decreasing of 2; and, in the case of uncompressed images, a lower higher values of 5. Figure above shows the average energy consumption per success fully transmitted image, as 3 and the error probability over the wireless channel change. Both the cases of uncompressed and compressed images are considered. In the case of compressed data, energy consumption includes the energy spent for communication as well as for compression. As shown in the plots, for low values of 3, we can reduce energy consumption by increasing the number of transmission attempts. In fact, by increasing, the probability that a the probability that a PDU is discarded decreases, and, hence, the number of successfully transmitted images grows. For higher values of 3, energy consumption does not further decrease by increasing 34. This suggests that, once the desired value of 6 is achieved, there is no reason to increase 3 beyond the value that guarantees the minimum energy consumption per image. Finally, the plots in below show consumption is obtained when images are compressed.The term surveillance is often used for all forms of observation or monitoring, not just visual observation. Surveillance is the monitoring of the behaviour, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting. The word surveillance is commonly used to describe observation from a distanceby means of equipment (such as CCTV cameras), or interception of electronically transmitted information (such as Internet traffic or phone calls). However, surveillance can also refer to simple, relatively no- or low- technology methods such as human intelligence agents and postal interception. Surveillance is very useful to governments and law enforcement to maintain social control, recognize and monitor threats and also in the scientific research and technology. Fig:2.6 SurvellianceSystem

Fig:2.7 Aerial Surveillance

There are many types of surveillance and can be used in many feilds like Computer Surveillance, Biometric Surveillance, Aerial surveillance, in Data mining and profiling, Human operatives, Satellite Imagery. Surveillance in day to day life help to create saftey and security and also helps to maintain peace. Using the idea of surveillance it can very helpful in scientific and research, using the surveillance based robot it is helpful in mining and research as it works on the environment un favourable for humans.

Using a wireless camera to view the objects and also the obstacles in the way. The wireless camera selected for this project is IP robo cam 21. This wireless camera is placed on the vehicle and for its movement stepper motor is being used and the stepper motor is controlled via PC. IP 6 robo cam 21 is used to monitor and protect anywhere via network or internet connection. It is a colour security camera with high sensitivity for perfect images at low light conditions. This camera can be operated remotely, as it has the nature of panning, tilting, zooming, and adjusts the resolution. IP robo cam 21 is the powerful, user friendly ultra veiw software by which one can watch and operate camera from anywhere in the world. It can be used both wired and wirelessly.

In radio terminology, a transceiver means a unit which contains both a receiver and a transmitter. From the beginning days of radio the receiver and transmitter were separate units and remained 9 so until around 1920. Amateur radio or "ham" radio operators can build their own equipment and it is now easier to design and build a simple unit containing both of the functions: transmitting and receiving. Almost every modern amateur radio equipment is now a transceiver but there is an active market for pure radio receivers, mainly for shortwave listening (SWL) operators. An example of a transceiver would be a walkie-talkie.

RF is the wireless transmission of data by digital radio signals at a particular frequency. It maintains a two-way, online radio connection between a mobile terminal and the host computer. The mobile terminal, which can be portable, even worn by the worker, or mounted on a forklift truck, collects and displays data at the point of activity. The host computer can be a PC, a minicomputer or a much larger mainframe. The advantages of a RF communication system are many. Start with the simple fact that if it is wireless, it don't have to lay cable. Cable is expensive, less flexible than RF coverage and is prone to damage. For new facilities, implementing a wireless infrastructure may be more cost effective than running cable through industrial environments, especially if the space configuration may change to support different storage space allocation or flexible manufacturing stations.

A radio system transmits information to the transmitter. The information is transmitted through an antenna which converts the RF signal into an electromagnetic wave. The transmission medium for electromagnetic wave propagation is free space. The electromagnetic wave is intercepted by the receiving antenna which converts it back to an RF signal. Ideally, this RF signal is the same as that originally generated by the transmitter. The original information is then demodulated back to its original form. Other general advantages of real-time RF communication include a significant improvement in order accuracy (>99%), the elimination of paperwork, replacement of time-consuming batch processing by rapid real-time data processing, prompt response times and improved service levels. Complementing a real-time data collection system with automated data entry by bar code scanning or another automatic data collection technology improves the accuracy of information and eliminates the need for redundant data entry, which provides another set of time- and cost-saving advantages.

2.5 STEPPER MOTOR

A stepper motor (or step motor) is a brushless, electric motor that can divide a full rotation into a large number of steps. The motors position can be controlled precisely without any feedback mechanism. Stepper motor are similar to switched reluctance motor (which are very large stepping motors with a reduced pole count, and generally are closed-loop commutated). Stepper motors have multiple toothed electromagnets arranged around a central gear-shaped piece of iron. The electromagnets are energized by an external cotrol circut, such as microcontroller. To make the motor shaft turn, first electromagnet is given power, which makes the gears teeth magnetically attracted to the electromagnets teeth. When the gear's teeth are thus aligned to the first electromagnet, they are slightly offset from the next electromagnet. So when the next electromagnet is turned on and the first is turned off, the gear rotates slightly to align with the next one, and from there the process is repeated. Each of those slight rotations is called a "step", with an integer number of steps making a full rotation. In that way, the motor can be turned by a precise angle. Stepper motor characteristics a. Stepper motors are constant power devices. b. As motor speed increases, torque decreases. (most motors exhibit maximum torque when stationary, however the torque of a motor when stationary (holding torque) defines the ability of the motor to maintain a desired position while under external load). c. The torque curve may be extended by using current limiting drivers and increasing the driving voltage (sometimes referred to as a 'chopper' circuit; there are several off the shelf driver chips capable of doing this in a simple manner).A stepper motor is an electromechanical device which converts electrical pulses into discrete mechanical movements. The shaft or spindle of a stepper motor rotates in discrete step increments when electrical command pulses are applied to it in the proper sequence.

. Fig: 2.8 Cross section of a variable reluctance Motor

The motors rotation has several direct relationships to these applied Input pulses. The sequence of the applied pulses is directly related to the direction of motor shafts rotation. The speed of the motor shafts rotation is directly related to the frequency of the input pulses and the length of rotation is directly related to the number of input pulses applied. A stepper motor is an electromechanical device which converts electrical pulses into discrete mechanical movements. The stepper motor is used for position control in applications like disk drives and robotics.

The name stepper is used because this motor rotates through a fixed angular step in response to each input current pulse received by its controller. In recent years, there has been wide-spread demand of stepping motors because of the explosive growth of computer industry.

Their popularity is due to The fact that they can be controlled directly by computers, microprocessors and programmable controllers. Stepper motors are ideally suited for situations where precise position and precise speed control are required without the use of closed-loop feedback. When a definite number of pulses are supplied, The shaft turns through a definite known angle. This fact makes the motor well suited for open-loop position control because no feedback need be taken from the output shaft.Every stepper motor has a permanent magnet rotor also known as shaft surrounded by a stator poles. The most common stepper motor s has four stator windings that are paired with a center-tapped. This type of stepper motor is commonly referred to as a four-phase stepper motor. The center tap allows a change of current direction in each of two coils when a winding is grounded, there by resulting in a polarity change of the stator.

The shaft or spindle of a stepper motor rotates in discrete step increments when electrical command pulses are applied to it in the proper sequence. The direction of the rotation is determined by the stator poles. the stator poles are determined by the current sent through the wire coils. As the polarity of the current is changed, the polarity is also changed causing the reverse motion of the motor The sequence of the applied pulses is directly related to the direction of motor shafts rotation.. The speed of the motor shafts rotation is directly related to the frequency of the input pulses and the length of rotation is directly related to the number of input pulses applied. While a conventional motor shaft moves freely, stepper motor shaft moves in a fixed repeatable increment which allows one to move it to a precise position. This repeatable fixed movement is possible as a result of basic magnetic theory where poles of he same polarity repel and opposite poles attract. The stepper motor converts digital signals into fixed mechanical increment of motion. It thereby provides a natural interface with the digital computer. It is a synchronous motor such that the rotor rotates a specific incremental number of degrees for each pulse input given to the motor system. These motors can provide accurate positioning without the need of position feedback sensors when compared to other motors. The position is known simply by keeping track of the input step pulses. Usually, position information can be obtained simply by keeping count of the pulses sent to the motor thereby eliminating the need of expensive position sensors and feedback controls. Stepper motors are rated by the torque they produce, step angle, steps per second and the number of teeth on rotor. The minimum degree of rotation with which the stepper motor turns for a single pulse if supply to one wire or a pair is called step angle. The minimum step angle is always a function of the number of teeth on rotor .i.e., the smaller the step angle the more teeth the rotor possess.

Stepspercompleterevolution = no.ofphases(coils) x No.ofteethonrotor Smaller the step angle, greater the number of steps per revolution and higher the resolution or the accuracy of positioning obtained. The step angles can be as small as 0.72 or as large as 90. The motor speed is measured in steps per second. Steps per second = (Revolution per minute x steps per Revolution)/ 60

Stepping motors has the extraordinary ability to operate at very high speeds and yet to remain fully in synchronism with the command pulses, when the pulse rate is high, the shaft rotation seems continuous. If the stepping rate is increased too quickly, the motor loses synchronism and stops. Stepper motors are designed to operate for long periods with the rotor held in a fixed position and with rated current flowing in the stator windings whereas for most of the other motors, this results in collapse of back emf and a very high current which can lead to a quick burn out.

A stepper motor is a special kind of motor that moves in individual steps which are usually .9 degrees each. Each step is controlled by energizing coils inside the motor causing the shaft to move to the next position. Turning these coils on and off in sequence will cause the motor to rotate forward or reverse. The time delay between each step determines the motor's speed. Steppers can be moved to any desired position reliably by sending them the proper number of step pulses. A stepper motor's shaft has permanent magnets attached to it. Around the body of the motor is a series of coils that create a magnetic field that interacts with the permanent magnets. When these coils are turned on and off the magnetic field cause the rotor to move. As the coils are turned on and off in sequence the motor will rotate forward or reverse.

A motor is a machine which converts electric energy into mechanical energy. Its action is based on the principle that when a current carrying conductor is placed in a magnetic field, it experiences a mechanical force whose direction is given by Flemings left hand rule.

CHAPTER III CONCLUSION

Experiments show that effective distributed processing can enhance the effectiveness of the SHM system. To further enhance the efficiency of this system domain specific data compression on the nodes could be helpful. Many factors affect the performance of a SHM system. This includes environmental conditions, which have to be observed in a real-life outdoor scenario over a longer period of time. It also remains to be seen that how well these algorithms perform in real-world situation with problems like node failures, complex civil structures, extreme weather conditions etc. Only then a complete transition from the conventional SHM to the WSN based SHM can be made.

The literature survey and software simulation of the project for this semester is completed. The final product of the project will be done on a PCB board and will be demonstrated with fully functioning mechanical part. The final product is supposed to represent thedesignconceptofourproduct.Thesameconceptcanbeutilizedtoconstructothersurveliencecarsasamassproduction fordetection aswell asscientific researchpurposes.However, theidea always hasa lot of rooms to make enhancements in the future and the defects can be corrected to make the idea to be utilized inreal practice. We anticipate a successful completion. CHAPTER IV BIBILOGRAPHY

REFERENCES1. Crossbow Technology Inc. (http://www.xbow.com)2. Wireless Vineyard (www.intel.com/labs/featur0es/ rs01031.html)3. A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J Anderson, WSNs for habitat

monitoring, WSNA '02, ACM, 2002. 4. Straser, E. G. and A. S. Kiremidjian, A modular Wireless Damage Monitoring System for Structures, Report #128, John A. Blume Earthquake Engineering Center, 1998SVES5

ECE Dept.