Saturday, 17 September 2011

Wi-Fi Technology paper presentation



                               Wi-Fi Technology

Introduction:

              Electrical technology has been greatly enhance  with the introduction of  wireless communication. Wi-Fi is the one among the achievements. The term Wifi stands for Wireless fidelity.
Wi-Fi is a brand originally licensed by the Wi-Fi Alliance to describe the underlying technology of wireless local area networks (WLAN) based on the IEEE 802.11 specifications. It was developed to be used for mobile computing devices, such as laptops, in LANs, Internet and VoIP phone access, gaming, and basic connectivity of consumer electronics such as televisions and DVD players, or digital cameras and used by cars in highways.
                    A person with a Wi-Fi enabled device such as a computer, cell phone or PDA can connect to the Internet when in proximity of an access point. The region covered by one or several access points is called a hotspot. Hotspots can range from a single room to many square  kilometres of  overlapping hotspots. Wi-Fi can also be used to create a mesh network and allows connectivity in peer- to- peer mode. Both architectures are used in community networks.

.
                                                                            Mesh Network



Discovery:

Despite the similarity between the terms 'Wi-Fi' and 'Hi-Fi', statements reportedly made by Phil Belanger of the Wi-Fi Alliance contradict the popular conclusion  that 'Wi-Fi' stands for 'Wireless Fidelity. The precursor to Wi-Fi was invented in 1991 by NCR Corporation/AT&T (later Lucent & Agere Systems) in Nieuwegein, the Netherlands.   Vic Hayes, who was the primary inventor of Wi-Fi and has been named the  ‘father of  Wi-Fi,' was involved in designing standards such as IEEE 802.11b, 802.11a and 802.11g.


Types:

Wi-Fi infrastructure devices typically fall into 3 categores as per it’s Application, with wireless being only one of many features.
  1.)Wi-Fi At Home
 Home Wi-Fi clients come in many shapes and sizes, from stationary PCs to digital cameras. The trend today and into the future will be to enable wireless into every devices where mobility is prudent.

                              Wireless Home Network Diagram Featuring Wi-Fi Router





Wi-Fi devices are often used in home or consumer-type environments in the following manner:
• Termination of a broad band connection into a single router which services both wired and wireless clients, where cable connection can not be hooked up.
• Ad-hoc mode for client to client connections ,
• Built into non-computer devices to enable simple wireless connectivity to other devices or the Internet.
2.)Wi-Fi in Business
In Business and Industry  current Technology of  Wi-Fi  is  moving toward 'thin' Access Points, with all of the intelligence housed in a centralized network appliance; relegating individual Access Points to be simply 'dumb' radios   utilizing true mesh topologies.
3.)Wi-Fi in gaming
Some gaming consoles and hand helds make use of Wi-Fi technology to enhance the gaming experience   in local multiplayer as well as connecting to wireless networks for online game play or with separate adapter.

Technical information
Wi-Fi: How it Works
A typical Wi-Fi setup contains one or more Access Points (APs) and one or more clients. An AP broadcasts its SSID (Service Set Identifier, "Network name") via packets that are called beacons, which are usually broadcast every 100 ms. The beacons are transmitted at 1 Mbit/s, and are of relatively



might use signal strength to decide which of the two APs to make a connection to. The Wi-Fi standard leaves connection criteria and roaming totally open to the client. Since Wi-Fi transmits in the air, it has the same properties as a non-switched wired Ethernet network, and therefore collisions can occur  , which can not be   detected, and instead uses a packet exchange (RTS/CTS used for Collision Avoidance or CA) to try to avoid collisions.

Channels:
 Wi-Fi uses the spectrum near 2.4 GHz, which is standardized, although the exact frequency allocations vary slightly in different parts of the world, as does maximum permitted power, except for 802.11a,which operates at 5 GHz.. However, channel numbers are standardized by frequency throughout the world, so authorized frequencies can be identified by channel numbers. The maximum number of available channels for Wi-Fi enabled devices are:
• 13 for Europe
• 11 for North America. Only channels 1, 6, and 11 are recommended for 802.11b/g to minimize interference from adjacent channels.
• 14 for Japan.


Standard Devices
 Wireless Access Point (WAP)
A wireless access point connects a group of wireless devices to an adjacent wired LAN. An access point is similar to an ethernet  hub, relaying data between connected wireless devices in addition to a (usually) single connected wired device, most often an ethernet hub or switch, allowing wireless devices to communicate with other wired devices.

Wireless Adapter
A wireless adapter allows a device to connect to a wireless network. These adapters connect to devices using various interconnects such as PCI, USB, and PCMCIA




Wireless Router
A wireless router integrates a WAP, ethernet switch, and internal Router firmware application that provides IP Routing, NAT, and DNS forwarding through an integrated WAN interface. A wireless router allows wired and

wireless ethernet LAN devices to connect to a (usually) single WAN device such as cable modem or DSL modem. A wireless router allows all three devices (mainly the access point and router) to be configured through one central utility. This utility is most usually an integrated web server which serves web pages to wired and wireless LAN clients and often optionally to WAN clients. This utility may also be an application that is run on a desktop computer such as Apple's AirPort.





Wireless Ethernet Bridge
A wireless Ethernet bridge connects a wired network to a wireless network. This is different from an access point in the sense that an access point connects wireless devices to a wired network at the data-link layer. Two wireless bridges may be used to connect two wired networks over a wireless link, such as between two separate homes.

 Range Extender
A wireless range extender ( repeater) can extend the range of an existing wireless network. Range extenders can be strategically placed to elongate a signal area or allow for the signal area to reach around barriers such as those created in L-shaped corridors..


Antenna connectors
Most commercial devices (routers, access points, bridges, repeaters) designed for home or business environments use either RP-SMA or RP-TNC antenna connectors. Most Mini PCI wireless cards utilize Hirose U.FL connectors, but cards found in various wireless appliances contain all of the connectors listed. Many high-gain (and homebuilt) antennas utilize the Type N connector ,used by other radio communications.

 Non-Standard Devices
 DIY Range Optimizations
USB-Wi-Fi adapters, food container "Cantennas", parabolic reflectors, and many other types of self-built antennae are increasingly made according to budget and requirement.
 Long Range Wi-Fi
Recently, long range Wi-Fi kits have begun to enter the market. Companies like RadioLabs and Broadb and Xpress offer long range around 220 Km..


Increasing range in other ways
 Specialized Wi-Fi Channels
In most Standard Wi-Fi routers, the three standards, A, B and G, are enough. But in long range Wi-Fi, special technologies are used to get the most out of a Wi-Fi connection. The 802.11-2007 standard adds 10 MHz and 5 MHz OFDM modes to the 802.11a standard, and extend the time of cyclic prefix protection from 800 nS to 3.2 uS, quadrupling the multi-path distortion protection. Some commonly available 802.11a/g chipsets support the OFDM 'half-clocking' and 'quarter-clocking' that is in the 2007 standard, and 4.9 GHz and 5.0 GHz products are available with 10 MHz and 5 MHz channel bandwidths. It is likely that some 802.11n D.20 chipsets will also support 'half-clocking' for use in 10 MHz channel bandwidths, and at double the range of the 802.11n standard.
Power increase
Another way of adding range to your Wi-Fi network is by hooking a power amplifier into your existing antenna  (which can amplify  upto 5x ).
802.11N (Mimo)
802.11 N is a feature that now comes standard in many routers, this technology works by using multiple antennas to target one or more sources to increase speed.11 channels used in 802.11N 2.4GHz WiFi Frequency range used by USA and Canada.
Channel Lower Frequency Center Frequency Upper Frequency
1 2.401 2.412 2.423
2 2.404 2.417 2.428
3 2.411 2.422 2.433
4 2.416 2.427 2.438
5 2.421 2.432 2.443
6 2.426 2.437 2.448
7 2.431 2.442 2.453
8 2.436 2.447 2.458
9 2.441 2.452 2.463
10 2.451 2.457 2.468
11 2.451 2.462 2.473


Hindrance of Long range Wi-Fi
    Long range setup of Wi-Fi connection sometimes becomes fragile and volatile due to cordless phone of same frequency ,which can be rectified by installing tall antenna tower which is easily possible for hilly region and tall buildings .
Case Study
Large-scale deployments
       The Technology and Infrastructure for Emerging Regions (TIER) project at University of California at Berkeley, in collaboration with Intel, utilizes a modified Wi-Fi setup to create long distance, point-to-point links for several of its development projects in the developing world.
       This technique, dubbed Wi-Fi over Long Distance (WiLD), is used to connect to call the doctors and nurses in the  Aravind Eye Hospital with several outlying clinics in Tamil Nadu state, India.
       Another network in Ghana links the University of Ghana, Legon campus to its remote campuses at the Korle bu Medical School and the City campus; a further extension will feature links up to 80 km apart.
The world Record for Wi-Fi Range
    Microserv Computer Technologies, based in Idaho Falls, and Trango Broadband Wireless, a fixed-wireless broadband equipment maker, on August 14, 2005 set the record for the longest Wi-Fi transmission at 220.8 km. Using gear from Trango, Microserv established the wireless link between two mountaintops in Idaho using the 2.4GHz and 5.8 Ghz wireless spectrum. The link was able to transmit an FTP file transfer at the rate of 2.3 megabits per second. The equipment used was not based on standard 802.11 wireless technology, but was new experimental technology from Trango. The companies used external PacWireless 2-foot dishes to transmit the radio signals.
Advantages of Wi-Fi
• Allows LANs to be deployed without cabling, typically reducing the costs of network deployment and expansion. Spaces where cables cannot be run, such as outdoor areas and historical buildings, can host wireless LANs.


• Wi-Fi chipset pricing continues to come down, making Wi-Fi a very economical networking option and driving inclusion of Wi-Fi in an ever-widening array of devices.
• Wi-Fi is a global set of standards. Unlike cellular carriers, the same Wi-Fi client works in different countries around the world.
• Widely available in more than 250,000 public hot spots and millions of homes and corporate and university campuses worldwide.
• As of 2006, WPA and WPA2 encryption are not easily crackable if strong passwords are used .
• New protocols for Quality of Service (WMM) and power saving mechanisms (WMM Power Save) make Wi-Fi even more suitable for latency-sensitive applications (such as voice and video) and small form-factor devices
Disadvantages of Wi-Fi
•  Spectrum assignments and operational limitations are not consistent worldwide; most of Europe allows for an additional 2 channels beyond those permitted in the US (1-13 vs 1-11); Japan has one more on top of that (1-14) - and some countries, like Spain, prohibit use of the lower-numbered channels. Furthermore some countries, such as Italy, used to require a 'general authorization' for any Wi-Fi used outside an operator's own premises, or require something akin to an operator registration
•  Equivalent isotropically radiated power (EIRP) in the EU is limited to 20 dBm (0.1 W).
•  Power consumption is fairly high compared to some other standards, making battery life and heat a concern.
•  The most common wireless encryption standard, Wired Equivalent Privacy or WEP, has been shown to be breakable even when correctly configured. Wi-Fi Protected Access (WPA and WPA2) which began shipping in 2003 aims to solve this problem and is now generally available.
•  Wi-Fi Access Points typically default to an open (encryption-free) mode. Novice users benefit from a zero configuration device that works out of the box but might not intend to provide open wireless access to their LAN.
•  Many 2.4 GHz 802.11b and 802.11g Access points default to the same channel, contributing to congestion on certain channels.








 Conclusion:

        The  defect for Wi-Fi  is the  hindrance of frequency  which can be rectified by using  2 more extra channels  or Errecting tall  towers. North Eastern region of  India , say Aruchanal ,Nagaland and Mizoram where most of the  villages are on high hill tops and  at about 5 to 10 km at a distance, although which have little or no connectivity wireless options, Long Range Wi-Fi- can well be used. Research works are done rapidly to decrease the hindrance also .
       Therefore, it can be concluded that Wi-Fi technology will be most use full for security and Intelegence communication in  hilly area of North East , Orissa, Bihar etc., where most of  Naxalites, Terrorists  etc., are taking shelters.

digital image processing paper presentation


DIGITAL IMAGE PROCESSING: APPLICATION FOR ABNORMAL INCIDENT DETECTION








A  PRESENTATION BY
 



D.ARAVIND
III/IV B.Tech
Sri Sarathi  Institute Of Engineering & Technology
Nuzvid
Ph:9885501050
Email:aravind_devarapalli@yahoo.co.in

P.NAVEEN

III/IV B.Tech
. Sri Sarathi  Institute Of Engineering & Technology

Ph:9866221733
Email:navin_pasupuleti@yahoo.co.in






                                                       
Sri Sarathi Institute Of  Engineering & Technology

                                           Nuzvid-521201

                                       ANDHRA PRADESH

                 

                    







Abstract- Intelligent vision systems (IVS) represent an exciting part of modern sensing, computing, and engineering systems. The principal information source in IVS is the image, a two dimensional representation of a three dimensional scene. The main advantage of using IVS systems is that the information is in a form that can be interpreted by humans.
               Our paper is an image process application for abnormal incident detection, which can be used in high security installation, subways, etc. In our work, motion cues are used to classify dynamic scenes and subsequently allow the detection of  abnormal  movements,  which may be related critical situations.
                 Successive frames are extracted from the video stream and compared. By subtracting the second image from the first, that difference image is obtained. This is the segmented to aid error measurement and thresholding. If is the threshold is exceeded, the human operator is alerted. So, that he / she may take remedial action. Thus by processing the input image suitably, our system alerts operators to any abnormal incidents, which might lead to critical situations.




















1. Introduction

1.1. Need for automated Surveillance

Motion-based automated surveillance or intelligent scene-monitoring systems were introduced in the recent past. Video motion detection and other similar systems aim to alert operators or start a high-resolution video recording when the motion conditions of a specific area in the scene are changed.
                         In recent years interest in automated surveillance systems has grown dramatically as the advances in image processing and computer hardware technologies have made it possible to design intelligent incident detection algorithms and implemented them as real-time systems. The need for such equipment has been obvious for quite some time now, as human operators are unreliable, fallible and expensive to employ. 

1.2. Motion analysis for incident detection
                        Interest in motion processing has increased with advance in motion analysis methodology and processing capabilities. The concept of automated incident detection is based on the idea of finding suitable image cues that can represent the specific event of interest with minimum overlapping with other classes. In this paper, motion is adopted as the main cue for abnormal incident detection.

1.3. Image acquisition
Obtaining the images is the first step in implementing the system.

1.4. Camera position
The camera is placed at a fixed height in the subway or corridor. This portion need not be changed along the course of operation.
              fig1. Camera Position

1.5. Frame extraction

                        In this system, motion is used as the main cue for abnormal incident detection. It is henceforth obvious that the first concern is obtaining the images required from the source. In the circumstances described (subways, high security installations) usually a closed circuit television system is employed.
                          Any ordinary video systems use 25 frames per second. The system described here uses scene motion information extracted at the rate of 8.33 times per second. These amounts to capturing a frame once every two frames in the video camera system. In practical real time operation a hardware block-matching motion detector is used for frame extraction.

2. THE DIFFERENCE IMAGE:
There are two major approaches to extracting two-dimensional motion from image sequential optical flow and motion correspondence.  Simple subtraction of images acquired at different instants in time makes motion detection possible, when there is a stationary camera and constant illumination.  Both of these conditions are satisfied in the areas of application of our system.
                  A difference image is nothing but a binary image d (i , j) where non-zero values represent image areas with motion, that is areas where these was a substantial difference between gray levels in consecutive images p1 and p2:
      d (i, j)  =  0      if  ½ p1 (i, j) – p2 (i, j) ½  <= ε
                  =  1      otherwise
Where ε is a small positive number. The   figure 3 shows the resultant image obtained by subtracting the images 1 and 2.  The threshold level used in the system is 0.8, which is found to be sufficient for obtaining a good binary difference image

             

         Initial position                     final position
                              
                              Difference image

The second figure shows a slightly displaced version of the first figure.
                                The system errors mentioned in the last item must be suppressed. If it is required to find the direction of motion, it can do by constructing a cumulative difference image.  This cumulative difference image can be constructed from a sequence
of images. This , however is not necessary our system as the direction of motion is invariably the same.
                     Obtaining the difference image is simplified in the MATLAB image processing Toolbox. The input images are read using the ‘imread’ function and converted to a binary image using the ‘ im2bw’ function. The  ‘ im2bw’ function converts the input image to gray scale image. The output binary image BW is 0.0  (black) for all pixels in the input image with luminance less than a user defined level and 1.0 (white) for all other pixels.

2.1. Segmentation details
This says about the segmentation details of the object. There are two basic forms of segmentation.
  • Complete Segmentation.
  • Partial Segmentation.

2.1.1. Complete and partial segmentation

                         Complete segmentation results in a set of disjoint region corresponding uniquely with objects in the input image. In partial segmentation, the regions may not correspond directly with the image objects.
                        If partial segmentation is the goal, an image is divided into separate regions that are homogeneous with respect to a chosen property such as brightness, color, reflectivity, texture etc.
                         Segmentation methods can be divided into three groups according to the dominant features they employ. First is global knowledge about an image or its past; edge-based segmentation forms the second group and region based segmentation, the third. In the second and third group each region can be represented by its closed boundary and each closed boundary describes the region. Edge based segmentation methods find the borders between regions while region based methods construct regions directly.
Region growing techniques are generally better in noisy images where borders are not very easy to detect. Homogeneity is an important property of regions and is used as the main segmentation criterion in region growing, where the basic idea is to divide an image into to zones of maximum homogeneity.
A complete segmentation of an image R is a finite set of regions R1...Rs ,
                      s
            R = U Ri                   Ri ∩ Rj = Φ     i ≠ j
                    i =1

Further, for region-based segmentation, the conditions need to be satisfied.

  H (Ri)          =     TRUE    i = 1,2,s
  H (Ri U Rj)  =     FALSE  i≠j

Ri adjacent to Rj where S is the total number of regions in an image and H(Ri) is a binary homogeneity evaluation of region Ri. Resulting regions of the segmented image must be both homogeneous and maximal where  ‘maximal’ means that the homogeneity criterion would not be true after merging a region with any adjacent region.

2.2. Region merging and splitting
The basic approaches to region-based segmentation are
  • Region Merging
  • Region Splitting
  • Split-and-Merge processing.

Region merging starts with an over segmented image and merges similar or homogeneous regions to form larger regions until no further merging are possible. Region splitting is the opposite of region merging. It begins with an under segmented image where the regions are not homogeneous. The existing image regions are sequentially split to form regions properly.



2.3. Region growing and segmentation

                       Our system uses the region growing segmentation method to video the image in to regions. In region growing segmentation, a seed point is first chosen in the image. Then the eight neighbours of the pixel are checked for a specific threshold condition. If the condition is satisfied it is incorporated as part of the region. This process is repeated for each of the eight neighbours and this continues until every pixel has been checked, and the whole image has been segmented into regions.      
                       In our system, the MATLAB function ‘bwlabel’ which performs region-growing segmentation. This function accepts the image to be segmented as input and returns a matrix representing the segmented image along with the number of segments.  It is to be noted that the image at this stage of processing is a binary image with only two levels-black (1) and white (0).

2.3.1. Segmentation algorithm

Ø  AN INITIAL SET OF SMALL AREAS ARBITERATIVELY MERGED ACCORDING TO SIMILARITY CONSTRAINTS.
Ø  START BY CHOOSING AN ARBITRARY SEED PIXEL, COMPARE IT WITH NEIGHBOURING PIXELS.
Ø  REGION IS GROWN FROM THE SEED PIXEL BY ADDING IN NEIGHBOURING PIXELS THAT ARE SIMILAR, INCREASING THE SIZE OF REGION.
Ø  WHEN THE GROWTH OF ONE REGION STOPS WE SIMPLY CHOOSE ANOTHER SEED PIXEL WHICH DOES NOT YET BELONG TO ANY REGION AND START AGAIN.
Ø  THE WHOLE PROCESS IS CONTINUED UNTIL ALL PIXELS BELONG TO SOME REGION.

                   
            example of difference image

Segment 1:                                                             
0   0   0   0   0   0   0   0   1   1   1    1
0   0   0   0   0   0   0   0   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   0   1   1   1   1    1
0   0   0   0   0   0   1   1   1   1   1    1
0   0   0   0   0   0   1   1   1   1   1    1
0   0   0   0   0   0   1   1   1   1   1    1
0   0   0   0   0   1   0   1   1   1   1    1
0   0   0   0   0   1   1   0   0   0   0    0


Segment 2:
0   0   0   0    2    2     2    2    0   0    0    0
0   0   0   0    2    2     2    2    2   2    2    2
0   0   0   2    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2
0   0   2   0    2    2     2    2    2   2    2    2
0   0   2   2    2    2     2    2    2   2    2    2
 0   0   0   2    2    2     2    2    2   2    2    2
0   0   0   0    2    2     2    2    2   2    2    2



       A portion of the corresponding segment matrix





3. THRESHOLDING AND ABNORMAL INCIDENT DETECTION

3.1. Need for thresholding

                    The process of segmentation aids in extracting the require information separately – in this case, the segments are a representation of the amount of motion of the subjects in the scene from one frame to the next. 

              
        Difference image
3.2. Thersholding algorithm
  • Get the total number of segments k.
  • Repeat through steps 3 to 7 for all k segments.
  • Scan the matrix to find the kith segment.
  • Store the column indices of the kith segment.
  • Find the maximum and minimum index values; subtract to find their difference.
  • If difference is greater than or equal to 16 pixels, sound alarm to alert the human operator.
  • Continue with next segment.
            An example of how the differences are stored in the form of a column vector is shown. If any value in the difference matrix is greater than or equal to 16, the human operator is alerted.


Sample difference matrix      
    6                    20                                        
     8                 20
    13                20
    17                20
    20                20
    20                20
    20                20
    20                20
    20                20
    20                18
    20                11

In the sample matrix for the difference image shown above, the                                                                                                                                                                                                 threshold of 16 pixels is exceeded and the human operator is alerted.


RESULTS
                  First image

          Second image


 













4. ADVANTAGES:


                The system we have explained can be used as mentioned earlier as an efficient and easily implementable pedestrian monitoring system in subways. It can detect quickly any fast or abnormal movement, which may lead to dangerous situations. Further, surveillance by humans is dependent on the quality of the human operator and a lot of factors like operator fatigue, negligence may lead to degradation of performance. These factors may can intelligent vision system a better option.

5. CONCLUSION:
                   Further, surveillance by humans is dependent on the quality of the human operator and a lot of factors like operator fatigue, negligence may lead to degradation of performance. These factors may can intelligent vision system a better option.  as in systems that use gait signature for recognition in vehicle video sensors for driver assistance.