Wireless Integrated Network Sensors(WINS) provide a new monitoring and control capability for transportation, manufacturing, health care, environmental monitoring and safety and security. They combine sensing, signal processing, decision capability, and wireless networking capability in a compact, low power system. This paper covers important applications of WINS, Border Security. Using the concept of WINS we can easily identify a stranger or some terrorists entering the border. The border area is divided into number of nodes. Each node is in contact with each other and with the main node. The noise produced by the foot-steps of the stranger is collected using the sensor. This sensed signal is then converted into power spectral density and the compared with reference value of our convenience. Accordingly the compared value is processed using a microprocessor, which sends appropriate signals to the main node. Thus the stranger is identified at the main node. A series of interface, signal processing, and communication systems have been implemented in micro power CMOS circuits.

Introduction

Wireless Integrated Network Sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security, border security. WINS combine micro sensor technology, low power signal processing, low power computation, and low power, low cost wireless networking capability in a compact system. WINS networks provide sensing, local control, and embedded intelligent systems in structures, materials, and environments. Compact geometry and low cost allows WINS to be embedded and distributed at a small fraction of the cost of conventional wireline sensor and actuator systems. On a local, wide-area scale, battlefield situational awareness will provide personnel health monitoring and enhance security and efficiency. Also, on a metropolitan scale, new traffic, security, emergency, and disaster recovery services will be enabled by WINS. On a local, enterprise scale, WINS will create a manufacturing information service for cost and quality control. The opportunities for WINS depend on the development of scalable, low cost, sensor network architecture. This requires that sensor information be conveyed to the user at low bit rate with low power transceivers. Continuous sensor signal processing must be provided to enable constant monitoring of events in an environment. Distributed signal processing and decision making enable events to be identified at the remote sensor. Thus, information in the form of decisions is conveyed in short message packets.

WINS NODE ARCHITECTURE

The WINS node architecture is developed to enable continuous sensing, event detection, and event identification at low power. Since the event detection process must occur continuously, the sensor, data converter, data buffer, and spectrum analyzer must all operate at micro power levels. In the event that an event is detected, the spectrum analyzer output may trigger the microcontroller. The microcontroller may then issue commands for additional signal processing operations for identification of the event signal. Protocols for node operation then determine whether a remote user or neighboring WINS node should be alerted. The WINS node then supplies an attribute of the identified event, for example, the address of the event in an event look-up-table stored in all network nodes. Total average system supply currents must be less than 30mA. Low power, reliable, and efficient network operation is obtained with intelligent sensor nodes that include sensor signal processing, control, and a wireless network interface. Distributed network sensor devices must continuously monitor multiple sensor systems, process sensor signals, and adapt to changing environments and user requirements, while completing decisions on measured signals.

For the particular applications of military security, the WINS sensor systems must operate at low power, sampling at low frequency and with environmental background limited sensitivity. The micro power interface circuits must sample at dc or low frequency where “1/f” noise in these CMOS interfaces is large. The micropower signal processing system must be implemented at low power and with limited word length. In particular, WINS applications are generally tolerant to latency. The WINS node event recognition may be delayed by 10 – 100 msec, or longer.

WINS MICROSENSORS

Source signals (seismic, infrared, acoustic and others) all decay in amplitude rapidly with radial distance from the source. To maximize detection range, sensor sensitivity must be optimized. In addition, due to the fundamental limits of background noise, a maximum detection range exists for any sensor. Thus, it is critical to obtain the greatest sensitivity and to develop compact sensors that may be widely distributed. Clearly, microelectromechanical systems (MEMS) technology provides an ideal path for implementation of these highly distributed systems. The sensor-substrate “Sensorstrate” is then a platform for support of interface, signal processing, and communication circuits. Examples of WINS Micro Seismometer and infrared detector devices are shown in “fig3”. The detector shown is the thermal detector. It just captures the harmonic signals produced by the foot-steps of the stranger entering the border. These signals are then converted into their PSD values and are then compared with the reference values set by the user.

ROUTING BETWEEN NODES

The sensed signals are then routed to the major node. This routing is done based on the shortest distance. That is the distance between the nodes is not considered, but the traffic between the nodes is considered. This has been depicted in the “fig4”. In the figure, the distances between the nodes and the traffic between the nodes have been clearly shown. For example, if we want to route the signal from the node 2 to node 4, the shortest distance route will be from node 2 via node 3 to node 4. But the traffic through this path is higher than the path node 2 to node 4. Whereas this path is longer in distance.

SHORTEST DISTANCE ALGORITHM

In this process we find mean packet delay, if the capacity and average flow are known. From the mean delays on all the lines, we calculate a flow-weighted average to get mean packet delay for the whole subnet. The weights on the arcs in the figure 5 give capacities in each direction measured in kbps.

In “fig6” the routes and the number of packets/sec sent from source to destination are shown. For example, the E-B traffic gives 2 packets/sec to the EF line and also 2 packets/sec to the FB line. The mean delay in each line is calculated using the formula.

Ti =1/(µc-λ) (1)

Where Ti= Time delay in sec

C= Capacity of the path in Bps

µ= Mean packet size in bits

λ= Mean flow in packets/sec.

The mean delay time for the entire subnet is derived from weighted sum of all the lines. There are different flows to get new average delay. But we find the path, which has the smallest mean delay-using program. Then we calculate the Waiting factor for each path. The path, which has low waiting factor, is the shortest path. The waiting factor is calculated using :

W = λi / λ (2)

Where λi = Mean packet flow in path

λ = Mean packet flow in subnet

CONCLUSION

A series of interface, signal processing, and communication systems have been implemented in micropower CMOS circuits. A micropower spectrum analyzer has been developed to enable low power operation of the entire WINS system. Thus WINS require a Microwatt of power. But it is very cheaper when compared to other security systems such as RADAR under use. It is even used for short distance communication less than 1 Km. It produces a less amount of delay. Hence it is reasonably faster. On a global scale, WINS will permit monitoring of land, water, and air resources for environmental monitoring. On a national scale, transportation systems, and borders will be monitored for efficiency, safety, and security.

ABSTRACT

  The current memory technologies when needed to expand will allow expansion only two dimensional space. Next Generation Memories such as Polymer memory satisfy all of the good attributes of memory. The most important one among them is their ability to support expansion in three dimensional spaces.

 A new memory technology promises to store more data at less cost than the expensive to build silicon chips used by popular consumer gadgets including digital cameras, cell  phones and portable music players. The magical ingredient is not smaller transistors or an exotic material cooked up by the  semiconductor industry. It is a plastic. This new memory does not use transistors to store information.  Instead, bits are written when a strong current passes through a polymer fuse, causing it to blow and change its conductivity.

I.INTRODUCTION

           While experimenting with a polymer material known as PEDOT, Princeton University researcher Sven Moller determined that although the plastic conducts electricity at low voltages, it permanently loses its conductivity when exposed to higher voltages. Together with colleagues from Hewlett-Packard Laboratories, he developed a method to take advantage of this property to store digital information, which can be stored as collections of ones and zeros. The PEDOT-based memory card consists of a grid of circuits comprising polymer fuses. A large applied current causes specific fuses to “blow,” leaving a mix of functioning and nonfunctioning connections. When a lower current is later used to read the data, a blown fuse blocks current flow and is read as a zero, whereas a working

fuse is interpreted as a one. Because the storage method involves a physical change to the device, it is a so-called WORM–write once, read many times–technology.

II.FEATURES OF POLYMER MEMORY

1. Data stored by changing the polarization of the polymer between metal lines.
2. Zero transistors per bit of storage.
3. Memory is Nonvolatile.
4. Microsecond initial reads. Write speed faster than NAND and NOR Flash.
5. Simple processing, easy to integrate with other CMOS.
6. No cell standby power or refresh required.
7. Operational temperature between – 40 and 110°C

III.PLASTIC MEMORY BEING DEVELOPED

            Researchers at Princeton University working with Hewlett-Packard have invented a new form of permanent computer memory that uses plastic, and may be much cheaper and faster than existing silicon circuits. By utilising a previously unknown property of a cheap, transparent plastic called PEDOT short for polyethylenedioxythiophene the inventors say that data densities as high as a megabit per square millimetre should be possible. By stacking layers of memory, a cubic centimeter device could hold as much as a gigabyte and be cheap enough to compete with CDs and DVD. PEDOT is an unusual plastic because it conducts electricity, a property that’s led to it being used for antistatic coatings. However, a sufficiently large pulse of current changes it permanently to an unconducting state, just like a fuse. By putting microscopic pellets of the stuff between two grids of wires, data can be stored by blowing patterns of bits. The memory cannot be rewritten, but can be read very fast and with low power consumption. The biggest challenge is developing production techniques. ‘We are hybridising’ said the leader of the research group, Princeton professor of electrical engineering Stephen Forrest. ‘We are making a device that is organic the plastic polymer and inorganic thin film silicon at the same time’.

“Fig. 1” showing polymer fuse,pedot,stainless steel.

   IV.HOW DOES POLYMER MEMORY WORK

Polymer memory stores information in an entirely different manner than silicon devices. Rather than encoding zeroes and ones as the amount of charge stored in a cell, Coatue’s chips(“fig. 2a”) store data based on the polymer’s electrical resistance. Using technology licensed from the University of California, Los Angeles, and the Russian Academy of Sciences in Novosibirsk, Coatue fabricates each memory cell(“fig. 2b”) as a polymer sandwiched between two electrodes. To activate this cell structure, a voltage is applied between the top and bottom electrodes, modifying the organic material. Different voltage polarities are used to write and read the cells. Application of an electric field to a cell lowers the polymer’s resistance, thus increasing its ability to conduct current; the polymer maintains its state until a field of opposite polarity is applied to raise its resistance back to its original level. The different conductivity States represent bits of information. A polymer retains space charges near a metal interface when there is a bias, or electrical current, running across the surface. These charges come either from electrons, which are negatively charged, or the positively-charged holes  acated by electrons. We can store space charges in a polymer layer, and conveniently check the presence of the space charges to know the state of the polymer layer. Space charges are essentially differences in electrical charge in a given region. They can be read using an electrical pulse because they change the way the device conducts electricity. The basic principle of Polymer based memory is the dipole moment possessed by polymer chains. It is the reason by which polymers show difference in electrical conductivity. As explained earlier implementing a digital memory means setting up away to represent logic one and logic zero. Here polarizations of polymers are changed up or down to represent logic one and zero.

(“Fig.2a”) Coatue’s

(“Fig.2b”) Coatue’s memory cell

V. ADVANTAGES OF POLYMER MEMORY

1. Plastic memory is fast. Labbuilt devices with a 1GB storage capacity have yielded read/write cycle times that are 10 times faster than CompactFlash, which are typically 2- 10MB/s read, 1-4MB/s write..
2. Memory is Nonvolatile
3. Fast read and write speeds
4. It requires far fewer transistors, typically only 0.5M (million) for 1GB of storage compared to silicon’s 1.5-6.5B      (billion).
5. It can be stacked vertically in a product, yielding 3D space usage; silicon chips can only be set beside each other.
6. Very low cost/bit, high capacity per dollar
7. Low power consumption
8. Easy manufacture _ use ink-jet printers to spray liquid-polymer circuits onto a surface
9. Thin Film system requires about 0.5 million transistors per gigabit of memory. Traditional silicon-based system would require between 1.5 to 6.5 billion transistors for that same gigabit.

EXPANDING MEMORY CAPABILITY- STACKED MEMORY 

Expanding memory capability is simply a matter of coating a new layer on top of an existing one. The footprint remains the same even after expansion because each new layer adds the same capacity as the first one. This stacking is a fundamental strength of the Thin Film technology. A layer may include a self-contained active memory structure with on-layer TFT circuitry, or share circuitry with all other layers. Both approaches offer true 3D memory architecture. This means that the new technology is not just for saving space, but also the option of using different, and optimized software architectures.

         The driver circuitry, comprising column and row decoders, sense amplifiers, charge pumps and control logic, is located entirely outside the memory matrix, leaving this area completely clear of circuitry, or be 100% built underneath the memory array. This is the fundamental factor which enabled the stacking option. With no circuitry in the memory plane, it is possible to build the polymer memory on top of other chip structures, e.g. processors or memory.

If you want to add more memory with silicon-based technology, you move in a two dimensional space. Put simply, the area taken, 128 MB RAM, is more than the area occupied by 64 MB RAM. With the new polymer-based technology, you will move in a three-dimensional space.

         That is, you move from talking about area to talking about volume. Put simply, a 128 MB RAM module will have the same footprint as a 64 MB module, but slightly thicker (or higher). This difference in thickness or height will be so small, that we may not even be able to tell the difference by just looking at it. If a 64 MB silicon-based module takes up 20mmx10mmx6mm (1200 cubic mm of space), then 124 MB occupies approximately double that volume. However, with polymer-based memory, the footprint (length x breadth) will remain the same (200 sq mm) but the height would increase only by about 1/10000th of a millimeter, which adds practically nothing to the volume. Polymer memory layers are just 1/10,000 of a millimeter or less in thickness, autonomous and easy to deposit. Layer upon layer may be coated on a substrate. A layer may include a selfcontained active memory structure with on-layer circuitry and TFT, or share circuitry (as in hybrid polymerover- silicon chips). In the latter case, stacked layers may be individually addressed from the bottom circuitry, giving three dimensional storage capacities. The Thin Film memory system is expandable by the addition of new layers manufacturers will be able to gain previously unattainable storage capacity within a given footprint.

  Examples: The equivalent of 400,000 CDs, or 60,000 DVDs, or 126 years of MPG music may be stored on a polymer memory chip the size of a credit card. 

LIMITATIONS OF POLYMER MEMORY 

But turning polymer memory into a commercial product won’t be easy. Memory technologies compete not only on storage capacity but on speed, energy consumption and reliability. The difficulty is in meeting all the requirements of current silicon memory chips. Until new memory materials are able to  compete with the high performance of silicon, their notes, they are likely to be limited to niche applications. One likely use is in disposable electronics, where cost, rather than performance, is the deciding factor. Researchers at Lucent Technologies’ Bell Laboratories are working on polymer memory devices for use in identification tags. The polymer memory made at Bell Labs is still relatively slow by silicon standards, and anticipated capacity is only on the order of a kilobit. But, says Bell Labs chemist Howard Katz, the flexible and low-cost polymer memory devices could be “very attractive” for, say, identification tags meant to be thrown away after a few uses.

CONCLUSION

         Plastic memory is considerably cheap and fast as compared to the silicon memory. This memory can be easily developed as the material required is easily available and the process of manufacturing is also simple. No huge investment is required as compared to its counterpart. The power consumption is very less and  the memory device is highly dense which can accumulate large amount of data in small space. The data is maintained in memory even when the power is off. As the technology is still in development phase it does not enjoy large business in the market. It needs lot of efforts by researchers and the marketing section to make this particular concept of memory popular and enjoy it is real worth.

                Samsung has come up with one more stunning smartphone Galaxy S III 19300. She has got smooth curves and sexy finishing. I think many are waiting  for to grab this sexy lady to their home.

             As per the news  Galaxy S III will be launched in the first week of June. And Galaxy S III will be priced at Rs. 38,000 (MRP), while the street price is expected to be around Rs. 34,000.

Some new features  introduced in Samsung Galaxy S III are

• New app like “face recognition photo” app lets you instantly send pictures to your friends that it recognizes in your pictures.
• The Galaxy S III also has a “pop-up player”, which is sort of an app version of picture-in-picture that allows you to watch a movie while you switch around to other applications.
• One more new feature named “S-Voice”. It is an innovative app to which you can ask things like, “Hi Mr. Galaxy S III, what’s the weather gonna be like today?” and S Voice will pop-up a screen that looks exactly like Apple’s Siri.
• Scan and Match for Music (iTunes Match).
• An “All Share” app uses Wi-Fi Direct connectivity which is available for a complete set of categories like AllShare Cast for video streaming, AllShare Play for file sharing, and Group Cast for sharing screens with multiple other devices on the same Wi-Fi network.
• 4.8 inch HD Super AMOLED (1280×720) display
• Android 4.0 (Ice Cream Sandwich)
• Recording & Playback: Full HD (1080p)

Specifications and Features of  and  of Galaxy S III 19300

Dimensions : 136.6 mm x 70.6 mm x 8.6 mm
Battey : Standard battery, Li-Ion 2100 mAH
Display Size: 4.8 ”
Resolution :   720 x 1280 pixels resolution
Primary Camera : 8 MP, 3264×2448 pixels, autofocus, LED flash
Secondary Camera : 1.9 MP
USB connectivity:  micro USB, v2.0
Operating System : Android OS, v4.0.4 (Ice Cream Sandwich).
Graphics : Mali-400 MP
Color   : Blue and White
Sensors : It has accelerometer, gyro, RGB sensor, proximity, compass, barometer
Messaging : SMS(threaded view), MMS, Email, Push Mail, IM, RSS.
Browsers : HTML5, Adobe Flash
Video files supported : Supports MPEG-4, DivX, Xvid, WMV, H.263, 3GP, HD
Bluetooth : NFC, Bluetooth 4.0(LE) 
External Memory : 64 GB Expandable,
Internal Memory : 16/32/64 GB storage with 1 GB RAM
Price : Rs. 34,000 Approx.

The review video of Samsung Galaxy S III is as shown below

Abstract— Current authentication systems suffer from many weaknesses. With all the means of technology developing, it can be very easy for ‘others’ to fabricate or to steal identity or to hack someone’s password. Users nowadays are provided with major password stereotypes such as textual passwords, biometric scanning, tokens or cards (such as an ATM). But some people hate the fact to carry around their cards, some refuse to undergo strong IR exposure to their retinas. Now with technology change, fast processors and many tools on the Internet this has become a Child’s Play. Therefore we propose and evaluate our contribution which is a new scheme of authentication. This scheme is based on a virtual three-dimensional environment.  Users navigate through the virtual environment and interact with items inside the virtual three- dimensional (3-D)   environment.   The combination of   all interactions, actions and inputs towards the items and towards the virtual three- dimensional environment constructs the user’s 3D password. The 3D password combines most existing authentication schemes such as textual passwords, graphical passwords, and biometrics into one virtual three-dimensional environment and main application is the protection of critical resources and systems.

 INTRODUCTION

Authentication is the process of validating who you are to whom you claimed to be.  In general, there are four human authentication techniques:

1.      What you know (knowledge based).

2.      What you have (token based).

3.      What you are (biometrics).

4.      What you recognize (recognition based).

Textual passwords are the most common authentication techniques used in the computer world. Textual password has two conflicting requirements: passwords should be easy to remember and hard to guess.

Klein acquired a database of nearly 15,000 user accounts that had alphanumerical passwords, and stated that

25% of the passwords were guessed using a small, yet well- formed dictionary of (3 × 106) words.

Even though the full textual password space for 8- character passwords consisting of letters and numbers is almost (2 × 1014) possible passwords, by using a small subset of the full space, 25% of the passwords were   guessed correctly.  This fact is due to the user’s   carelessness in selecting their textual passwords and to the fact that most users do not select random passwords. Many graphical passwords schemes have been proposed. The strength of graphical passwords comes from the fact that users can recall and recognize pictures more than words. Most graphical passwords are vulnerable for shoulder surfing attacks,   where   an   attacker   can   observe   or   record   the legitimate user’s graphical password by camera. A study concluded that the selection of faces in Pass Faces can be affected by the attractiveness, gender and race of the selected face which results in an insecure scheme. Currently, many types of graphical passwords are under study yet, it might be some time before they can be applied in the real world.

Token based systems such as ATMs are widely applied in banking systems and in laboratories entrances as a mean of authentication.   However, tokens are vulnerable to loss or theft.   Moreover, the user has to carry the token whenever access required.

Many biometric schemes have been proposed. Each biometric recognition scheme is different considering consistency, uniqueness, and acceptability.  Users tend to resist some biometrics recognition systems due to its intrusiveness to their privacy.

The 3D password combines all existing authentication schemes into one three-dimensional virtual environment.  The three-dimensional virtual environment consists of many items or objects. Each item has different responses to actions. The user actions, interactions and inputs towards the objects or towards the three-dimensional virtual environment create the user’s 3D password.

The 3D password gives users the freedom of selecting what type of authentication techniques they want to be performed as their 3D password. The 3D password has a large number of possible passwords because of the high number of possible actions and interactions towards every object and towards the three dimensional virtual environment.

The remainder of this paper is organized as follows: Section II introduces the 3D password. Section III discusses the security analysis. Section IV presents our conclusions and future work.

Abstract— This paper deals with the security problems in the distribution automation system (DAS) which has an inherent vulnerability to cyber attacks due to its high dependency on the communication and geographically widely spread terminal deceives. We analyze the types of cyber threads in many application of the distribution system and formulate security goals. The protocol avoids complex computation of any encryption algorithm, considering resource- constraint network nodes.

                                                                                                                                                               Introduction

The Distribution automation system (DAS) provides capabilities for a central server to collect operation data such as voltage and current, to monitor and control feeder remote terminal units (FRTU) which are dispersed in the remote areas, and to detect and restore faults automatically. As information exchange between the DAS server and field equipments becomes more critical for the system operation, communication technology plays an integral part of the distribution system.

 Despite the importance of the communication technology, little effort has yet been invested on cyber security in the power system networks including the supervisory control and data acquisition (SCADA) system and the distribution automation system. In most SCADA systems, the approach for security relies on the physical security where equipments are located in highly protected sites and only authorized operators can access them.

The major reason why the SCADA security is getting attention is that the SCADA system is no longer a closed network where only privileged and authorized persons can have rights to access. Recent advances in business model require the SCADA network to be connected with corporate networks.

Moreover, the communication architecture is more relying on the open standard communication protocols. The use of the open communication protocols renders the SCADA system more vulnerable to cyber attacks.

In the past few years, the security issues in the SCADA system have been analyzed and some efforts have been carried out for developing security mechanisms. The works have been focused on mostly key management schemes for cryptographic algorithm as well as transition issues for adapting security mechanisms and intrusion detection schemes.

As for the cyber attacks, the distribution system is more vulnerable in many ways. The terminal devices in the SCADA system are mostly located in restricted local area networks, while FRTUs in the distribution system are located at remote and unmanned sites in most cases, and are spread in wider area networks. As communication between the DAS server and FRTUs becomes more critical, security measures should be implemented to protect the normal control operations from any cyber threats. Recently, agent-based service restoration algorithms in the DAS network have been proposed, and those algorithms are dependent on the security and reliability of the network.

In this paper, we consider possible cyber attacks in the applications based on the current distribution communication architecture, and then derive the security goals. Next, we analyze the cryptographic algorithms and devise an efficient security protocol that can be adapted to achieve these security goals, considering the constraints imposed on the distribution system.

In the following section, we explain the communication architecture we make reference to, and analyze the cyber threats and formulate the security goals. In Section III, we consider the efficient ways of adapting the current cryptographic algorithms. In Section IV, we propose the security protocols to achieve the security goals.

Specifications of Galaxy Note N7000

Dimensions : 146.9 x 83 x 9.7 mm
Battey : Li-Ion, 2500 mAh
Display screen Size: 5.29 Inches
Resolution :  800 x 1280 Pixels
Colors support : supports for 16 Millions of colors
Primary Camera : 8 MP, 3264×2448 pixels with autofocus, LED flash
Secondary Camera : 2 Megapixel

Talktime : 26 hrs (2G), 13 hrs (3G)
Standby Time : 960 hrs (2G), 820 hrs (3G)
Internet Features: Push mail, Adobe Flash 10.1
USB connectivity: Yes, micro USB, v2
Operating System : Android v2.3 (Gingerbread) OS with Touchwiz 4 launcher.
Processor : 1.4 GHz ARM Cortex-A9, Dual Core
Graphics : Mali-400 MP
Display Type :  HD Super AMOLED
Color   : Black and White
Sensors : It has Accelerometer, gyro, proximity, compass, barometer.
Messaging : SMS(threaded view), MMS, Email, Push Mail, IM, RSS.
Browsers : HTML5, Adobe Flash
Video files supported : Supports MPEG-4, H.263, H.264, DivX, Xvid, WMV, VC-1, 3GP, HD Video Playback

Price : 32,700/- in Rupees.

• 5.29-inch HD Super AMOLED Touchscreen
• 1.4 GHz Dual Core ARM Cortex-A9 Processor with 1GB of RAM
• Full HD 1080p video Recording @30fps
• TV-out (via MHL A/V link)
• Supports for Java via Java MIDP emulator

The unboxing video of Note N7000 is as shown below

Here are the user’s reviews and opinions about the Galaxy Note N7000

User I.

 I bought this phone 10 days ago and have been using it every day since then and here is what i think about this phone. 

Pros : 
1. Having this phone is like having a portable laptop with me all the time. 
2. Camera is superb.
3. Stylus is very comfortable to use. 
4. nice memory and processor speed. I often run 3-5 apps at a time and i hardly notice and performance degradation. 
5. Great number of apps and games.
6. Considering the massive screen and its capabilities battery lasts about a day to day and half. is better then the most smart phone and lot better than iPhone 4s (which dies with in a days use for the same amount of usage).

Cons:
1. Although it fits nicely into pocket, its massive size is not as comfortable as other smaller phones.
2. Phone hanged on 4 day. (although a faulty app caused the hang. it still shows that android has good scope to mature further)
3. UI is great but not as smooth as iPhone.

all in all i am glad i bought this phone and i would choose galaxy note over iphone 4s any time.

Galaxy Note is really handy and easy to carry. 

Eventually, its each one’s discretion of being either in heaven or hell …………………. with Galaxy Note I feel satisfied about my decision and I am in heaven…… CONGRATULATIONS !!!! to all those who feels the same with Galaxy Note “WELCOME TO THE FAMILY”.

User III.

As far as GUI is concerned galaxy note does a fantastic job. Browsing, Reading docs, etc is fun. But BIGGEST DRAWBACK of galaxy note is its audio. Its hopeless. Absolutely no surround sound no matter how good headphones one uses. And ya ZERO BASS as well.

User IV.

Best Phone ever!!!
I was in a dilemma whether to purchase Note or not.. Everyone suggested that the biggest disadvantage in note is its size.. its quite big.. But something made me to go for Note.. and trust me I am in love with NOTE..
I wont be comfortable with small mobile phones now.. that is the drawback of note..

User V.

The best phone I’ve ever used! Currently am using iphone 4s and Note.
Sorry apple fans( so am I..) but Note has overdone itself this time!
I’m using the ICS 4.0.3 currently.
There are no apparent lags as I heard from others.
The gaming is the best in its kind. No other device is so good in supporting full HD games as note!
Hats off!

User VI

Its been two days now since I’ve received my Galaxy Note and goodness, am I glad! The Note has utterly redefined the way I look at smartphones, no longer can I look at anything less than a four incher without cringing…the Note’s supreme five point three inch super amoled display has set the visual bar so high I’m afraid no phone will ever surpass the glory that has been revealed unto me. Pardon the emotion.

There’s a good reason why I beleive the Galaxy Note is the best phone on the planet as of now. Period. You see I’ve been using the HTC Desire HD for the past couple of months and although it was a great phone, it did have some very serious issues. The most annoying of which was the earth-shatteringly poor battery life. The Desire HD lasted a day at best, no matter how much I coaxed the bugger it would just blink off and die at the end of the day and that was annoying. To make matters worse, there were no reliable extended battery packs, the handful that were there were extremely limited thanks to HTC’s unibody design…a power nightmare.

So you could imagine the look on my face when Samsung announced that the Galaxy Note would be packing a stellar twenty-five-hundred mah battery, I think my Desire HD’s twelve-hundred and thirty mah battery curled up and called it quits. So indeed my expectations for the Note were high. I was not disappointed. The beast gave me two hours of 3G surfing, three hours of video, an hour and half of calling and an hour of playing games…at the end of which, it still had a good thirty percent juice left! And this with the battery literally new- gobsmackingly brilliant!

Almost everything is a comfort, from typing to using the s-pen. Although I must admit I do miss HTC’s predictive keyboard- which in my opinion is the best darn keyboard. Now a couple of words about the s-pen. First its the best thing since sliced bread, no seriously its that good, it makes me wonder why no other major manufacturer thiugh about it. No the s-pen does not work with the Galaxy SII or other capacitive phones and yes I’ve tried using it so. Secondly, and the only grouch, there is a noticeable lag if you swipe quickly while say making a sketch. This is most likely a software issue and I do hope Samsung will fix it in their IceCreamSandwich update. Fingers crossed!

In every other area the Galaxy Note does not disappoint. The speed at which the phone goes about its regular “chores” is staggeringly fast and the browser opens more than seven web pages without breaking a sweat. YouTube 1080p playback is smooth and glitch free while watching videos and playing games is second to none. Again there is no other phone that has a display as mesmerizing as the Note’s. The front and primary cameras are stellar. Video recording is second only to the iPhone’s, its worth mentioning that video recording is the only area the iPhone surpasses the Note. Office documents and pdf files open utter ease and the finesse with which the Note accomplishes its tasks is admirable.

I don’t think I’d ever want another phone

Neurochip based on light-addressable potentiometric sensor (LAPS), whose sensing elements are excitable cells, can monitor electrophysiological properties of cultured neuron networks with cellular signals well analyzed. Here I am reporting a kind of neurochip with rat pheochromocytoma (PC12) cells hybrid with LAPS and a method of de-noising signals based on wavelet transform. Cells were cultured on LAPS for several days to form networks, and we then used LAPS system to detect the extracellular potentials with signals de-noised according to decomposition in the time-frequency space. The signal was decomposed into various scales, and coefficients were processed based on the properties of each layer. At last, signal was reconstructed based on the new coefficients. The results show that after de-noising, baseline drift is removed and signal-to-noise ratio is increased. It suggests that the neurochip of PC12 cells coupled to LAPS is stable and suitable for long-term and non-invasive measurement of cell electrophysiological properties with wavelet transform, taking advantage of its time-frequency localization analysis to reduce noise.

Introduction;

Until recently, neurobiologists have used computers for simulation, data collection, and data analysis, but not to interact directly with nerve tissue in live, behaving animals. Although digital computers and nerve tissue both use voltage waveforms to transmit and process information, engineers and neurobiologists have yet to cohesively link the electronic signalling of digital computers with the electronic signalling of nerve tissue in freely behaving animals.

Recent advances in microelectromechanical systems (MEMS), CMOS electronics, and embedded computer systems will finally let us link computer circuitry to neural cells in live animals and, in particular, to re-identifiable cells with specific, known neural functions. The key components of such a brain-computer system include neural probes, analog electronics, and a miniature microcomputer. Researchers developing neural probes such as sub- micron MEMS probes, microclamps, microprobe arrays, and similar structures can now penetrate and make electrical contact with nerve cells with out causing significant or long-term damage to probes or cells.

Researchers developing analog electronics such as low-power amplifiers and analog-to-digital converters can now integrate these devices with micro- controllers on a single low-power CMOS die. Further, researchers developing embedded computer systems can now incorporate all the core circuitry of a modern computer on a single silicon chip that can run on miniscule power from a tiny watch battery. In short, engineers have all the pieces they need to build truly autonomous implantable computer systems.

Until now, high signal-to-noise recording as well as digital processing of real-time neuronal signals has been possible only in constrained laboratory experiments. By combining MEMS probes with analog electronics and modern CMOS computing into self-contained, implantable microsystems, implantable computers will free neuroscientists from the lab bench.

INTEGRATING SILICON AND NEUROBIOLOGY

Neurons and neuronal networks decide, remember, modulate, and control an animal’s every sensation, thought, movement, and act. The intimate details of this network, including the dynamic properties of individual neurons and neuron populations, give a nervous system the power to control a wide array of behavioural functions.

The goal of understanding these details motivates many workers in modern neurobiology. To make significant progress, these neurobiologists need methods for recording the activity of single neurons or neuron assemblies, for long timescales, at high fidelity, in animals that can interact freely with their sensory world and express normal behavioral responses.

Conventional techniques

Neurobiologists examine the activities of brain cells tied to sensory inputs, integrative processes, and motor outputs to understand the neural basis of animal behavior and intelligence. They also probe the components of neuronal control circuitry to understand the plasticity and dynamics of control. They want to know more about neuronal dynamics and networks, about synaptic interactions between neurons, and about the inextricable links between environmental stimuli and neuronal signalling, behavior, and control.

To explore the details of this biological circuitry, neurobiologists use two classes of electrodes to record and stimulate electrical signals in tissue

1. intracellular micropipettes to impale or patch- clamp single cells for interrogation of the cell’s internal workings, and
2. Extracellular wires or micro machined probes for interrogating multisite patterns of extra- cellular neural signalling or electrical activity in muscles.

Neurobiologists use amplifiers and signal generators to stimulate and record to and from neurons through these electrodes, and signal-processing systems to analyse the results. They have used these techniques for decades to accumulate a wealth of understanding about the nervous system. Unfortunately, to date, most of these experiments have been performed on slices of brain tissue or on restrained and immobilized animals, primarily because the electronic instruments required to run the experiments occupy the better part of a lab bench.

This situation leaves neurobiologists with a nagging question: Are they measuring the animals nor mal brain signals or something far different? Further, neurobiologists want to understand how animal brains respond and react to environmental stimuli. The only way to truly answer these questions is to measure a brain’s neural signalling while the animal roams freely in its natural environment.

SALIENT   OBJECTIVES

The solution to these problems lies in making the test equipment so small that a scientist can implant it into or onto the animal, using materials and implantation techniques that hurt neither computer nor animal. Recent developments in MEMS, semi-conductor electronics, embedded systems, bio compatible materials, and electronic packaging finally allow neuroscientists and engineers to begin packaging entire neurobiology experiments into hardware and firmware that occupy less space than a human fingernail.

Researchers call these bioembedded systems neurochips. Scientists from the University of Washing-ton, Caltech, and Case Western Reserve University have teamed to build these miniaturized implantable experimental setups to explore the neural basis of behavior.

This research effort has developed or is in the process of developing the following:

Miniaturized silicon MEMS probes for recording from the insides of nerve cells;

1. biocompatible coatings that protect these probes from protein fouling;
2. a stand-alone implantable microcomputer that records from and stimulates neurons, sensory pathways, or motor control pathways in an intact animal, using intracellular probes, extra- cellular probes, or wire electrodes;
3. neurophysiological preparations and techniques for implanting microchips and wire electrodes or MEMS probes into or onto animals in a way that does not damage the probes or tissue;
4. firmware that performs real-time biology experiments with implanted computers, using analytical models of the underlying biology; and
5. software to study and interpret the experimental results, eventually leading to reverse- engineered studies of animal behavior.

 As the “Neuroscience Application Examples” sidebar shows, the first neurochip experiments use sea slugs and moths in artificial environments, but broad interest has already arisen for using implantable computers in many other animals.

DESIGNER NEUROCHIPS

Like their bench top experimental counterparts, neurochips use amplifiers to boost low-voltage biological signals, analog-to-digital converters (ADCs) to digitize these signals, microcomputers to process the signals, on-board memory to store the signals, digital-to-analog converters (DACs) to stimulate nerves, and software to control the overall experiment.

Figure1. Neurochip functional block diagram solid line show required components, dashed lines show some optional components

          Figure 1 shows a neurochip’s basic elements. The key requirements are that the neurochip be small and lightweight enough to fit inside or onto the animal, have adequate signal fidelity for interacting with the millivolt-level signals characteristic of nerve tissue, and have sufficient processing power to perform experiments of real scientific value.

The basic components of a neurochip are commercially available today. They include instrumentation amplifiers, ADCs/DACs, reconfigurable microcomputers, and high-density memory. For example, a Programmable System-on-a-Chip from Cypress Microsystems integrates a microprocessor, variable-gain amplifiers, an ADC, a memory controller, and a DAC into a single integrated circuit.

First-generation neurochips integrate one or more ICs, passive elements such as capacitors, batteries, and 110 pads on small micro-PCBs. The prototype neurochip shown in Figure 2 used packaged ICs and button cells, and occupied a 1 cm x 3 cm printed-circuit board. The “production” version, due out of processing in early 2003, uses die-on- board technology and thin-film batteries, and is smaller than 1 square centimeter. Future-generation neurochips will integrate all the electronics onto a single silicon chip, and will likely be smaller than 10 mm on a side.

Probes

Building the probes that let a neurochip eaves drop on the electrical signaling in a nerve bundle, group of neurons, or single neuron presents a daunting task. Benchtop experiments on con strained animals typically use metallic needles— often made of stainless steel or tungsten—to communicate with nerve bundles, micromachined silicon probes to record from groups of neurons, or glass capillaries filled with a conductive ionic solution to penetrate and record from the inside of individual neurons. In unconstrained animals, flexible metallic needles, attached to the animal with surgical superglue, and micromachined silicon probes still work. However, replicating the performance of glass capillaries in flying, swimming, wiggling animals is a different story entirely.

Several centimeters long and quite fragile, the glass capillaries that neurobiologists use to probe the insides of nerve cells typically have tip diameters smaller than 0.3 microns. They impale neurons even more fragile than the probes themselves.Neuro biologists use micromanipulators to painstakingly and precisely drive single probes into single neurons. Fortunately, MEMS technology offers a possible alternative to these glass capillaries. As Figure 3 shows, University of Washington researchers are developing silicon MEMS probes and flexible inter connect structures to mimic the performance of glass capillaries in an implanted preparation. Researchers have already recorded intracellular signals with early prototypes, and development is on-going.

Glyme

Researchers seek to implant both probes and neurochips inside an animal’s brain. Unfortunately, an animal’s immune system rapidly and indiscriminately encapsulates all foreign bodies with proteins, without regard for the research value of implanted probes and neurochips. The adsorbed proteins not only attenuate the recorded electrical signals, but can also jeopardize the animal’s survival by causing abnormal tissue growth.

Researchers at the University of Washington’s Center for Engineered Biomaterials have developed plasma-deposited ether-terminated oligoethylene glycol coatings that inhibit protein fouling, as Figure 4 shows. Preliminary research indicates that these glyme coatings can reduce the protein fouling of probes and neurochips to levels acceptable for week-long experiments.

power

Neurochips can derive power from on-board batteries, external radiofrequency sources, a wire tether, or the nerve tissue itself, The ultimate decision on the power source depends on the nature of the experiments and the animal’s environment. Batteries are attractive because they avoid the antennas and charge pumps required to capture RF energy, operate in all environments, do not restrict the animal’s movement the way a tether does, and provide much more power than tapping nerve cells for energy.

Batteries have a weight disadvantage, but thin- film technologies using LiCoO2/LiPON/Li and Ni/KOHIZn promise flexible rechargeable batteries with peak current densities greater than 12 mA per square centimeter for short-duration experiments, and lifetimes measured in days or longer at low-current densities.

Batteries are ideal for the two sample preparations shown in the “Neuroscience Application Examples” sidebar. The typical hawkmoth flight time is less than 60 seconds. The 12 mA provided by a 200 mg, one-square-centimeter battery easily powers a neurochip for this experiment’s duration. The sea slug trolling methodically along the seafloor lies at the opposite end of the spectrum, needing only a few milliamps of current to power a neurochip for a week. The slug can easily accommodate a large battery in its visceral cavity, allowing extended untethered experiments.

Memory

Once implanted, an embedded neurochip must read its experimental procedure from memory, run the experiment, acquire the neural spike trains, then store the results in memory. As with all computer systems, memory size is an issue for neurochips. Fortunately, the electrical spike trains generated by nerve tissue have a stereotyped shape as shown in Figure 2c, suggesting that neurochips should com press the neural waveforms before storing them in memory.

Compressing the signals has two advantages. First, it effectively increases the on-board storage capacity. Second, it decreases the frequency of memory writes, reducing power consumption. Even simple compression algorithms such as run- length encoding can achieve better than 10 to 1 compression ratios on neural signals.

Custom algorithms that apply vector quantization, run-length encoding, and Huffman encoding to different parts of the neural waveform can achieve up to 1,000 to 1 compression ratios. Given the limited computing power of an implantable microcomputer, simpler is better when it comes to compression, but even simple RLE offers huge power and memory-size benefits.

A STIMULATING WORLD

Passive neurochips that do nothing more than record will provide neurobiologists with a wealth of data. But even now, with the first neurochips barely in production, neurobiologists are already calling for designs that stimulate nerve tissue as well as record from it. Active neurochips will allow stimulus-response experiments that test models of how nervous systems control behavior, such as how sensory inputs inform motor-circuit loops and the logic or model behind the response.

Indeed, the neurochip project’s long-term goal is to develop a hardware and software environment in which a neurobiologist conceives a stimulus-response experiment, encodes that experiment in software, downloads the experiment to an implanted neurochip, and recovers the data when the experiment concludes. Figure 5 shows a model of integrative biology in which neurochips play a key part.

CONCLUSION

With advances in integrated circuit processing we can expect ever more capable and power-efficient embedded computers. The simple neurochips of today will become the complex embedded systems of tomorrow, when embedding in this ultimate sense will mean computer electronics embedded in nerve tissue.

Enabling neuroscientists to better understand the neural basis of behaviour is reason enough to develop such devices. The long-term promise is much greater, however, perhaps leading one day to neural prosthetics, hardware-based human-computer interfaces, and artificial systems that incorporate principles of biological intelligence.

Introduction to working of search engines >>

BigFiles:

Big Files are virtual files spanning multiple file systems and are addressable by 64 bit integers. The allocation among multiple file systems is handled automatically. The BigFiles package also handles allocation and deallocation of file descriptors, since the operating systems do not provide enough for our needs.

Repository:

The repository contains the full HTML of every web page. Each page is compressed using zlib. The choice of compression technique is a tradeoff between speed and compression ratio. We chose zlib’s speed over a significant improvement in compression offered bzip. In the repository, the documents are stored one after the other and are prefixed by docID, length, and URL.

Document Index:

The document index keeps information about each document. It is a fixed width ISAM (Index sequential access mode) index, ordered by docID. The information stored in each entry includes the current document status, a pointer into the repository, a document checksum, and various statistics. If the document has been crawled, it also contains a pointer into a variable width file called docinfo which contains its URL and title. Otherwise the pointer points into the URLlist which contains just the URL.

Lexicon:

The lexicon has several different forms. The current lexicon contains 14 million words. It is implemented in two parts — a list of the words and a hash table of pointers.

Hit Lists:

A hit list corresponds to a list of occurrences of a particular word in a particular document including position, font, and capitalization information. Hit lists account for most of the space used in both the forward and the inverted indices. Because of this, it is important to represent them as efficiently as possible. Google considers several alternatives for encoding position, font, and capitalization – simple encoding, a compact encoding, and Huffman coding.

Forward Index:

The forward index is actually already partially sorted. It is stored in a number of barrels. Each barrel holds a range of wordID’s. If a document contains words that fall into a particular barrel, the docID is recorded into the barrel, followed by a list of wordID’s with hitlists which correspond to those words. Instead of storing actual wordID’s, it stores each worded as a relative difference from the minimum worded that falls into the barrel the worded is in.

Inverted Index:

The inverted index consists of the same barrels as the forward index, except that they have been processed by the sorter. For every valid wordID, the lexicon contains a pointer into the barrel that wordID falls into. It points to a doclist of docID’s together with their corresponding hit lists. This doclist represents all the occurrences of that word in all documents.

Anatomy & Working of Search Engines >>

Introduction to working of search engines >>

Architecture of Google search engine

In Google Search engine, the web crawling is done by several distributed crawlers. There is a URL server that sends lists of URLs to be fetched to the crawlers. The web pages that are fetched are then sent to the storeserver, which then compresses and stores the web pages into a repository. Every web page has an associated ID number called a docID, which is assigned whenever a new URL is parsed out of a web page. The indexing function is performed by the indexer and the sorter. Indexer reads the repository, uncompresses the documents, and parses them. Each document is converted into a set of word occurrences called hits. The hits record the word, position in document, an approximation of font size, and capitalization. The indexer distributes these hits into a set of ‘barrels’, creating a partially sorted forward index. The indexer performs another important function. It parses out all the links in every web page and stores important information about them in an anchors file. This file contains enough information to determine where each link points from and to, and the text of the link.

The URLresolver reads the anchors file and converts relative URLs into absolute URLs and in turn into docIDs. It puts the anchor text into the forward index, associated with the docID that the anchor points to.

It also generates a database of links, which are pairs of docIDs. The links database is used to compute PageRank for all the documents.

The sorter takes the barrels, which are sorted by docID, and resorts them by wordID to generate the inverted index. This is done in place so that little temporary space is needed for this operation. The sorter also produces a list of wordIDs and offsets into the inverted index. A program called Dump Lexicon takes this list together with the lexicon produced by the indexer and generates a new lexicon to be used by the searcher. The searcher is run by a web server and uses the lexicon built by Dump Lexicon together with the inverted index and the Page Ranks to answer queries.

Fig. A model of Google Architecture

How does Google crawl the web content ?

Running a web crawler is a challenging task. Crawling is the most fragile application since it involves interacting with hundreds of thousands of web servers and various name servers which are all beyond the control of the system.

To scale to millions of web pages, Google has a fast-distributed crawling system. A single URLserver serves lists of URLs to a number of crawlers. Each crawler keeps roughly 300 connections open at once. It works on a simple iterative algorithm. This algorithm differs with the search engines as well as the kind of query. This is necessary to retrieve web pages at a fast enough pace. This factor makes the crawler a complex component of the system.(Refer fig)

This means running a crawler which connects to more than half a million servers, and generates tens of millions of log entries. Because of the immense variation in web pages and servers, it is virtually impossible to test a crawler without running it on large part of the Internet.

The Google Ranking System:

Google maintains much more information about web documents than typical search engines. Every hitlist includes position, font, and capitalization information. Combining all of this information into a rank is difficult.

First, consider the simplest case — a single word query. In order to rank a document with a single word query, Google looks at that document’s hit list for that word. Google counts the number of hits of each type in the hit list. Then it computes an IR score for the document. Finally, the IR score is combined with PageRank to give a final rank to the document.

For a multi-word search, the situation is more complicated. Now multiple hit lists must be scanned through at once so that hits occurring close together in a document are weighted higher than hits occurring far apart. For every matched set of hits, proximity is computed. Counts are computed not only for every type of hit but for every type and proximity. Thus it computes an IR score. (Refer fig.)

Fig. Google ranking mechanism

Anatomy & Working of Search Engines >>

Introduction to working of search engines >>

Data is stored in number of files on the internet, as ASCII files, binary files or data bases. Search engines may vary on the way the data is stored. If the data is stored as the data base the same can be used to create search engines. For html files, graphics & PDFs search engine is an additional program.

A search engine that doesn’t have a given content, searches it elsewhere. This data comes from a program that crawls many pages and reads the contents. Such a program is called a ROBOT or a SPIDER. It crawls the URLs specified by the search engine and marks when a new one is found. Google.com differentiates the pages that have been crawled and those that have not. The pages that have been crawled display the pages title on the results page. For those which are not crawled it displays the URL of the page.

When an user searches, he is not actually searching the contents. Instead he is searching an index of the content, the spider has found. In a database driven site, the user performs a query on the content.

1. Simple data queries::

When data is stored in a database, simple queries are possible which is a call to database by a middleware program based on user input. This query looks at a selected number of fields in the database. If it finds a match for the input the database returns the information to the middleware program, which generates a useful HTML display of the content that was found.

The database will be indexed for complex queries where it searches the index instead of the content. It also helps in noise word reduction, stemming and look up tables for content mapping.

2. Complex data queries::

Nielson’s recent summary on searching behavior suggests that if users are not successful with their first search they will not improve their search results on a second or third query (Nielsen). Since finding the correct piece of information quickly is important, complex queries are appropriate for keyword searching. They allow the user to ask that a series of conditions about their specific query be met.

3. Boolean searching:

Some search engines allow the user to specify conditions they want to be met in their search results. Boolean searching allows the user to specify groups of words, that should not appear and whether the search should be case sensitive or not. AND or OR can be used to refine the search. These terms are logical expressions included in Boolean searching.
Most search engines allow some or the other form of Boolean searching. It includes syntax for case sensitive searching; but, some data bases store their information in case insensitive fieldtypes.

4. Pre-processed data::

In most search engines the data that the user searches is not the actual pages of information but a dataset of information about what’s contained in the pages. This is called an index. The original content is in a database and index is second dataset.
Content indexing creates a document index that contains information about where each word is found on each page. The user performs a search on this index. The display results page translates the information found in the document index back into the information that is on the actual pages.

5. Indexing content::

Databases are sometimes given to improve performance. A search engine can be improved in terms of speed by using an index. An index is used to strip noise words out of the content.

6. Document index::

A document index is special content index. Most search engines make use of it to get responses for keywords. Information about the words in the documents allows the search engine’s relevancy calculation to return the best result.

7. Noise words::

To save space and time, search engines strip out words when you query the database. Some databases such as MySQL, have noise word rules built in. These general rules can be modified, additional rules can be placed on specific data sets to allow best results. The words that are stripped are called noise words. Noise words may be stripped out based on a specific list of words or length.

Search results display

1. Number of records displayed::
Search results should give relevant information. They can be divided over a number of pages. Nielsen states ‘users almost never look behind the second page of search results’ (Nielsen). Some results may be lost by pagination.

2. Suggesting new spellings::
Sometimes spelling mistakes occur. To provide more chances of relevant search it suggests alternate spellings. Synonym lists present the user with an alternative word. A spellchecker can provide a list of alternate spellings of a word.

3. Hit highlighting::
In the search results page the words you were searching on are sometimes highlighted in some way. Usually the word is bold. This is hit highlighting.

4. Returning results for each successful query::
Often a user will search for multiple keywords. But each page should be returned only once. Instead of each successful result being displayed once, the search engine must know if a page has already been tagged as containing a result. If so, it should not be re-tagged.

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