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Components of Wireless Intelligent Network

What is Wireless Intelligent Network (WIN)

Increasing complexity in telecommunications services requires ever more complex standards, and therefore the need for better means to write them. Today's wireless subscribers are much more sophisticated telecommunications users than they were five years ago. No longer satisfied with just completing a clear call, today's subscribers demand innovative ways to use the wireless phone. They want multiple services that allow them to handle or select incoming calls in a variety of ways. Wireless Intelligent network is developed to drive intelligent network capabilities such as service independence, separation of basic switching functions from service and application functions, and independence of applications from lower-level communication details into wireless networks.

What is it?

Enhanced services are very important to wireless customers. They have come to expect, for instance, services such as caller ID and voice messaging bundled in the package when they buy and activate a cellular or personal communications service (PCS) phone. Whether prepaid, voice/data messaging, Internet surfing, or location-sensitive billing, enhanced services will become an important differentiator in an already crowded, competitive service-provider market. Enhanced services will also entice potentially new subscribers to sign up for service and will drive up airtime through increased usage of PCS or cellular services. As the wireless market becomes increasingly competitive, rapid deployment of enhanced services becomes critical to a successful wireless strategy.
Intelligent network (IN) solutions have revolutionized wire-line networks. Rapid creation and deployment of services have become the hallmark of a wire-line network based on IN concepts. Wireless intelligent network (WIN) will bring those same successful strategies into the wireless networks.
Wireless Intelligent Network (WIN) is a concept being developed by the Telecommunications Industry Association (TIA) Standards Committee TR 45.2. The charter of this committee is to drive intelligent network (IN) capabilities, based on interim standard (IS)-41, into wireless networks. IS-41 is a standard currently being embraced by wireless providers because it facilitates roaming. Basing WIN standards on this protocol enables a graceful evolution to an IN without making the current network infrastructure obsolete.


The main benefit of intelligent networks is the ability to improve existing services and develop new sources of revenue. To meet these objectives, providers require the ability to accomplish the following:

  • Introduce New Services Rapidly: Intelligent network provides the capability to provision new services or modifies existing services throughout the network with physical intervention.
  • Provide Service Customization: Service providers require the ability to change the service logic rapidly and efficiently. Customers are also demanding control of their own services to meet their individual needs.
  • Establish Vendor Independence: A major criterion for service providers is that the software must be developed quickly and inexpensively. To accomplish this, suppliers must integrate commercially available software to create the applications required by service providers.
  • Create Open Interfaces: Open interfaces allow service providers to introduce network elements quickly for individualized customer services. AIN technology uses the embedded base of stored program-controlled switching systems and the SS7 network. The AIN technology also allows for the separation of service-specific functions and data from other network resources. This feature reduces the dependency on switching system vendors for software development and delivery schedules. Service providers have more freedom to create and customize services.

Components of Wireless Intelligent Network

Figure 1: Components of Wireless Intelligent Network

  • Intelligent Peripheral (IP):  The IP gets information directly from the subscriber, be it credit card information, a PIN, voice-activated information. The peripheral gets information, translates it to data, and hands it off to another element in the network-like the SCP-for analysis and control.
  • MSC - The mobile switching center used for the switching function portion of the network.
  • Signal Transfer Point (STP): This is a packet switch in the signaling network that handles the distribution of control signals between different elements in the network such as MSCs and HLRs or MSCs and SCPs. The advantage of an STP is that it concentrates link traffic for the network. It can also provide advanced address capabilities such as global title translation and gateway screening.
  • Signal Transfer Point (STP): This is a packet switch in the signaling network that handles the distribution of control signals between the different elements in the network such as MSC and SCP. The advantage of an STP is that it concentrates link traffic for the network. It can also provide advanced address capabilities such as global title translation and gateway screening.
  • Service Control Point (SCP): This device provides a centralized element in the network that controls service delivery to subscribers. High-level services can be moved away from the MSC and controlled at this higher level in the network. It is cost-effective because the MSC becomes more efficient, does not waste cycles processing new services, and simplifies new service development.
  • MSC as Service Switching Point (SSP):  In the IN, the SSP is the switching function portion of the network. The mobile switching center (MSC) provides this function in the WIN.
  • Visitor location register (VLR): Within an MSC there is a VLR that maintains the subscriber information for visitors or roamers to that MSC. Every MSC or group will have a VLR.
  • Home location register (HLR): Information on roamers is obtained from the subscriber HLR. Each subscriber is associated with a single HLR, which retains the subscriber’s record. When the subscriber roams to another switch, the VLR queries the subscriber's home location register to get information about subscribers.

The Wireless Intelligent Network (WIN) intends to take advantage of the Advanced Intelligent Network (AIN) concept and products developed from wireline communication. However, the progress of AIN deployment hands been slow due to the many barriers that exist in traditional wireline carriers, multiple vendor expertise, and computerized service creation and implementation tools.

Advantages of Wireless Intelligent Network

  • Functions can be developed independently
  • Efficient network utilization
  • Mobility management and radio frequencies (Radio Control Function – RCF)
  • Rapid services creation and deployment
  • Deliver high-volume data
  • Takes less delivery time for services

Disadvantages of Wireless Intelligent Network

  • Implementation cost
  • Lower speed compared to a wired network
  • More complex to configure to the wired network


[1] Amyot, Daniel, and Rossana Andrade, "Description of wireless intelligent network services with Use Case Maps", In SBRC’99, 17th Brazilian Symposium on Computer Networks. 1999.

[2] Mandeep kaur and Pritpal Singh, “Wireless intelligent network (WIN): Primary Weapon for Empowering Telecom Service providers”, International Journal of Advanced Engineering and Global Technology, Volume 04, Issue 03, June 2016

[3] Fatih Ertürk, “Wireless Intelligent Networking (WIN)”, available online at:

[4] S. S. Riaz Ahamed, “Analysis of Wireless Intelligent Network (WIN) for Empowering Providers to Deliver Distinctive Services with Enhanced Flexibility”, Journal of Theoretical and Applied Information Technology, pp. 1054-1058

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Cross Site Request Forgery

What is Cross Site Request Forgery (CSRF/XSRF)

Nowadays, the Internet plays an important role for business people and for commercial use. Everyday life becomes easier for internet users because of the progression in the technologies, but some vulnerability moves the web application into a risky environment. Even though many internet users get increased, the attackers too get increased in balance. So security providence becomes a must in the case of a secure organization, defense personnel, and financial bank that interact with the public. The web has become an indispensable part of our lives. Unfortunately, as our dependency on the web increases, so does the interest of attackers in exploiting web applications and web-based information systems.


Cross-Site Request Forgery is considered one of the top vulnerabilities in today’s web, where an untrusted website can force the user browser to send the unauthorized valid request to the trusted site. Cross-Site Request Forgery will let the integrity of the legitimate user.

Cross‐site request forgery (CSRF; also known as XSRF or hostile linking) is a class of attack that affects web-based applications with a predictable structure for invocation. This class of attack has in some form been known about and exploited since before the turn of the millennium. Cross-site request forgery (CSRF), also known as XSRF, Sea Surf, or Session Riding, is an attack vector that tricks a web browser into executing an unwanted action in an application to which a user is logged in. A successful CSRF attack can be devastating for both the business and the user. It can result in damaged client relationships, unauthorized fund transfers, changed passwords, and data theft—including stolen session cookies.

A Cross-site Request Forgery, aka CSRF or one-click attack, is a diffused security issue where unauthorized commands are sent from the user's browser to a website or a web application. CSRF is different from Cross-Site Scripting in the sense that it does not need to inject code into trusted pages but can work from untrusted ones thanks to the open architecture of the web. A CSRF or XSRF attack can be executed by stealing the identity of an existing user and then hacking into a Web server using that identity. An attacker may also trick a legitimate user into unknowingly sending Hypertext Transfer Protocol (HTTP) requests that return sensitive user data to the intruder.

CSRFs are typically conducted using malicious social engineering, such as an email or link that tricks the victim into sending a forged request to a server. As the unsuspecting user is authenticated by their application at the time of the attack, it’s impossible to distinguish a legitimate request from a forged one. Following are the figure depicting the scenario of a CSRF attack.

Cross Site Request Forgery

Figure 1 Cross Site Request Forgery


CSRF attacks do not appear in the Web Security Threat Classification and are rarely discussed in academic or technical literature.2 CSRF attacks are simple to diagnose, simple to exploit, and simple to fix. They exist because web developers are uneducated about the cause and seriousness of CSRF attacks. Web developers also may be under the mistaken impression that defenses against the better-known Cross-Site Scripting (XSS) problem also protect against CSRF attacks.  For a request to be vulnerable to CSRF, the following conditions must hold:

  • The request can be issued cross-domain, for example using an HTML form. If the request contains non-standard headers or body content, then it may only be issuable from a page that originated on the same domain.
  • The application relies solely on HTTP cookies or Basic Authentication to identify the user that issued the request. If the application places session-related tokens elsewhere within the request, then it may not be vulnerable.
  • The request performs some privileged action within the application, which modifies the application's state based on the identity of the issuing user.
  • The attacker can determine all the parameters required to construct a request that performs the action. If the request contains any values that the attacker cannot determine or predict, then it is not vulnerable.

CSRF Example

In the CSRF attack example below, the data to be changed is contained in a parameter called “EmailAddress”. If the user can be tricked into visiting a website under the attacker’s control, the following code can be used to change the email address stored as a login credential on that site.

The page can be presented as anything: it could be blank, or it could be a replica of the website that’s under attack. All it needs is the code above, which displays an image; this image does not need to exist, and it only covers a 1x1 pixel area, so it does not arouse suspicion. As soon as the user's browser loads the page, the code will automatically submit the request to change the user’s email address. As long as the victim is logged into the website at the time, it will be processed exactly as if the victim had clicked the link.

Even if the website only allows updates via POST, it’s possible to change the email address in the same manner; it just requires some different code:

In both cases, once this is submitted, the email address is automatically changed. Then it’s as simple as using the built-in password-reset facility that most websites have: If this sends the password directly to the registered email address, the password will then be mailed to the attacker and the user account is compromised.

The caveat to mention here is that the user must be logged in to the legitimate website at the time he or she is tricked into visiting the malicious website. However, many sites have a “keep me logged in” facility, which provides a much larger timeframe for the attack.


[1] Zeller, William, and Edward W. Felten. "Cross-site request forgeries: Exploitation and prevention." Bericht, Princeton University (2008).

[2] Sentamilselvan K and Lakshmana Pandian S, “Cross Site Request Forgery: Preventive Measures”, International Journal of Computer Applications (IJCA), Volume 106 – No.11, November 2014.

[3] “Cross-site request forgery: Lessons from a CSRF attack example”, available online at:

[4] “Cross Site Request Forgery (CSRF) Attack”, available online at:

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Modules of a Fingerprint Verification System

Fingerprint Recognition Applications, Advantage and Limitations

Fingerprint identification is one of the most well-known and publicized biometrics. Because of their uniqueness and consistency over time, fingerprints have been used for identification for over a century, more recently becoming automated (i.e. a biometric) due to advancements in computing capabilities. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and their established use and collections by law enforcement and immigration.

What is it?

Fingerprint recognition is one of the most popular and accurate Biometric technologies. Fingerprint recognition (identification) is one of the oldest methods of identification with biometric traits. A large no. of archeological artifacts and historical items shows the signs of fingerprints of humans on stones. The ancient people were aware of the individuality of fingerprints, but they were not aware of scientific methods of finding individuality.

Fingerprints have remarkable permanency and uniqueness throughout time. Fingerprints offer more secure and reliable personal identification than passwords, id-cards or keys can provide. Examples such as computers and mobile phones equipped with fingerprint sensing devices for fingerprint-based password protection are being implemented to replace ordinary password protection methods.

Finger-scan technology is the most widely deployed biometric technology, with a number of different vendors offering a wide range of solutions. Among the most remarkable strengths of fingerprint recognition, we can mention the following:

  • Its maturity provides a high level of recognition accuracy.
  • The growing market of low-cost small-size acquisition devices allows its use in a broad range of applications, e.g., electronic commerce, physical access, PC logon, etc.
  • The use of easy-to-use, ergonomic devices, does not require complex user-system interaction. On the other hand, a number of weaknesses may influence the effectiveness of fingerprint recognition in certain cases:
  • Its association with forensic or criminal applications

State of the Art in Fingerprint Recognition

This section provides a basic introduction to fingerprint recognition systems and their main parts, including a brief description of the most widely used techniques and algorithms.

Modules of a Fingerprint Verification System

Figure 1: Main Modules of a Fingerprint Verification System

The main modules of a fingerprint verification system are: a) fingerprint sensing, in which the fingerprint of an individual is acquired by a fingerprint scanner to produce a raw digital representation; b) preprocessing, in which the input fingerprint is enhanced and adapted to simplify the task of feature extraction; c) feature extraction, in which the fingerprint is further processed to generate discriminative properties, also called feature vectors; and d) matching, in which the feature vector of the input fingerprint is compared against one or more existing templates. The templates of approved users of the biometric system, also called clients, are usually stored in a database. Clients can claim an identity and their fingerprints can be checked against stored fingerprints.

Strengths and Weaknesses of Fingerprint Recognition


  • Proven technology capable of high levels of accuracy
  • Range of deployment environments
  • Ergonomic easy-to-use devices
  • Ability to enroll multiple fingers


  • Inability to enroll some users
  • Performance deterioration over time
  • Association with forensic applications
  • Need to deploy specialized devices

Applications of Fingerprint Recognition

  • Fingerprint recognition is widely used in various applications ranging from law enforcement and international border control to personal laptop access. Almost all law enforcement agencies worldwide routinely collect fingerprints of apprehended criminals to track their criminal history.
  • To enhance border security in the United States, the US-VISIT program acquires fingerprints of visa applicants to identify high-profile criminals on a watch list and detect possible visa fraud. India’s UIDAI project was initiated to issue a unique 12-digit identification number to each resident. Given the large population in India (approximately 1.2 billion), an identification number for an individual is associated with his biometric information (i.e., ten fingerprints and two irises) to ensure that each resident has only one identification number.
  • Fingerprint recognition systems are now pervasive in our daily life. Disney Parks, for example, captures the fingerprints of visitors when they initially enter the park to link the ticket to the ticket holder’s fingerprint.
  • Fingerprint verification is performed whenever the same ticket is presented for reuse to prevent fraudulent use of the ticket (e.g., sharing of a ticket by multiple individuals).
  • Many automated teller machines (ATMs) in Brazil use fingerprint recognition as a replacement for personal identification numbers (PINs).
  • Also, several laptop computer models are equipped with fingerprint sensors and authenticate users based on their fingerprints


[1] “CHAPTER – 2: Introduction to Fingerprint and Face Recognition”, available online at:

[2] Fierrez, Hartwig Fronthaler, Klaus Kollreider, and Javier Ortega-Garcia, "Fingerprint Recognition", pp. 51-90.

[3] Om Sri, Satyasai, and Tatsat Naik, "Study of Fingerprint Recognition System" Btech dissertation, 2011.

[4] Le Hoang Thai and Ha Nhat Tam, “Fingerprint recognition using standardized fingerprint model”, IJCSI International Journal of Computer Science Issues, Volume 7, Issue 3, No 7, May 2010

[5] Soweon Yoon, “Fingerprint recognition: models and applications”, Michigan State University, 2014.

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Fields of Computer Vision

what is Computer Vision and it Applications

Computer vision is the science and technology of machines that see, and seeing, in this case, means that the machine is able to extract from an image some information that is necessary for solving some task. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer vision systems.


The human ability to interact with other people is based on their ability to recognize. This innate ability to effortlessly identify and recognize objects, even if distorted or modified, has induced research on how the human brain processes these images. This skill is quite reliable, despite changes due to viewing conditions, emotional expressions, aging, added artifacts, or even circumstances that permit seeing only a fraction of the face. Furthermore, humans are able to recognize thousands of individuals during their lifetime. Understanding the human mechanism, in addition to cognitive aspects, would help to build a system for the automatic identification of faces by a machine. However, face recognition is still an area of active research since a completely successful approach or model has not yet been proposed to solve the face recognition problem. Automated face recognition is a very popular field nowadays. Face recognition can be used in a multitude of commercial and law enforcement applications. For example, a security system could grab an image of a person and the identity of the individual by matching the image with the one stored on the system database.

Typical tasks of computer vision are:

  • Recognition
  • Motion analysis
  • Scene reconstruction
  • Image restoration

The Difficulty with Computer Vision

At present, a computing machine is not able to actually understand what it sees. This level of comprehension is still a faraway goal for computers, as the ability to understand an image is not just to collect some pixels. The capability to identify an object perfectly is truly incredible

Computers only “see” just a grid of numbers from the camera or from a disk, and that is how far it can go. Those parameters have rather a large noise component, so the profitable information is quite small at the end. Many computer vision problems are difficult to specify, especially because the information is lost in the transformation from the 3D world to a 2D image. Furthermore given a two-dimensional view of a 3D world, there is no unique solution to reconstruct the 3D image. The noise in computer vision is typically dealt with with the use of statistical methods. However, other techniques account for noise or distortions by building explicit models learned directly from the available data.

Fields of Computer Vision

Figure 1: Fields of Computer Vision

The image seen in Figure 1 displays various fields of computer vision which include pattern recognition and image processing. These fields can be considered as abstractly related because usually, advances in one field could potentially lead to advances in other fields as well. Developing a successful face recognition system requires cumulative knowledge from all of these fields.

Computer Vision: Applications

The good news is that computer vision is being used today in a wide variety of real-world applications, which include:

  • Optical character recognition (OCR): reading handwritten postal codes on letters and automatic number plate recognition (ANPR);
  • Machine inspection: rapid parts inspection for quality assurance using stereo vision with specialized illumination to measure tolerances on aircraft wings or auto body parts or looking for defects in steel castings using X-ray vision;
  • Retail: object recognition for automated checkout lanes;
  • 3D model building (photogrammetric): fully automated construction of 3D models from aerial photographs used in systems such as Bing Maps;
  • Medical imaging: registering pre-operative and intra-operative imagery or performing long-term studies of people’s brain morphology as they age;
  • Automotive safety: detecting unexpected obstacles such as pedestrians on the street, under conditions where active vision techniques such as radar or lidar do not work well.
  • Match move: merging computer-generated imagery (CGI) with live-action footage by tracking feature points in the source video to estimate the 3D camera motion and shape of the environment. Such techniques are widely used them also require the use of precise matting to insert new elements between foreground and background elements.
  • Motion capture (MOCAP): using retro-reflective markers viewed from multiple cameras or other vision-based techniques to capture actors for computer animation;
  • Surveillance: monitoring for intruders, analyzing highway traffic, and monitoring pools for drowning victims;
  • Fingerprint recognition and biometrics: for automatic access authentication as well as forensic applications.


[1] Bradski, G. and Kaehler, A. 2008, Learning OpenCV: Computer Vision with the OpenCV Library. Sebastopol: O’Reilly.

[2] Bambach, S, A survey on recent advances of computer vision algorithms for egocentric video. arXiv preprint arXiv:1501.02825, 2015.

[3] Chuang, Y.-Y., Agarwala, A., Curless, B., Salesin, D. H., and Szeliski, R. (2002), Video matting of complex scenes, ACM Transactions on Graphics (Proc. SIGGRAPH 2002), 21(3):243–248.

[4] Richard Szeliski, “Computer Vision: Algorithms and Applications”, September 3, 2010 draft 2010 Springer.

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A Thumb for Biometric Verification

What is Biometrics Authentication, Types and Applications

One of our highest priorities in the world of information security is confirmation that a person accessing sensitive, confidential, or classified information is authorized to do so. Such access is usually accomplished by a person’s proving their identity by the use of some means or method of authentication. Biometrics Authentication is a field of technology that has been and is being used in the identification of individuals based on some physical attribute. Biometric Authentication is used for automatic personal recognition based on biological traits—fingerprint, iris, face, palm print, hand geometry, vascular pattern, voice or behavioral characteristics gait, signature, and typing pattern. Fingerprinting is the oldest of these methods and has been utilized for over a century by law enforcement officials who use these distinctive characteristics to keep track of criminals.

Basic Overview

In this computer-driven era, identity theft and the loss or disclosure of data and related intellectual property are growing problems. We each have multiple accounts and use multiple passwords on an ever-increasing number of computers and Web sites. Maintaining and managing access while protecting both the user's identity and the computer's data and systems has become increasingly difficult. Central to all security is the concept of authentication - verifying that the user is who he claims to be.

Biometrics Authentication seems to be everywhere these days. Consumer preference has turned the technology into a must-have for the modern smartphone or laptop. Biometric authentication is a security process that relies on the unique biological characteristics of an individual to verify that he is who is says he is. Biometric authentication systems compare a biometric data capture to stored, confirmed authentic data in a database. If both samples of the biometric data match, authentication is confirmed. Typically, biometric authentication is used to manage access to physical and digital resources such as buildings, rooms, and computing devices.

A Thumb for Biometric Verification

Figure 1: A Thumb for Biometric Verification

Generally speaking, there are four factors of physical attributes that are used or can be used in user authentication:

  • Fingerprint scans, have been in use for many years by law enforcement and other government agencies and are regarded as a reliable, unique identifier.
  • Retina or iris scans have been used to confirm a person’s identity by analyzing the arrangement of blood vessels in the retina or patterns of color in the iris
  • Voice recognition, uses a voice print that analyses how a person says a particular word or sequence of words unique to that individual

There are seven basic criteria for biometric security systems: uniqueness, universality, permanence, collectability, performance, acceptability, and circumvention.

Criteria of Biometric Security

Figure 2 Criteria of Biometric Security

Types of Biometrics

A number of biometric methods have been introduced over the years, but few have gained wide acceptance.

Signature dynamics: Based on an individual's signature, but considered unforgeable because what is recorded isn't the final image but how it is produced -- i.e., differences in pressure and writing speed at various points in the signature.

Typing patterns: Similar to signature dynamics but extended to the keyboard, recognizing not just a password that is typed in but the intervals between characters and the overall speeds and pattern. This is akin to the way World War II intelligence analysts could recognize a specific covert agent's radio transmissions by his "hand" -- the way he used the telegraph key.

Eye scans: This favorite of spy movies and novels presents its own problems. The hardware is expensive and specialized, and using it is slow and inconvenient and may make users uneasy.

In fact, two parts of the eye can be scanned, using different technologies: the retina and the iris

Fingerprint recognition: Everyone knows fingerprints are unique. They are also readily accessible and require little physical space either for the reading hardware or the stored data.

Voice recognition: This is different from speech recognition. The idea is to verify the individual speaker against a stored voice pattern, not to understand what is being said.

Facial recognition: Uses distinctive facial features, including upper outlines of eye sockets, areas around cheekbones, the sides of the mouth, and the location of the nose and eyes. Most technologies avoid areas of the face near the hairline so that hairstyle changes won't affect recognition.

Biometrics Authentication Applications

Biometric technology can be used for a great number of applications. Chances are if security is involved, biometrics can help make operations, transactions, and everyday life both safer and more convenient. Here you will find a list of the many areas of deployment for biometrics and the companies that provide applicable identity solutions

Biometric Security

Biometric Security As connectivity continues to spread across the globe, it is clear that old security methods are simply not strong enough to protect what’s most important. Thankfully, biometric technology is more accessible than ever before, ready to bring enhanced security and greater convenience to whatever needs protecting, from a door to your car to…

  • Border Control/Airports

Border Control and Airport Biometrics A key area of application for biometric technology is at the border. Anyone who’s traveled by air can tell you security checkpoints and border crossings are some of the most frustrating places to have to move through. Thankfully, biometric technology is helping automate the process.

  • Consumer/Residential Biometrics

Consumer and Residential Biometrics Recent innovations in mobility and connectivity have created a demand for biometrics in the homes and pockets of consumers. Smartphones with fingerprint sensors, apps that allow for facial and voice recognition, and mobile wallets: are the increasingly popular ways that consumers around the world are finding biometrics in their lives.

  • Fingerprint & Biometric Locks

Fingerprint Biometric Locks If you have something worth protecting why not give it the star treatment? Biometric physical access control solutions are stronger authentication methods than keys, key cards, and PINs for a simple reason: they’re what you are, not what you have.

  • Healthcare Biometrics

Biometrics in Healthcare Biometrics bring security and convenience wherever they’re deployed, but in some instances, they also bring increased organization. In the field of healthcare, this is particularly true. Health records are some of the most valuable personal documents out there, doctors need access to them quickly, and they need to be accurate.


[1] Tanuj Tiwari, Tanya Tiwari, and Sanjay Tiwari, "Biometrics Based User Authentication"

[2] Russell Kay, “Biometric Authentication”, available online at:

[3] “Applications”, available online at:

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Bioinformatics Field

What is Bioinformatics and it’s application

Most biological research involves the application of some type of mathematical, statistical, or computational tools to help synthesize recorded data and integrate various types of information. In this process, the answer to a particular biological question has been investigated. Bioinformatics involves the use of computers to collect, organize and use biological information to answer questions in fields of biological research.

Bioinformatics is an interdisciplinary research area at the interface between computer science and biological science. A variety of definitions exist in the literature and on the World Wide Web; some are more inclusive than others. Bioinformatics involves the technology that uses computers for storage, retrieval, manipulation, and distribution of information related to biological macromolecules such as DNA, RNA, and proteins. The emphasis here is on the use of computers because most of the tasks in genomic data analysis are highly repetitive or mathematically complex. The use of computers is absolutely indispensable in mining genomes for information gathering and knowledge building. Bioinformatics is the science of developing computer databases and algorithms for the purpose of speeding up and enhancing biological research. It can be defined more specifically,

“Bioinformatics combines the latest technology with biological research.”


Bioinformatics involves the integration of computers, software tools, and databases in an effort to address biological questions. Bioinformatics approaches are often used for major initiatives that generate large data sets. Two important large-scale activities that use bioinformatics are genomics and proteomics. Genomics refers to the analysis of genomes. A genome can be thought of as the complete set of DNA sequences that code for the hereditary material that is passed on from generation to generation.

These DNA sequences include all of the genes (the functional and physical unit of heredity passed from parent to offspring) and transcripts (the RNA copies that are the initial step in decoding the genetic information) included within the genome. Thus, genomics refers to the sequencing and analysis of all of these genomic entities, including genes and transcripts, in an organism. Proteomics, on the other hand, refers to the analysis of the complete set of proteins or proteomes. In addition to genomics and proteomics, there are many more areas of biology where bioinformatics is being applied. Each of these important areas in bioinformatics aims to understand complex biological systems.

Bioinformatics Field

Figure 1: Bioinformatics Field

Bioinformatics Goals

The ultimate goal of bioinformatics is to better understand a living cell and how it functions at the molecular level. By analyzing raw molecular sequence and structural data, bioinformatics research can generate new insights and provide a “global” perspective of the cell. The reason that the functions of a cell can be better understood by analyzing sequence data is ultimate that the flow of genetic information is dictated by the “central dogma” of biology in which DNA is transcribed to RNA, which is translated to proteins. Cellular functions are mainly performed by proteins whose capabilities are ultimately determined by their sequences. Therefore, solving functional problems using sequence and sometimes structural approaches have proved to be a fruitful endeavor.


Bioinformatics has not only become essential for basic genomic and molecular biology research. This has a major impact on many areas of biotechnology and biomedical. It has applications, like in knowledge-based drug design, forensic DNA analysis, and agricultural biotechnology. Studies of protein-ligand interactions provide a basis for the rapid identification of novel synthetic drugs. Knowledge of the three-dimensional structures of proteins allows the designing of a target protein with great affinity and specificity. This approach significantly reduces the time and cost to develop drugs. In forensics, the results of analysis have been accepted as evidence in courts. Some Bayesian statistics and likelihood-based methods are used for the analysis of DNA in forensics.


[1] Jin Xiong, “Essential bioinformatics” Cambridge University Press, 2006

[2] joannefox, “What Is Bioinformatics?”, available online at:

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All Around of Big Data Analytics

What is Big Data Analytics

In the digital world, data are generated from various sources and the fast transition from digital technologies has led to the growth of big data. It provides revolutionary breakthroughs in many fields with the collection of large datasets A huge repository of terabytes of data is generated each day from modern information systems and digital technologies such as the Internet of Things and cloud computing. Analysis of these massive data requires a lot of effort at multiple levels to extract knowledge for decision-making. Therefore Big data analytics examines large amounts of data to uncover hidden patterns, correlations, and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient than more traditional business intelligence solutions.


The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get important value from it. But even in the 1950s, decades before anyone expressed the term “big data,” businesses were using basic analytics (essential numbers in a spreadsheet that were manually examined) to uncover insights and trends.

Artificial Intelligence (AI), mobile, social, and Internet of Things (IoT) are driving data complexity, new forms, and sources of data. Big data analytics is the use of advanced analytic techniques against very large, different data sets that include structured, semi-structured, and unstructured data, from different sources, and in different sizes from terabytes to zetta-bytes.

It allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independently or together with their existing enterprise data to gain new insights resulting in better and faster decisions. The new benefits that bring to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics, and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

All Around of Big Data Analytics

Figure 1: All-Around of Big Data Analytics

Defining Big Data Analytics

Big Data analytics is the process of collecting, organizing, and analyzing large sets of data (called Big Data) to discover patterns and other useful information. It can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with Big Data typically want the knowledge that comes from analyzing the data.

Companies and enterprises that implement it often reap several business benefits, including more effective marketing campaigns, the discovery of new revenue opportunities, improved customer service delivery, more efficient operations, and competitive advantages. Companies implement it because they want to make more informed business decisions. It gives analytics professionals, such as data scientists and predictive modelers, the ability to analyze Big Data from multiple and varied sources, including transactional data and other structured data.


Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits, and happier customers. In his report Big Data in Big Companies, found the effective importance mentioned below:

Advantages of Big Data Analytics

Figure 2 Advantages

  • Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
  • Faster, better decision-making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
  • New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that, more companies are creating new products to meet customers’ needs


Think of a business that relies on quick, agile decisions to stay competitive, and most likely analytics is involved in making that business tick. Here’s how different types of organizations use the big data analytics:

Travel and Hospitality

Keeping customers happy is the key to the travel and hotel industry, but customer satisfaction can be hard to gauge – especially in a timely manner. Resorts and casinos, for example, have only a short window of opportunity to turn around a customer experience that’s going south fast. Analytics gives these businesses the ability to collect customer data, apply analytics and immediately identify potential problems before it’s too late.


Certain government agencies face a big challenge: tighten the budget without compromising quality or productivity. This is particularly troublesome with law enforcement agencies, which are struggling to keep crime rates down with relatively scarce resources. And that’s why many agencies use big data analytics; the technology streamlines operations while giving the agency a more holistic view of criminal activity.


Customer service has evolved in the past several years, as savvier shoppers expect retailers to understand exactly what they need and when they need it. Analytics technology helps retailers meet those demands. Armed with endless amounts of data from customer loyalty programs, buying habits, and other sources, retailers not only have an in-depth understanding of their customers, they can also predict trends, recommend new products – and boost profitability.

Health Care

Big data is a given in the health care industry. Patient records, health plans, insurance information, and other types of information can be difficult to manage – but are full of key insights once analytics are applied. That’s why analytics technology is so important to health care. By analyzing large amounts of information – both structured and unstructured – quickly, health care providers can provide lifesaving diagnoses or treatment options almost immediately.


[1] D. P. Acharjya and Kauser Ahmed P, “A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, 2016

[2] What is Big Data Analytics? IBM Analytics, available online at:

[3] Big Data Analytics: What it is and why it matters, available online at:

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What is Automatic Speech Recognition

Speech is a versatile means of communication. It conveys linguistic speaker and environmental information. Even though such information is encoded in a complex form, humans can relatively decode most of it. Among all speech tasks, automatic speech recognition (ASR) has been the focus of many researchers for several decades. In this task, the linguistic message is the information of interest. Speech recognition applications range from dictating a text to generating subtitles in real-time for a television broadcast. Despite the human ability, researchers learned that extracting information from the speech is not a straightforward process.


Speech recognition has in years become a practical concept, which is now being implemented in different languages around the world. Speech recognition has been used in real-world human language applications, such as information recovery. Speech in humans can be said as the most common means of communication because the information maintains the basic role in the conversation. The conversation or speech that is captured by a microphone or a telephone is converted from an acoustic signal to a set of words in speech recognition. It can be defined:

“Automatic speech recognition (ASR) can be defined as the independent, computerdriven transcription of spoken language into readable text in real-time.”

Automatic speech recognition is primarily used to convert spoken words into computer text. Additionally, automatic speech recognition is used for authenticating users via their voice (biometric authentication) and performing an action based on the instructions defined by the human. Typically, automatic speech recognition requires preconfigured or saved voices of the primary user(s). Human needs to train the automatic speech recognition system by storing speech patterns and vocabulary of their in the system.

Automatic speech recognition (ASR) systems convert speech from a recorded audio signal to text. Humans convert words to speech with their speech production mechanism. An ASR system aims to infer those original words given the observable signal.

Speech Recognition System

Figure 1: Speech Recognition System

Speech is naturally dynamic in nature. Acoustic model is used to model the statistics of speech features for each speech unit of the language such as a phone or a word. Figure 1 shows the basic block diagram of a speech recognition system. As can be seen from Figure 1, acoustic models are required to analyze the speech feature vectors for their acoustic content

ASR Challenges

Speech analytics market is also expected to grow owing to the growth in automatic speech recognition market. Speech analytics also known as audio mining are widely used to formulate meaning from the captured words. Better decisions for operational and strategic issues are expected to be solved by the study of voice.

Inaccuracy in ASR systems is one of the biggest challenges faced by speech-based biometrics industry. Reduced accuracy level due to surrounding noise serves as a significant disadvantage to highly sensitive voice recognition applications. The hassle of ASR systems being highly sensitive poses a key challenge to the acceptance of such sensitive applications.

The lack of efficient I.T. infrastructure is expected to hinder the overall market growth. Further, the lack of knowledge and ability to adopt new technology by some organizations is anticipated to restrain industry growth.

Voice recognition broadly utilizes front-end and back-end techniques. Front-end techniques are plagued by the challenge of time and accuracy. However, owing to high speed and precision, back-end recognition techniques are widely used. Back-end techniques are expected to handle noise-generated errors and disturbances. This system also needs to detect low pitch sound and thus is highly sensitive

How Does ASR Work?

The goal of an ASR system is to accurately and efficiently convert a speech signal into a text message transcription of the spoken words independent of the speaker, environment, or the device used to record the speech (i.e. the microphone). This process begins when a speaker decides what to say and actually speaks a sentence.  The software then produces a speech waveform, which embodies the words of the sentence as well as the extraneous sounds and pauses in the spoken input.  Next, the software attempts to decode the speech into the best estimate of the sentence. First, it converts the speech signal into a sequence of vectors which are measured throughout the duration of the speech signal. Then, using a syntactic decoder generates a valid sequence of representations.


ASR is an application that consistently exploits advances in computation capabilities. With the availability of a new generation of highly parallel single-chip computation platforms, ASR researchers are faced with the question of unlimited computing to make speech recognition better

There are fundamentally three major reasons why so much research and effort has gone into the problem of trying to teach machines to recognize and understand speech:

  • Accessibility for the deaf and hard of hearing
  • Cost reduction through automation
  • Searchable text capability


[1] What is Automatic Speech Recognition? Available online at:

[2] “Chapter 2: Automatic Speech Recognition”, available online at: file:///C:/Users/maxtech-10/Downloads/9783642195853-c2.pdf

[3] Adami, André Gustavo. "Automatic speech recognition: From the beginning to the portuguese language", In The International Conference on Computational Processing of Portuguese (PROPOR), 2010.

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panna national park

Panna National Park Madhya Pradesh

Panna National Park is a town and a municipality in the Panna district in the Indian state of Madhya Pradesh. Panna was the capital of Chhatar Sal, the Bundela Rajput leader who led a revolt against the Mughal Empire. Upon his death in 1732, his kingdom was divided among his sons. Panna is famous for its diamond mines in India, Panna has Rolling Meadows dotted with evergreen trees, rocks, hills, and forests. Panna is famous for its temples which strike a very fine blend of Hindu and Muslim architecture. With a sanctuary for rare wildlife and avifauna and a diamond mine, Panna has been transformed into a city of natural beauty and eternal serenity.

Top Things To Do In Panna

Tiger Reserve

Panna has a tiger reserve which is also called Panna National Park. The sightings of tigers in Panna have fallen over recent years, and official tiger population figures were disputed by naturalists. There were plans to relocate two tigresses to Panna in 2009, which actually happened, but the last male tiger meanwhile disappeared. A male tiger was relocated there. One of the relocated tigresses gave birth to three cubs in 2010. The reserve is home to a wide variety of other animals, many of which can be seen at closer quarters than in other reserves because Panna has fewer visitors. There are jungle lodges and hotels near the reserve, it can also be reached from Khajuraho. Raneh fall and Pandav fall are also famous visiting spots for tourists during monsoon.

panna national park

Figure 1 panna national park (Taken from)

Diamond Mines, Panna

Asia's biggest and only active diamond mine is situated in Madhya Pradesh due to this it is also known as treasure land. A tour of the Panna diamond mine will answer all the mysteries about diamond farming. Asia's only active diamond mine is situated in Majhagaon about 55km away from Khajuraho in Madhya Pradesh. It is located in the interior of the Panna district. The mine is spread over an area of 80km belt, starting from the Paharikhera North-East to Majhgawan South-West with a breadth of around 30 kms. The mines are under the supervision of the Diamond Mining Project of the National Mineral Development Corporation (NMDC Ltd) of the Government of India. Tour to a diamond mine is a sparkling experience. Exploration of the diamond mine will answer the individual about the qualities of diamonds and how they get their structure.

Jugal Kishoreji Temple

This is the most important Hindu Temple in the town of Panna. The structure of the temple is inspired by the typical Bundela temple style. It is believed that a pilgrimage of the four Dhams is considered incomplete if it does not conclude with a visit to the Jugal Kishoreji Temple.

Raneh Falls and the Ken crocodile sanctuary, Panna

The Raneh Falls are formed when the Ken river falls from the canyons of the Rewa Plateau about 20 km from Khajuraho. The Ken Crocodile sanctuary is in the vicinity of the Raneh Falls itself.

Pandava Falls and Caves, Panna

Located within the territory of the Panna National Park, as the name suggests this place is where the Pandavas sought haven during their exile. Mythology enthusiasts, rejoice! This location is definitely going to leave you in awe, with the waterfalls, the deep lake, and the lush green environs making it a perfect setting for your rendezvous with serenity.

How To Reach Panna

By Air: Khajuraho is the nearest airport to Panna (45 km). There are regular air links from Khajuraho to New Delhi, Bhopal, and a few other cities.

By Road: A well-maintained network of roads links Panna with the rest of the country. Distance Chart (Khajuraho - 45 km, Bhopal - 450 km, Delhi - 670 km).

By Rail: The nearest Railway station is Satna and Khajuraho which is directly linked to Bhopal, Jabalpur, and Delhi. The distance between Panna to Satna and Khajuraho is only about 70 km and 45 km respectively.





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Pachmarhi : Important places to visit

Satpura ki Rani Pachmarhi : Important places to visit

Pachmarhi is a hill station in the Hoshangabad district of Madhya Pradesh state. It is widely known as Satpura ki Rani ("Queen of Satpura"), situated at a height of 1067 m in a valley of the Satpura Range. The name Pachmarhi is believed to be derived from the Hindi words Panch ("five") and Marhi ("caves"). According to a legend, these caves were built by five Pandava brothers of Mahabharatha during their thirteen years of exile.

Places of tourist interest

Tourists visit Pachmarhi throughout the year. Pachmarhi has numerous nature spots, greenery, scenic views, waterfalls, mountain streams, and rare wildlife.

Pachmarhi : Important places to visit

  1. Jata Shankar:

Jatashankar is a natural cave, a Hindu shrine located north of Pachmarhi. The cave is located in a deep ravine, with enormous boulders perched above. The cave contains stalagmites revered as naturally formed lingams. Cave serves as a shrine to the God Shiva and is a popular destination for pilgrims. Jata means hair and Shankar is another name for Lord Shiva. There are two different types of the little pond, fed by springs, found in the locality, one of cold water and the other one of hot water.

  1. Dhoopgarh

The highest point in the Satpura range, the Dhoopgarh hilltop is a beautiful spot to see marvelous sunsets and sunrises. However, this point can only be reached by trekking. The trekking route is relatively tough as it passes through some waterfalls and valleys.

  1. Gupt Mahadev

It is a Shiva Temple, in a very narrow cave. One can enter the cave from one side and there is a very small place inside the cave where shivling is there for worship.

  1. Chauragarh

Chauragarh is the second highest peak. It is a pilgrimage site with Lord Siva's temple at the top. There is a Chauragarh fort there built by the king Sangram Shah of the Gond Dynasty. It is also a very well-known spot for sunrise viewing. The sun rising scene seen here is breathtaking and climbing 1300 steps to reach the point makes it worth it. Surrounded by dense forest and verdant valleys, the Chauragarh or Chota Mahadeo Temple is perched on the Chauragarh Peak, which is one of the most revered shrines of Lord Shiva in the Pachmarhi region. The temple is situated at a mighty height, and one needs to climb 1250 steps to get to the top. After the arduous climb, devotees are charmed with thousands of Trishul stacked in the temple courtyard.

  1. Pandava caves

Though Pandava caves are proudly associated with Mahabharata, many archaeologists contradict the fact. They believe that these caves belong to the Gupta period (9th or 10th century AD) and even predict their construction by Buddhist monks. Traces of an ancient brick-built stupa have been unearthed on top of the caves, which dates back these caves to the Buddhist period. Archeologists believe the stupa is a remnant of the regime of King Ashoka.

  1. Bee Fall

Bee Fall is a popular water-fall located at Pachmarhi Hill Station. There is a number of waterfalls in Pachmarhi but among them, Bee Fall is considered as most popular one and a must-visit tourist point. Pachmarhi is a popular tourist destination among domestic tourists. Here we can find tourism activities throughout the year. It is a natural waterfall deep below the valley, reached with the help of a jeep to some distance then footsteps. There are many waterfalls in Pachmarhi, but no one is like Bee falls, as the waters of a perennial stream tumble down 35 meters, giving a spectacular view to all those nature lovers who come here, there are many waterfalls around Pachmarhi, but the beauty of falling a narrow stream of water from a great height, and when it looks like falling melting silver, this all makes Bee Falls alike to other.

  1. Priyadarshini Point

This is the point from where Captain James Forsyth discovered Pachmarhi in the year 1857. It was only after this that Pachmarhi was recognized as a hill station and a resort. Priyadarshini's point gives an eagle's eye view of the entire hill station and its serene landscape.

  1. Reechhgarh

Situated some 6 km away from Pachmarhi is a natural cavern that has opening at both its ends. One has to scramble up a boulder to enter the cave and to exit from its other end, one should be willing to enter a narrow canyon wherefrom once a stream must have snaked through. Visiting this place is an adventure in itself and it does make it one of the top places to visit near Pachmarhi.

  1. Apsara Falls

On the way to Rajat Prapat, Apsara is a small cascade whose water accumulates in a pond. This place is ideal to stop by for some quiet time before making it to the most visited tourist place, Bee Fall in Pachmarhi.

  1. Handi Khoh

It is a point en route to Chauragarh that offers an incredible view of a 100m gorgeous gorge with 300 m towering cliffs. One can also see Chauragarh in distance along with Priyadarshini or Forsyth Point from here. According to the locals, there was once a lake here which dried off and this place came to know as Andhi Khoh, which later turned to Handi Khoh, due to its shape. The verdant gorge is home to a large number of bees and a small ravine whose sound can be heard amidst perfect silence. This place is ideal for nature lovers and those seeking some solitude.

How to reach Pachmarhi

by Air

Bhopal and Jabalpur airport serves as the nearest airports to Pachmarhi. Visitors can avail of direct flights to these cities from Delhi and Indore. Otherwise connecting flights to Bhopal or Jabalpur from other Indian cities including Raipur, Hyderabad and Ahmedabad can be availed. From the airport, buses or cabs are available for Pachmarhi.

by Road

Plenty of state government and private buses are available for Pachmarhi from nearby cities like Bhopal, Jabalpur, Nagpur, Indore, and attractions like Kanha National Park and Pench National Park. Being a cantonment town, the road condition here is quite good. Visitors must note that even if they are traveling by train or flight, the last journey till Pachmarhi has to be done by road.

by Rail

Visitors have to board trains to Pipariya railway station, the nearest railway station to Pachmarhi. Quite a number of direct trains link Pipariya with important cities like Kolkata, Jabalpur, Agra, Gwalior, Delhi, Ahmedabad, Varanasi, Nagpur, etc. In case one cannot find a direct train till Pipariya, a train till Itarsi railway station can be boarded.






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