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How to Become a Microst Student Patner?


Atul Purohit
 On 13th April we had a guest lecture bt we where nt informed that who is going to come ....
i was thinkng might be some IIT professor or lecturer .... but wen i reached to the seminar hall i was surprised seeing the banner of Microsoft Student Partners........
Oh i thought i might be just a lecture on microsoft produts and technology ......
ya i was right bt partly, because they had lecture on products and technology of microsoft, which is going to be used in the future.... ya u cn see wat is the future .... for morein detail about the technlogy like windows 8 the latest one which is going to be using can read @ my blog Best Hacking News & Updates. 
ok lets leave that u keep reading that latter.. as this event was conducted by the members of Microsoft DreamSpark Yatra who r also the part of Microsoft Student Partner and have been working under it since few years and also conducting events and competition for various institute and colleges.
 so they told us about the msp(microsoft student partner) program and how to apply to it.
and at last they gave us certificate of Microsoft DreamSpark Yatra 2011
and along with that the license key, which is used for downloading all the latest software of microsoft for free for limited period which includes the software like office10, windows7 & and other genuine microsoft softwares for free as we are now the part of Microsoft Student Partner.
Here are some snaps for the event launched and the certification that i got:-
       
 
I am a 2nd year E.C.E Engineering student from India & i m intrested to become a Microsoft Student Partner ( MSP ). Because when i saw them i.e. MSPs Conducting various events related to Microsoft Products,their knowledge,Position,etc i got exicted wen the event was carried in our college AITS by the members of Microsoft DreamSpark Yatra!! But most of us do not know How to Become a Microsoft Student Partner MSP ? So here is golden oppurtunity for Students to become a Microsoft Student Partner ( MSP ). You can apply for the MSP Program 2011-2012.  



How to Become a Microsoft Student Partner ( MSP ) 

Most of the my friends ask me about How to Become a Microsoft Student Partner ( MSP ), or generally there is a discussion about this great Program by microsoft. So you can become a Microsoft Student Partner ( MSP ) 2011-2012. Just Check out the below -  

MSP Program India
 
Program Overview-- The Microsoft Student Partner Program recognizes top young minds from around the world that are passionate about technology. It's a once in a lifetime opportunity to develop real-world skills to help you succeed in your future career, to help others learn about the technology of today and tomorrow, and to connect with other like-minded students, all whilst having a ton of fun along the way. The program is our way of encouraging students who are interested in building a closer relationship with Microsoft, and those who are simply hooked on technology, to develop their skills further.

What is a Microsoft Student Partner?The ‘ideal candidate’ would be a passionate and enthusiast individual who wants to learn about new tools and technologies. You would need to have a whole range of skills including excellent time management, organization and communication skills to ensure that you could host successful campus events. An MSP should be comfortable and confident presenting in front of large audiences of both students and faculty members. General business and marketing skills come in very handy in order to allow you to articulate your ideas effectively when presenting. MSP's are social, friendly and approachable individuals who like to meet new people. You will require the ability work as a team as well as use your own initiative. In summary, MSP's are innovative and creative students who are extremely passionate about technology.

How to apply? Applications for the 2011-2012 Academic Year will be accepted from April 2011 onwards. To apply for the MSP India program, please refer to the selection criteria below.

Contact Us For all questions regarding the MSP India program, please email newmsp@microsoft.com.
MSP India Program
2011-2012 Academic Year Selection Process

The selection process consists of multiple rounds that will begin in April 2011.
EligibilityTo consider applying for the MSP Program, you must be:
  • Over 17 years of age. 
  • Studying a full-time course at an officially recognized University/College in India.
  • Bachelor's/Master's Degree student who will complete the course during or after May 2012.
Competencies A good MSP is one who has the following basic qualities:
  • Technical competencies
  • Passionate about software
  • Quick learner
  • Respected by peers
Community-building competencies
  • Enthusiastic about technology
  • High level of social activity, both online & offline
  • Can organize college and city-level events
Fundamental competencies
  • Passionate about Microsoft
  • Confident & outgoing
  • Good rapport with faculty
  • Willing to share knowledge & eager to uplift self and peers
ResponsibilitiesYour short term goals will include:
  • Conduct at least 1 technical session per month in a Student Tech Club.
  • Participate and drive entries for Imagine Cup
  • Maintain a technology-related blog
Your long term goals will include:
  • Promote and build your city-level Microsoft Student User Group
  • Organize city-level events like Academic Developers’ Conference (DevCon)
  • Deploy &/or maintain Live@Edu for your college
  • Mentor other MSPs
BenefitsAs an MSP, a host of benefits are available:
  • Welcome letter
  • Exclusive MSP events conducted by Microsoft
  • MSDN subscription after completion of probation period
  • Rewards & Recognition for top performers
  • Networking opportunities
  • Technical training & resources
  • Specific Microsoft events
  • Interactions with MVPs & Microsoft Employees
  • Internship & Recruitment announcements for top-performers
  •  
To consider applying for the MSP Program, you must be over 17 years of age and be be studying a full-time course at an officially recognized University/College.
You can visit the Microsoft Students Partner website, select you country from the drop-down menu and get the required information.
US residents can fill out the application forms here and here as required. More at TechNet. UK residents may want to visit this website. Students from India may go here. [Chance to Become a Microsoft Student Partner ( MSP ),How to Become a Microsoft Student Partner ( MSP ) ]


Posted by @Atul Purohit

Category: , 2 comments

Biomatrics


Atul Purohit
Ohh!!, Today we had Guest Lecture on BIOMETRICS by the Professor From IIT Roorkee.
 They gave us a brief introduction on BIOMETRICS with slide presentation the Devices  and the Technology us in Biometrics.

       

 Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. In computer science, in particular, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance.

Biometric characteristics can be divided in two main classes:-

•    Physiological are related to the shape of the body. Examples include, but are not limited to fingerprint, face recognition, DNA, Palm print, hand geometry, iris recognition, which has largely replaced retina, and odour/scent.

•    Behavioral are related to the behavior of a person. Examples include, but are not limited to typing rhythm, gait, and voice. Some researchers have coined the term behaviometrics for this class of biometrics. 

Strictly speaking, voice is also a physiological trait because every person has a different vocal tract, but voice recognition is mainly based on the study of the way a person speaks, commonly classified as behavioral.

           


The basic block diagram of a biometric system

It is possible to understand if a human characteristic can be used for biometrics in terms of the following parameters:

•            Universality – each person should have the characteristic.

 
•            Uniqueness – is how well the biometric separates                     individuals from another.

 
•            Permanence – measures how well a biometric resists                 aging and other variance over time.

 
•            Collectability – ease of acquisition for measurement.

 
•            Performance – accuracy, speed, and robustness of                  technology used.

 
•            Acceptability – degree of approval of a technology.

 
•            Circumvention – ease of use of a substitute.
               A biometric system can operate in the following two


•             Verification – A one to one comparison of a captured                    biometric with a stored template to verify that the                    individual is who he claims to be. Can be done in                    conjunction with a smart card, username or ID number.

•             Identification – A one to many comparison of the                    captured biometric against a biometric database in attempt                to identify an unknown individual. The identification only                  succeeds in identifying the individual if the comparison of                  the biometric sample to a template in the database falls                  within a previously set threshold.

 
               The first time an individual uses a biometric system is called an enrollment. During the enrollment, biometric information from an individual is stored. In subsequent uses, biometric information is detected and compared with the information stored at the time of enrollment. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is to be robust. The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the times it is an image acquisition system, but it can change according to the characteristics desired. The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc. In the third block necessary features are extracted. This step is an important step as the correct features need to be extracted in the optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is a synthesis of the relevant characteristics extracted from the source. Elements of the biometric measurement that are not used in the comparison algorithm are discarded in the template to reduce the filesize and to protect the identity of the enrolled.

 
               If enrollment is being performed, the template is simply stored somewhere (on a card or within a database or both). If a matching phase is being performed, the obtained template is passed to a matcher that compares it with other existing templates, estimating the distance between them using any algorithm (e.g. Hamming distance). The matching program will analyze the template with the input. This will then be output for any specified use or purpose (e.g. entrance in a restricted area).

          

Performance

The following are used as performance metrics for biometric systems:[3] just a trial

•        false accept rate or false match rate (FAR or FMR) – the probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs which are incorrectly accepted.


•        false reject rate or false non-match rate (FRR or FNMR) – the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs which are incorrectly rejected.


•        Receiver operating characteristic or relative operating characteristic (ROC) – The ROC plot is a visual characterization of the trade-off between the FAR and the FRR. In general, the matching algorithm performs a decision based on a threshold which determines how close to a template the input needs to be for it to be considered a match. If the threshold is reduced, there will be less false non-matches but more false accepts. Correspondingly, a higher threshold will reduce the FAR but increase the FRR. A common variation is the Detection error trade-off (DET), which is obtained using normal deviate scales on both axes. This more linear graph illuminates the differences for higher performances (rarer errors).


•    equal error rate or crossover error rate (EER or CER) – the rate at which both accept and reject errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is most accurate.


•    failure to enroll rate (FTE or FER) – the rate at which attempts to create a template from an input is unsuccessful. This is most commonly caused by low quality inputs.


•    failure to capture rate (FTC) – Within automatic systems, the probability that the system fails to detect a biometric input when presented correctly.


•    template capacity – the maximum number of sets of data which can be stored in the system. 


       Iris recognition :-
is a method of biometric authentication that uses pattern-recognition techniques based on high-resolution images of the irides of an individual's eyes.

Not to be confused with another, less prevalent, ocular-based technology, retina scanning, iris recognition uses camera technology, with subtle infrared illumination reducing specular reflection from the convex cornea, to create images of the detail-rich, intricate structures of the iris. Converted into digital templates, these images provide mathematical representations of the iris that yield unambiguous positive identification of an individual.

            
          Iris recognition efficacy is rarely impeded by glasses or contact lenses. Iris technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. Because of its speed of comparison, iris recognition is the only biometric technology well-suited for one-to-many identification. A key advantage of iris recognition is its stability, or template longevity, as, barring trauma, a single enrollment can last a lifetime.

Breakthrough work to create the iris-recognition algorithms required for image acquisition and one-to-many matching was pioneered by John G. Daugman, Ph.D, OBE (University of Cambridge Computer Laboratory). These were utilized to effectively debut commercialization of the technology in conjunction with an early version of the IrisAccess system designed and manufactured by Korea's LG Electronics. Daugman's algorithms are the basis of almost all currently (as of 2006) commercially deployed iris-recognition systems. (In tests where the matching thresholds are—for better comparability—changed from their default settings to allow a false-accept rate in the region of 10−3 to 10−4 [1], the Iris Code false-reject rates are comparable to the most accurate single-finger fingerprint matchers.

Visible Wavelength (VW) vs Near Infrared (NIR) Imaging:-


            The majority of iris recognition benchmarks are implemented in Near Infrared (NIR) imaging by emitting 750 nm wavelength light source. This is done to avoid light reflections from cornea in iris which makes the captured images very noisy. Such images are challenging for feature extraction procedures and consequently hard to recognize at the identification step. Although, NIR imaging provides good quality images, it loses pigment melanin information, which is a rich source of information for iris recognition.
                   
    Visible Wavelength Iris Image           Near Infrared (NIR) version
          
                        

The melanin, also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–reddish). NIR imaging is not sensitive to these chromophores, and as a result they do not appear in the captured images. In contrast, visible wavelength (VW) imaging keeps the related chromophore information and, compared to NIR, provides rich sources of information mainly coded as shape patterns in iris. Hosseini et al.  provide a comparison between these two imaging modalities and fused the results to boost the recognition rate. An alternative feature extraction method to encode VW iris images was also introduced, which is highly robust to reflectivity terms in iris. Such fusion results are seemed to be alternative approach for multi-modal biometric systems which intend to reach high accuracies of recognition in large databanks 

By:- Atul Purohit 
 
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