Data is information that has been translated into a form that is more convenient to process. Data  is  often  viewed  as  a  lowest  level  of  abstraction  from  which useful information and knowledge are  derived. Experimental  data  refers  to  the  data  generated within  the  context  of  a  scientific  investigation  by  observation  and  recording. In an embedded system, data to be collected is either in the form of binary sequences of pulses, generally  referred  to  as  digital  data  or  has  continuous  range  of  values which  is  called analog data.

Most research projects need data in order to answer a proposed research problem. The data that need to be acquired, and the sources of such data, must be identified as a matter of utmost importance. No amount or depth of subsequent data analysis can make up for an original lack of data quantity or quality.

Research problems and objectives (or hypotheses) need to be very carefully constructed and clearly defined, as they dictate the data that need to be obtained and analyzed in order to successfully address the objectives themselves. In addition, the quantity of data, their qualities, and how they are sampled and measured, have implications for the choice and effectiveness of the data analysis techniques used in subsequent analysis.

Data Acquisition is the process of sampling and collection of real world data that can be either processed within a specific deadline from time of collection of data or stored in a storage device and retrieved for processing later on. Alternatively, a data acquisition system may be either real time if it processes the data as it collects it or non real time if it stores the data and processes it later on. The purpose of data acquisition is to measure an electrical or physical phenomenon such as voltage, current, temperature, pressure, or sound and log the readings in a storage device. 

Every data acquisition system shares a common goal of acquiring, analyzing, and presenting information. A processor based data acquisition generally involves conversion of analog data to digital data for storing in the memory. Hence, an analog to digital converter is the heart of an analog data acquiring system. PC-based data acquisition uses a combination  of  modular  hardware  to  take  the measurements,  application  software  to  operate  the  system,  and  a  computer  to  store  the data  and  process  it  further. 

Two students at NSIT, Nehul Malhotra and Mayank Jain, specialising in Electronics and Communication, have successfully developed a fully dedicated data acquisition system having the ability to acquire data at a specified time with fine resolution and high accuracy in both time and data measurement. Their guide is a reputed faculty member of the Electronics and Communication Division, Prof. Dhananjay V. Gadre.

The system can be used to develop the V-I curves  of  charging  of  a  capacitor  with  the  help  of  technique  known  as undersampling. From the V-I curves obtained, we can find the value of capacitance. The  system  also  has  the  ability  to  communicate with  the  PC.  The  other  parts  of  the  project  also  include  MATLAB  based  support  to  interface  the  system with PC  to  create a user  friendly environment.

The system they used is a generic PC – based data acquisition system which uses a modular hardware comprising of a digital processor. A microcontroller is a small computer on a single integrated circuit consisting internally of a relatively simple CPU, clock, timers, I/O ports, and memory. The microcontroller has been  programmed  in  C  language  to  collect  the  data  at  a  specified  time  with  fine resolution and high accuracy in both time and data measurement along with the ability to communicate with PC by software implemented for storage and further processing of data.

Although the system can collect any analog data available on its data input terminal but in this case, it has been made to collect the analog voltage across the capacitor during its charging with the resistance of pre-specified value. It should be noted that measurements like voltage across the capacitor during charging and discharging require utmost accuracy of  time  and  it  can’t  be  obtained  by  a  multithreaded  environment  like  OS (Windows/Linux) on a PC. Therefore, the need arises to develop a fully dedicated system to  collect  the  data  at  accurate  times  which  can  then  be  logged  into  PC  for  further processing.  Time  errors  arising  during  logging  don’t  matter  as  the  actual  data  was collected at the exact time although it may be received by PC at some later time. Once the PC has collected all the data it can process using features like curve tracing etc. to find the value of capacitance.

The readings across a capacitor have been taken by the process of undersampling meaning that after collecting a reading at a particular time, the capacitor is discharged to ground  and  then  charged  again  till  specified  time  to  collect  the  next  reading.  So, the actual time required to collect a single complete set of readings takes a lot more time than the time taken by capacitor to charge in a single go.

Below is a screenshot:

Nehul Malhotra can be contacted at malhotra.nehul@gmail.com.

Google Summer of Code (GSoC) is a global program that offers student developers stipends to write code for various open source software projects. Google works with several open source, free software, and technology-related groups to identify and fund several projects over a three month period. Since its inception in 2005, the program has brought together nearly 3300 students and more than 5,000 mentors & co-mentors from nearly 100 countries worldwide, all for the love of code. Through Google Summer of Code, accepted student applicants are paired with a mentor or mentors from the participating projects, thus gaining exposure to real-world software development scenarios and the opportunity for employment in areas related to their academic pursuits. In turn, the participating projects are able to more easily identify and bring in new developers. Best of all, more source code is created and released for the use and benefit of all.

The program invites students who meet their eligibility criteria to post applications that detail the software-coding project they wish to perform. These applications are then evaluated by the corresponding mentoring organization. Every participating organization must provide mentors for each of the project ideas received, if the organization is of the opinion that the project would benefit from them. The mentors then rank the applications and submit the ranked list to Google. Google then decides how many projects each organization gets, and selects the top-n applications for that organization, where n is the number of projects assigned to them.

Tux4Kids  is a volunteer project dedicated to creating fun and educational software for children. The project was started by Sam Hart over ten years ago and currently maintains and develops three software programs. The programs take their name from Tux, the Linux mascot. Tux Paint, led by Bill Kendrick, is an award-winning and widely used artistic graphics program. Tux Paint is enjoyable for everyone from the youngest children capable of using computers up through adults. Tux Math (originally written by Bill Kendrick, now led by David Bruce and Tim Holy) is a video game-style math drill program. It covers basic math operations up through topics such as negative numbers, factoring, and order-of-operations exercises. Tux Typing (originally written by Sam Hart, now led by David Bruce) offers word typing practice in the setting of two video game-type activities, as well as phrase and sentence typing for older students. They are developed natively on Linux and are included in all major desktop distributions, as well as non-Linux Free Software collections such as the FreeBSD Ports Collection and the MacPorts project for OS-X. Builds are also available for Microsoft Windows and BeOS. The aim is to avoid specific platform dependencies so the programs can be made available as widely as possible, including the computing environments that children are most likely to actually encounter in schools.

Akash Gangil is a student at NSIT who did a project called TuxMath under the Google Summer of Code 09 program with the organisation Tux4kids. He was among the 1000 students who were selected after a highly competitive application procedure in 2009.TuxMath is an arcade game that helps kids practice their math facts. This project aims to provide multiplayer facility over network (LAN) to the game.

The project enabled the prospect of competitive and collaborative playing mode in the game . Since TuxMath is being extensively used in some of the primary US schools.  Moreover , this implementation also provided a basic framework which could be duplicated in TuxType ,a typing tutor program of Tux4Kids.

Shown below are snapshots of the game play and lessons.

As an engineering student, this project was a great learning experience for Akash as it gave him a deeper insight into the software development process in the open source communities.  Also, he learnt how collaboration is achieved between developers spread over various time zones through version control systems (svn), IRC and mailing lists.

Akash Gangil can be reached at akashg1611@gmail.com if you require more information.

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In iris recognition system, accurate iris segmentation and localisation from eye image is the foremost important step. In this project, a robust and efficient method of iris segmentation is proposed.

Iris recognition systems have widespread applications including their use as a security system at airports, banks, vault entrances, study of various effects on irises of twins, as a segmentation method in other applications, etc.  Its purpose is real-time, high confidence recognition of a person's identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance. Because the iris is a protected internal organ whose random texture is complex, unique, and very stable throughout life, it can serve as a kind of living passport or password that one need not remember but can always present.

 Because the randomness of iris patterns has very high dimensionality, recognition decisions are made with confidence levels high enough to support rapid and reliable exhaustive searches through national-sized databases.

This project has been done by Aman Bindal, Akhil Goyal and Ankuj Gupta, students of the Computer Science Division, graduating in the year 2010.

This project has been shaped under the guidance of Prof. Anand Gupta, an esteemed Graphics lecturer in the Computer Science division.

Many algorithms have been suggested for segmenting of iris but most lack either of these features: speed, accuracy, bounding the iris with circle only, etc. This project aims at designing an algorithm to overcome these limitations. 

The algorithm proposed by this project achieved speeds that were about ten times faster than the existing methods. Moreover, exact bounding of iris can be determined resulting in iris recognition systems that are more robust against fake-iris-based spoofing attempts. Furthermore, a high accuracy was achieved.

Their paper was published in the 2009 IEEE International Conference on Image Processing (ICIP) and in the 2009 International Journal of Critical Infrastructure Protection (IJCIP).

Aman Bindal can be contacted at tbfaction.aman@gmail.com

Dynamic scene management is a process by which changes in a dynamic scene, such as a video, can be handled using data structures. Its everyday applications are widespread - right from streaming online videos to rendering good animation graphics.

Tracking, monitoring and management of moving objects become increasingly important in modern geospatial application. An important example of such applications is monitoring and management of a dynamic scene captured by a network of videos or sensors for ensuring for example the security of the people during a public event, or for leading emergency or rescue teams in real environment.

At present the most important things on scene rendering in computer graphics are fidelity and efficiency. In order to achieve these goals, a considerable number of applications require a data structure like a tree to manage the entire scene. Searching the scene means to traversal the tree and moving objects in the scene means to recreate the tree, which are very costly but unavoidable process.

This project basically seeks to make this process faster, while maintaining the quality of the output graphics. Possible applications for this algorithm would be in fast graphics, such as in arcade and online gaming. 

Finally, it discuss other potentials and limitations of the proposed model and suggest some new research avenues regarding the improvement of the proposed model.

This project has been shaped by Vaishnavi Sundararajan and Saurav Malviya of Instrumentation and Control Department of NSIT, graduating in the year 2010. This has been achieved under the guidance of Prof. Anand Gupta, an esteemed Graphics lecturer in the Computer Science Division.

This project, as they both agree, is the longest running and most interesting project of their undergraduate studies so far. This project involves the management of dynamic scenes in three dimensions using tree data structures called octrees. Octrees are most often used to partition a three dimensional space by recursively subdividing it into eight octants.

This project aims at improving the existing resource-hungry methods of dynamic scene management in three dimensions. The previously existing method involved resizing of the nodes of an octree, while this project completely avoided or minimised it, thus achieving greater efficiency. The algorithm developed proved to be much faster than the existing ones (for certain scene configurations, as much as 25% faster).

They even developed a research paper on the same, which was published at the IEEE International Multitopic  Conference 2008.

After having published this paper, they came up with a more general method which could handle a greater variety of scenes than their previous algorithm, while maintaining the same time complexity. This method is the focus of the transaction which is currently under review at the IEEE Image Processing Society.

As far as real world applications are concerned, this method can be used for any situation involving changes. Possible applications of this project are face isolation and face following.

Vaishanvi S. can be contacted at vaishnavi.89@gmail.com.

You will need a resume if you are looking for an internship and applying for the intern jobs. And remember your resume will be one of the deciding factor for getting a call for internship.

A well made resume, which identifies your goals, academic background, skills, experience (if any), and activities, is just as critical for the internship search as it will be for your job search after you complete your education. A resume or CV which is for internship may not be very different from the one that you will use for a job application - both in intent and content. However, you may include some information for the internship resume which you may not in your final resume for the job search.

Employers are demanding more & more they rank internships as the second most important quality in a candidate behind communication skills when interviewing.

The more internships, the more it will help you better prepare for the demands of a workplace. You have to bring something else to stay competitive. This is critical if you don't want to find yourself in the interview waiting area sitting next to a person from a more elite school than yours