1. Pravalika Reddy Punugu
E-Mail: pravalikareddy.421@gmail.com
Mobile: +91 8143013090
OBJECTIVE
To establish myself as a successful Software Programmer with excellence in terms of achievement, creativity
and dedication so as to join an organization, which provides opportunities to learn, grow and improve the
scope for implementing my skills and there by contributing to the growth of the organization.
SUMMARY
 Good knowledge of .NET technologies: ASP. Net, C#. Net, VB.Net, ADO.NET, Visual Studio. Net.
 Good knowledge in using MS SQL Server.
 Good knowledge in using Object Oriented Programming.
 Hard-working, self-motivated team player with good communication, analytical and interpersonal
skills. Ability and adaptability to work with different technologies on different platforms.
 Knowledge in Agile (Scrum) development process.
 Willingness and ability to quickly adopt and master new technologies
EDUCATIONAL QUALIFICATIONS
 M.Tech in DECS from Jawaharlal Nehru Technologies University, Hyderabad in 2015 with 82%
 B.Tech in ECE from Jawaharlal Technological University, Hyderabad in 2013 with 77%
SKILL SET
Technical:
Operating Systems Windows XP/2000
Programming Languages C, C#.Net, VB.Net
Database Technologies SQL Server 2008
Windows Technologies Windows Form
Web Technologies ADO.Net, HTML, ASP.Net
TRAINING DETAILS
Attended Microsoft .Net training from Sathya Technologies, Hyderabad.
PROJECT1:
Title: Mobile Shoppe
Description: Mobile Shoppe is a windows Application which is computerizing a mobile Shoppe day to day
transactions.
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2. ACADEMIC ACHIEVEMENTS
 Participated in Paper presentation on “wireless mobile charger using microwaves” in “SLC’S
Institute of engineering & technology”, Hyderabad.
 Participated in paper presentation on “WISENET’ “Stanly Stephen College of engineering &
technology” at Kurnool
ACADEMIC PROJECT SUMMARY
Project: Portable Camera-Based Assistive Text and Product Label Reading from Hand-Held Objects for
Blind Persons
Team Size: 1
Description: We proposed a camera-based assistive text reading framework to help blind persons read
text labels and product packaging from hand-held objects in their daily lives.
To automatically localize the text regions from the object ROI, we propose a novel text localization algorithm
by learning gradient features of stroke orientations and distributions of edge pixels in an Ad boost model.
Text characters in the localized text regions are then binarized and recognized by off-the-shelf optical
character identification software.
Environment MATLAB, Embedded C.
.
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