I am a first year MS student at the University of Minnesota Twin Cities. I am currently looking for Summer internships in thee field of Robotics, Computer Vision and Automation.
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approachijcoa
Detection of software bugs and its occurrences, repudiation and its root cause is a very difficult process in large multi threaded applications. It is a must for a software developer or software organization to identify bugs in their applications and to remove or overcome them. The application should be protected from malfunctioning. Many of the compilers and Integrated Development Environments are effectively identifying errors and bugs in applications while running or compiling, but they fail in detecting actual cause for the bugs in the running applications. The developer has to reframe or recreate the package with the new one without bugs. It is time consuming and effort is wasted in Software Development Life Cycle. There is a possibility to use graph mining techniques in detecting software bugs. But there are many problems in using graph mining techniques. Managing large graph data, processing nodes with links and processing subgraphs are the problems to be faced in graph mining approach. This paper presents a novel algorithm named BugLoc which is capable of detecting bugs from the multi threaded software application. The BugLoc uses object template to store graph data which reduces graph management complexities. It also uses substring analysis method in detecting frequent subgraphs. The BugLoc then analyses frequent subgraphs to detect exact location of the software bugs. The experimental results show that the algorithm is very efficient, accurate and scalable for large graph dataset.
I am a graduate student pursuing MS in Electrical Engineering (Embedded Systems) at the University of Colorado Boulder. Here is my resume which contains the list of projects I have completed so far, technologies that I have worked on, organizations I have been associated with and a patent that I have co-authored.
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approachijcoa
Detection of software bugs and its occurrences, repudiation and its root cause is a very difficult process in large multi threaded applications. It is a must for a software developer or software organization to identify bugs in their applications and to remove or overcome them. The application should be protected from malfunctioning. Many of the compilers and Integrated Development Environments are effectively identifying errors and bugs in applications while running or compiling, but they fail in detecting actual cause for the bugs in the running applications. The developer has to reframe or recreate the package with the new one without bugs. It is time consuming and effort is wasted in Software Development Life Cycle. There is a possibility to use graph mining techniques in detecting software bugs. But there are many problems in using graph mining techniques. Managing large graph data, processing nodes with links and processing subgraphs are the problems to be faced in graph mining approach. This paper presents a novel algorithm named BugLoc which is capable of detecting bugs from the multi threaded software application. The BugLoc uses object template to store graph data which reduces graph management complexities. It also uses substring analysis method in detecting frequent subgraphs. The BugLoc then analyses frequent subgraphs to detect exact location of the software bugs. The experimental results show that the algorithm is very efficient, accurate and scalable for large graph dataset.
I am a graduate student pursuing MS in Electrical Engineering (Embedded Systems) at the University of Colorado Boulder. Here is my resume which contains the list of projects I have completed so far, technologies that I have worked on, organizations I have been associated with and a patent that I have co-authored.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
1. Amol Vagad 414 Erie Street, Minneapolis, MN 55414
612-481-2013
amolvagad@gmail.com
SUMMARY
Highly motivated graduate student seeking internship/co-op opportunities for Summer 2017 in the field of Robotics, Controls and Automation.
EDUCATION
Master of Science (M.S) [ Sep 2016-May 2018]
University of Minnesota-Twin Cities, Minneapolis, MN
Major: Electrical Engineering
Minors: Computer Science and Scientific Computations
Courses: Robotics, Computer Vision, Advanced Algorithms, Modern Control, Robust Control.
GPA: 3.55/4.0
Bachelor of Engineering (B.E) [ 2012-2016]
University of Mumbai, Mumbai, India
Major : Instrumentation Engineering
GPA: 8.41/10.0
WORK EXPERIENCE
Summer Intern [June-July 2015]
Air India Ltd, Mumbai, India
Assisted to acquire and load data from the SSFDR using Rose software.
Carried out testing of the air conditioning system, refrigerators and ovens.
Collaborated with a multi-disciplinary team of software, electrical, and aerospace engineers
SKILLS
Computer Languages: MATLAB, C/C++, Python, PLC Ladder and Assembly
Design & Simulation: OpenCV, ROS, Gazebo, Arduino,NI Lab View, and Keil uVision IDEDelta V DCS, Simulink, AutoCAD
Operating Systems: Windows, Linux and Raspbian.
Documentation : Github, MS Office and Latex
PROJECTS
Advanced Driver Assisting System Using Computer Vision Feb 2017- Present
Detecting vehicles , lanes, traffic signal for autonomous vehicles using computer vision
Currently developing algorithms for vehicle detection
Implementing algorithms using OpenCV and C++
Robot Platoon System using ROS Oct-Dec 2016
Designed a leader-follower platoon system using autonomous mobile robots Pioneer 3-DX and SICK Laser sensor with ROS.
Implemented wall following and segmentation algorithms for the leader and follower robots respectively using Python 3.5 in Linux
Adaptive Cruise Control for Vehicle Platoons March 2017- Present
Developed optimal control methods for vehicular platoons
Implemented Linear Quadratic Regulator control scheme
Generated simulations for test data using MATLAB
Blood Alcohol Measurement for Drivers Jun-Sep 2015
Developed an Arduino-Uno and MQ-3 gas sensor based system to measure BAC levels of car drivers.
Created a control system to restrict inebriated drivers from starting a car.
Built a GPS communication system for contact with friends/family of the driver based on IoT platform.
Smart Home Surveillance Jun-Aug 2014
Designed a Raspberry Pi and Python 2.7 based home surveillance system based on the IoT platform.
Implemented automatic image based email system to user in case of unwanted movements inside the house.
Control Strategies for Barrett WAM Arm with ROS Nov 2016-Present
Installed and implemented ROS libraries for the Barret WAM arm
Simulated basic movements using Gazebo and Rviz