ProgrammingC, Automotive, CAN, UDS, DETtool, I2C, SPI, Autosar, GitHub

Koteshwari Kopparthi

Farmington, Michigan Contact No: +1-248-***-****

Email ID: ************@**.*****.*** LinkedIn GitHub

SUMMARY

To obtain a challenging and responsible position in a professional organization where can Contribute my best for the successful growth of an organization by utilize my skill and hard work. Also, I Will put efforts put my efforts up my professional and personal life. Quick learner and enthusiastic about learning new advanced technology and a responsible individual contributing to the overall improvement of the organization.

SKILLS

Programming Skills: Embedded C, Python

Vector Tools: CANoe, CANalyzer, CANape, Vector Davinci Configurator

OEM Specified tool (FORD): Diagnostic Engineering tool (DET)

Communication Protocol: UDS (ISO 14229-1), CAN (ISO 11898), I2C, SPI.

Architecture: Basics of Autosar.

Other tools: Jira, GitHub, Git

EDUCATION

Master of Science in information studies June2023- still (pursuing)

SUMMARY

Pursuing MS in information studies (hoping to complete by May’2025).

Completed Cybersecurity, Objective Oriented Programming in Java, Data mining and Data visualization, Network Management and Finance for Engineer in my MS so far.

IT IBM certificate from Coursera on Cyber Security.

Excellent Overall knowledge of Automotive Embedded Software Engineering, Embedded C, Automotive protocols, tools.

Excellent knowledge of overall Embedded SW development process (Writing code or Generating code as per requirements, compile the SW, flash the executable files in ECU, Testing and Debugging).

Excellent knowledge of Braking system and ADAS systems (Radars and Cameras).

Excellent knowledge of CAN and UDS Protocol.

o$10 – Diagnostic session control

o$11 – ECU Reset

o$14 – Clear diagnostic information

o$19 – Read diagnostic information

o$22 – Read data by identifier (DID)

o$2E – Write data by identifier

o$27 – Security Access

o$3E – Tester present etc. and NRC codes as well.

Excellent knowledge of AUTOSAR layered architecture.

Excellent knowledge of testing using Vector CANoe and CANalyser.

Bug life cycle, Bug Investigation, Debugging and provide solutions for issues identified during testing.

Serial Communication Protocols SPI and I2C.

Good knowledge of Agile Sprint Process (Daily scrum, Sprint planning and Sprint Retrospective).

Good Knowledge in GitHub tool (Creating projects, Configure, Insights, pull requests, status check) and so on.

Good Knowledge on Jira tool (Creating Change requests, Stories, Epics, Action items, updating status..etc).

Quick learner with the ability to grasp new technologies and software.

Certifications

Cyber security certification from Coursera-2024.

Certificate of Completion for Data mining and Data from uCertify -2023.

ESL from Novi adult education-2022.

Academic Project

Human Activity Recognition Using Digital Image Processing:

In this project recognizing human activities from video sequences or still images is a challenging task due to problems, such as background clutter partial occlusion, changes in scale, viewpoint, lighting and appearance.

Many applications including video surveillance systems, human-computer interaction, and robotics from human behavior characterization require a multiple activity recognition system.

In particular we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested.

Fake Product Detection:

In this project we have to investigate on fake products in the market and need to design a system, so that it can help the individuals to differentiate between fake and real products in the market.

We need to design a machine learning system to detect the fake products. After investigating we came across lots of products being faked in the market. We took automobile industry for our system.

We used the confidence score of each image to detect whether it is fake or not.

If a product confidence is more than 0.9 then it’s going to be real image otherwise fake image.

Contact this candidate