Two approaches have been used one is based on machine learning with manual feature extraction i.e. The aim of this project is to create an embedding for the face which is capable of processing facial pictures rapidly fast and quickly with low error rates. “A Unified Embedding For Facial Recognition” Jan 2018-Dec 2018 Maintenance of testing Zig for BMS functional testing and voltage/current Calibration. Testing, debugging and calibration of the BMS using IAR Embedded Workbench. Testing of the Datalogger system, BOM tracking.Ĭontroller used: STM32, PCB Tracking, Designing Schematics Battery Management System: 4-Cell Li-ion batteryĭeveloped the hardware /software for the BMS as per requirements. Ltd.” as Embedded Engineer Dec 2018 - Currently Datalogger (Telemetry system for the BMS(Battery management system of EVehicles) Product Algorithm Designing and Developing firmware for the telemetry system along with hardware team.Īlgo: Embedded C language, Scheduling Tasks, GSM/GPS. Software: MATLAB, Cadence-Virtuoso, Xilinx ise, XC16,Multisim,IAR Systems, KEIL, MPLAB, Attolic, XCTU.īoards: FPGA Basys2, Arduino Uno, Raspberry Pi. Protocols and Peripherals: I2C,ADC,SPI,UART,PWM,DAC.
Programming: C, VHDL,Embedded C, Assembly Language.
**** -*** LNM Institute of Information Technology 9.17/10.00 2015 12th-CBSE Jawahar Navodaya Vidyalaya SilI,Silvassa 89.90/100.00 2013 10th-CBSE Jawahar Navodaya Vidyalaya Basohli,J&K 10.00/10.00 TECHNICAL SKILLS: Year Degree/Certificate Institute/School CGPA / %
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