Embedded Systems R D

Sravan Kumar Velicheti

+1-603-***-**** ***********@*****.*** linkedin/sravan-kumar-velicheti OrCid GitHub Summary

Enthusiastic and detail-oriented LabVIEW Developer with hands-on experience in developing automated test systems and integrating instrumentation protocols. Seeking to contribute to innovative and pioneering projects while continuing to grow through specialized internal training in a dynamic RD environment. Demonstrated ability to lead and contribute to projects in academic, research, and internship settings. Built Emotion Recognition models using NLP and deep learning (DistilBERT, PyTorch, Transformers). Education

Rivier University Nashua, USA

Master in Computer Science 2023 – present

Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Nashua, USA Bachelor’s in Electronics and Communication Engineering 2019 – 2023 Technical Skills

Languages: LabVIEW (Core + Advanced), Embedded C, Python, SQL, JavaScript, JSON, HTML/CSS, Matlab, Instrumentation Protocols: GPIB, I2C, SPI, Modbus, Serial (UART), USB, TCP/IP, MQTT Instruments: DAQ6510, SMU2400 series, CRO, Weather Chamber, NI MyRIO, FPGA Database System: MySQL, ORACLE, PostgreSQL, AWS Aurora, RDS Tools & Platforms: NI TestStand, NI DAQ, Multisim, Arduino UNO, Raspberry Pi, NodeMCU, SIM800L, Arduino Nano, Arduino Mega 2560

Sensors & Radars: Ultrasonic, IR, Colour Sensor, Temprature Sensor(LM35), Motion Sensor(PIR), LDR, Heart Rate Sensor, FMCW Radar

Testing & Integration: ATE (Automated Test Equipment) Development, GUI Design, Hardware Integration, Test Scripting

Experience

Media Garcia – Software Intern Dec 2024 – March 2025 Intern St Paul, MN, USA

• Designed and developed web pages using Wix Studio and WordPress, ensuring fully functional and responsive designs.

• Managed customer data efficiently using Wix Database, enabling seamless export and import processes for analysis via PostgreSQL.

• Deployed 5 static and dynamic websites using AWS Lightsail, ensuring optimal performance and functionality. Crest Lab – Instrument Automation Aug 2022 – Jan 2023 Research Intern Chang Gung University, Taiwan

• Automated PCB reliability testing using Keithley DAQ6510 and 2400 Series SMU to monitor electrical characteristics under accelerated stress conditions.

• Developed LabVIEW-based control scripts for 24-day continuous data acquisition of resistance and current with constant voltage input.

• Integrated the setup with a programmable weather chamber, simulating varying temperature and humidity to evaluate environmental impact on PCB performance. Automation with DAQ and LabVIEW.

• Logged and analyzed large datasets to detect early fault signatures and parameter drift, enabling predictive maintenance and reliability assessment.

• Enhanced test efficiency and repeatability through automated measurement cycles, contributing to long-term data-driven diagnostics in electronic system evaluation.

• Automated the agriculture process with LabVIEW and MyRIO using some Sensors such as moisture sensor, Temprature Sensor, NPK Sensor

• Designed GUI to demostrate the Alert System in Chemical Factory using SMTP that sends Email to Operator. Ignitarium – Product Testing March 2022 – May 2022 Intern Chennai, IND

• Conducted degradation testing on Arduino UNO microcontroller by placing it in a climate-controlled chamber for 4 days under continuous 5V power supply to study long-term reliability.

• Focused on the degradation of the onboard crystal oscillator, which resulted in a 12.5% reduction in clock speed, significantly impacting the microcontroller’s processing efficiency.

• Post-degradation analysis using a path detection robot showed an increase in decision latency of approximately 13 milliseconds, highlighting the influence of oscillator wear on real-time embedded applications.

• Provided empirical insights into oscillator-based aging in embedded systems, contributing to predictive maintenance models and hardware selection strategies in mission-critical applications. Projects

Emotion Recognition in Asynchronous Communication Using DistilBERT

• Built an Emotion Recognition system using DistilBERT to classify emotions from text, achieving 79.45% accuracy.

• Compared performance with LSTM-based models, which achieved 75.1% accuracy.

• Implemented deep learning pipelines using PyTorch and Hugging Face Transformers to optimize model performance.

• Employed cross-entropy loss with AdamW optimizer for fine-tuning, significantly improving emotion detection accuracy.

• Developed predictive models for real-time emotion analysis using asynchronous text inputs.

• Technologies used: Python, PyTorch, TensorFlow, Hugging Face Transformers, DistilBERT, LSTM Smart Environmental Monitoring and Alert System using LabVIEW and Arduino UNO

• Designed and implemented a real-time environmental monitoring system by integrating Arduino UNO with sensors (DHT22 for temperature humidity, MQ-135 for air quality).

• Used NI LabVIEW for real-time data acquisition, visualization, and alert generation based on dynamic thresholds.

• Built a custom LabVIEW GUI for continuous monitoring and automated alerts (email/SMS) in case of critical pollution or temperature levels.

• Enabled data logging and trend analysis features for historical tracking of environmental conditions.

• System deployed in indoor labs and tested for reliability under varying environmental conditions.

• Technologies used: NI LabVIEW, Arduino UNO, DHT22, MQ-135, Serial Communication, NI-VISA, GUI Design

Publications

Smart Social Distancing Robot for COVID Safety

Contactless Fog-Based Handwash Kit for COVID Safety Professional Certificates

AWS Certified Cloud Practitioner

Best Presenter Award for IEEE Article: Honored for delivering an exceptional presentation on my research article at an IEEE conference, demonstrating strong communication skills.

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