Machine Learning Real-Time

Ayushi Ashok Patel

E-mail: **********@*****.*** LinkedIn: https://www.linkedin.com/in/ayushi-patel-90560819b/ Mobile: +1-415-***-****

EDUCATION

San Francisco State University San Francisco, CA

Master of Science (M.S.) - Electrical and Computer Engineering(CGPA – 3.68) Aug, 2022 – Dec, 2024

• Coursework: On-device Machine learning, Computer Architecture, Embedded Systems, Advanced Design with Microprocessors, Advanced Computer Networks, Advanced Design in Microcontrollers, Neural-Machine Interface, AI in Engineering, Robotics Gujarat Technological University Surat, Gujarat, India Bachelor of Engineering (B.E.) - Electrical Engineering(CGPA – 9.18/10) Aug, 2018 – June, 2022

• Coursework: Control systems, Industrial Automation, High voltage Engineering, pPower electronics, Circuit analysis SKILLS

• Programming languages: C, C++, Python, Embedded C, Assembly Language, HTML/CSS, Shell Scripting, Bash

• Protocols & Technologies: Bluetooth, Wi-Fi, I2C, UART, SPI, PWM, ADC/DAC

• Hardware Platforms:STM32, ESP32, ESP8266, Raspberry Pi, Arduino

• Software Platforms: STM32CubeIDE, Keil u-Vision, MATLAB, Simulink, Visual Studio, Git, PyCharm

• Hardware Testing Tools: Oscilloscope, Logic Analyzer, Multimeter

• Operating Systems: Windows, Linux

EXPERIENCE

ML and CV Research Assistant (San Francisco State University) Feb,2025 – Current

• Research Focus - Edge AI for Real-Time Industrial Defect Detection

• Assisting ongoing research on a lightweight computer vision model for automated defect detection in manufacturing, utilizing YOLOv8 and attention-based CNN architectures.

Embedded Engineer (Tirupati Electrical Corporation) April,2021 – April, 2022

• Designed and configured industrial-grade CCTV and fire alarm systems, drove industry compliance, ensuring ONVIF and NFPA standards adherence, enabling a 100% approval rate for large-scale security installations.

• Worked with both analog and IP-based surveillance systems, integrating them with embedded controllers for automated monitoring, real-time event processing, and secure data transmission over industrial protocols like Modbus and CAN.

• Optimized firmware for security and alarm systems, improving firmware efficiency in alarm and surveillance systems, reducing false alarms by 25%, and enhancing real-time event processing.

• Collaborated on a government railway project, enhanced system reliability by leading EMI/EMC testing for railway surveillance, ensuring compliance across 10 zones and reducing interference-related failures by 30%. PROJECTS

Transfer learning based Real Time Sign Language Translator Feb,2024 – Dec, 2024

• Engineered an advanced transfer learning model that processed over 21,000 training samples, achieving a 15% increase in prediction reliability while optimizing resource allocation within the machine learning pipeline. Vehicle tracking and theft Prevention System Aug,2023 – Dec,2023

• Developed a real-time vehicle tracking and theft prevention system using an ARM Cortex-M4-based MCU with STM32CubeIDE and HAL-based programming with integration of GPS Neo-7M for location tracking and 4G-SIM7600A-H for communication via SMS and calls.

Line Following Robot using STM32L476RG Jan, 2023 – April,2023

• Developed a line following robot on the STM32L476RG platform using Keil uVision5, CMSIS, and C, configuring multiple GPIOs. Integrated infrared sensors with a closed-loop control algorithm for accurate line tracking and conducted sensor calibration and real- world testing

Human Activity Classification using Smartphone Data Aug,2022 – Dec, 2022

• Applied statistical and signal processing techniques (RMS, mean, standard deviation) to extract features from time-series data. Utilized dimensionality reduction with PCA to optimize performance and trained multiple classifiers, achieving 100% training accuracy with KNN and high test accuracy with an SVM Linear model.

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