Portfolio - Daniel Plotkin

About Me

Hello! I'm Daniel Plotkin, a passionate and driven Electrical and Computer Engineering Student with a strong foundation in signal processing, systems design, and innovative problem-solving. I have over three years of professional experience working with cutting-edge technologies and tools such as AutoCAD, MATLAB, and Raspberry Pi.

I’m pursuing my Bachelor’s degree in Electrical and Computer Engineering at the New York Institute of Technology (NYIT), maintaining a 3.87 GPA. My involvement in various groundbreaking research projects, such as developing a gunshot-detecting microphone system, utilizing Raspberry Pi, complements my academic journey. This experience has deepened my expertise in hardware design, optimization algorithms, and data analysis.

I thrive in collaborative, innovative environments and enjoy tackling complex challenges. My enthusiasm for engineering goes beyond the classroom, as I constantly explore emerging technologies, participate in community events, and work on personal projects to stay at the forefront of this ever-evolving field.

Education

New York Institute of Technology

B.Sc. in Electrical and Computer Engineering

Expected Graduation: Fall 2025

Minor in Mathematics | 3.87 Cumulative GPA | Presidential Honors List

Relevant coursework: RF Electric Circuits, Silicon Integrated Circuit Theory & Fabrication, Introduction to Vlsi Design, Random Signals & Statistics, Signals and Systems, Communication Theory, Control Systems, Electronics II, Microprocessors & Embedded Systems, Operating Systems.

Extracurricular Activities:

  • Member of IEEE.

Skills

  • Language: Intermediate in Russian; Fluent in English.
  • Software/Hardware: AutoCAD, Revit, MATLAB, Raspberry Pi, Adobe Creative Suite, Microsoft Office Suite, Microsoft Projects, Bluebeam Revu.
  • Programming: Verilog, Java, Python, C, Assembly.
  • Other: Excellent teamwork and project management skills; adept at troubleshooting and problem-solving complex systems.

I continuously seek opportunities to enhance my technical and soft skills through hands-on projects and advanced training programs.

Work Experience

NYIT – Research Assistant

Supported a project on developing a gunshot-detecting microphone using Raspberry Pi, focusing on signal processing, detection accuracy, and real-world testing scenarios.

WSP – Electrical Engineering Intern

Participated in the design and modification of electrical systems for buildings, renewable energy setups, and smart grids.

  • Created detailed AutoCAD designs for commercial and industrial projects.
  • Conducted energy audits and prepared optimization recommendations.

Lecron Inc. – Assistant Electrician

Directed a team of electricians in the assembly of electrical panels and troubleshooting systems in both residential and commercial settings.

  • Streamlined panel assembly processes, reducing time by 20%.
  • Ensured adherence to safety standards during installations and repairs.

Projects

Wearable Inductive Sensor–Based Controller for Drone Flight In Progress

Objective: Develop a glove interface using inductive sensing (LDC1614) and IMU data on an ESP32 to gesture-control a quadcopter.

Key Features:

  • 9-axis IMU filters wrist orientation (palm up/down/left/right).
  • 4-finger inductive coils detect “finger-gun” & “fist” gestures.
  • ESP32 handles I²C data, runs ML gesture classifier, and streams commands via Wi-Fi/LoRa.
  • Gazebo simulation & Arduino-style firmware for rapid prototyping.

Technologies Used:

  • LDC1614 inductive sensor board, GY-91 IMU module
  • ESP32 (I²C, Wi-Fi, dual-core processing)
  • Python/Edge Impulse for model training, Gazebo for simulation

Outdoor Gunshot Detection System In Progress

Objective: Deploy ESP32-based sensor nodes and a Raspberry Pi to detect and localize gunshots in outdoor environments with optimized accuracy and efficiency.

Key Features:

  • Custom sound-library generation and synthetic data creation to model gunshot acoustics in diverse outdoor settings.
  • Signal preprocessing and feature extraction (filtering, normalization, mel-spectrograms) tuned for robustness against noise, echoes, and multipath.
  • Machine-learning classification (CNN, CNN+LSTM, 1D CNN, transformer models) to distinguish gunshots from urban noise.
  • Microphone placement optimization to maximize direct-path signal fidelity and minimize multipath interference.
  • Time-Difference-of-Arrival (TDoA) localization combining classification outputs with spatial analysis for real-time source positioning.

Technologies Used:

  • Hardware: ESP32 microcontrollers, omnidirectional microphones, Raspberry Pi.
  • Software: Python (NumPy, SciPy), MATLAB, ODEON acoustic modeling.
  • ML Frameworks: Edge Impulse, TensorFlow Lite.

Resources:

Hardware-Accelerated QR Decomposition Engine Completed

Objective: Implement a parameterizable Verilog FSM to perform Modified Gram–Schmidt QR decomposition on fixed-point M×N matrices using Block RAM.

Key Features:

  • Pure Verilog-2001 FSM with load, projection, normalization, and store states.
  • BRAM interface streams input words and captures flattened Q/R outputs.
  • Parameterizable word-width and matrix dimensions.
  • Testbench with waveform dump (VCD) for automated verification.

Technologies Used:

  • Icarus Verilog & GTKWave
  • Fixed-point arithmetic (8.8 format)

Resources: