ZEM SYED

University of Central Florida⚔️

Ph.D. in Electrical and Computer Engineering; GPA: 3.82/4.00

Field of Specialization: Wireless Communication and Embedded Systems

Relevant Coursework:

• Field Programmable Gate Array Design
• Wireless Communication
• Computer Communication and Networking Fundamentals
• Machine Learning
• Computer Vision
• Robot Agent and Human Interaction
• Advanced Computer Networks
• Design Analysis of Algorithm

University of Central Florida⚔️

Master of Science in Civil Engineering; GPA: 3.85/4.00

Field of Specialization: Traffic Engineering and Intelligent Transportation Systems

Relevant Coursework:

• Data Mining Methodology I
• Data Preparation
• Algorithm and Models for Smart Cities
• Traffic operations
• Modelling Human behaviors
• IoT Application in Smart Cities
• Inter-Desciplinary Introducction to Smart Cities

Islamic University of Technology

Bachelor of Science in Electrical and Electronic Engineering; GPA: 3.88/4.00

Relevant Coursework:

• Wireless Communication
• Data Communication and Networking
• Microcontroller-based System Design
• Embedded System Design
• Electrical Circuits I and II
• Electronics I and II
• Digital Electronics
• Power Electronics
• Control System Engineering
• Digital Signal Processing
• Engineering Economics

NWSL — Networking and Wireless Systems Lab

Graduate Research assistant

• Working under the supervision of Prof. Dr. Murat Yuksel.
• Currently working in the development of wireless algorithms tailored for free space optical communication. My role involves the end-to-end process of algorithm creation, from conceptualization to implementation. This encompasses crafting sophisticated simulation models to validate the efficacy of the algorithms in diverse scenarios.
• In addition to algorithm development, I actively engage in the practical aspects of my work by creating and testing prototypes. Leveraging off-the-shelf hardware and embedded systems, I bring theoretical concepts to life, ensuring that the developed algorithms seamlessly integrate into real-world applications. This multifaceted role not only hones my algorithmic expertise but also emphasizes hands-on proficiency in hardware and embedded system development.

UNMD Lab

Graduate Research assistant

Research Interests:
• Machine Learning
• Wireless Communication
• Connected Autonomous Vehicles (CAV)
Main responsibilities:
• Process, analyze, and visualize traffic data sets using Python
• Develop machine learning models for predicting traffic and crashes
• Analyze and simulate traffic networks to understand the effects of AV and CAV
• Develop and analyze smart transport systems for efficient Hurricane Evacuation

Huawei Bangladesh ltd.

Product Control Manager

• Orchestrate the seamless delivery of telecommunications services. My role involves managing billing processes, optimizing subcontractor coordination, and ensuring the successful execution of projects. Utilizing SQL and shell scripting, I actively monitor network performance, addressing issues proactively to maintain high service quality.
• design and integration of wireless and optical products. Leveraging my technical expertise, I forecast and meticulously maintain project timelines, aligning integration efforts with overarching organizational goals. My approach to project planning involves resource forecasting and collaborative efforts to ensure the success of initiatives.
• Collaborate with cross-functional teams to develop comprehensive project plans.Collaborate with cross-functional teams to develop comprehensive project plans.

FPGA Based Image Processing

The objective is to manipulate raw image formats, in this case a gray scale image provided by the coe file, and each pixel in the grayscale image corresponds to a single byte ranging from 0 to 255. The main objective involves connecting a keyboard to the FPGA board, and upon pressing the "0" key, a Low Pass Filter (LPF) is implemented, and the processed image is displayed on the screen. The LPF operation, characterized by the coefficients [1/9 1 1 1; 1 1 1; 1 1 1], is reiterated with each key press, progressively blurring the image on the monitor.

Real-Time Crash and Pedestrian Detection System using Internet of Vehicle (2023)

Implemented a Real-time Crash and Pedestrian Detection System using Internet of Vehicle (IoV). Utilized two Raspberry Pi devices as client and server to prototype mobile nodes, creating an ad hoc network and a 2.4 GHz WiFi network. Employed tiny ML models for crash and pedestrian detection from a real-time dash cam feed using computer vision at the client side. C and Python programming languages were used for implementation.

Co-Simulation Study to Assess the Impacts of Connected and Autonomous Vehicles (CAV) (2021)

Led a project funded by the SAFERSIM project to integrate a large-scale traffic simulator with communication simulator (OMNET) for Connected and Autonomous Vehicles (CAV). Conducted a co-simulation study to assess the impacts of CAVs on the traffic environment, bridging the gap between traffic dynamics and communication behaviors.

Mars Rover Project (2017-2019)

Spearheaded the development of a versatile Mars rover, showcasing expertise in both hardware and software. Designed a robotic arm with 6 degrees of freedom (DOF) using an STM 32 micro-controller and Raspberry Pi for motor control and sensor detection. Developed a 100 m Line of Sight (LOS) communication setup using 5GHz IEEE 802.11 standard for real-time video transfer and rover control. Designed custom PCB circuits for power control with a buck-boost converter and implemented an L298 motor controller.

Programming Languages

Robotics and Embedded Systems

Machine Learning :

1. Randomized 3D Neighbor Discovery with Mechanically Steered FSO Transceivers, IEEE MILCOM'24
   Optimally searched for the pointing angle to an optical receiver for which a 3D location with error is given.
2. Predicting Real-time Crash Risks During Hurricane Evacuation Using Connected Vehicle Data
   Provided insights into potential crash points and mitigation strategies during emergency evacuations.
3. A Co-Simulation Study to Assess the Impacts of Connected and Autonomous Vehicles on Traffic Flow Stability During Hurricane Evacuation
   Explored the benefits and challenges of integrating autonomous vehicles into evacuation procedures.
4. Real-time Traffic Restoration Time Prediction Based on the Estimated Traffic State
   Offered methodologies to quickly restore traffic flow post-disaster, crucial for effective recovery operations.

Presented at Major Transportation Conferences:

• Transportation Research Board (TRB) 102nd Annual Meeting
  Highlighted novel findings from studies on autonomous vehicles and their role in disaster management.
• Future of Autonomous Vehicles (FAV) Summit
  Discussed innovative traffic management solutions using real-time data during natural disasters.