Project Descriptions

With this project we will be exploring the use of fiber optics, including the use of fiber Bragg gratings (FBG’s), for vibrational analysis and structural monitoring.  A FBG behaves as an optical filter that will reflect only one particular wavelength and pass all others.  These devices are typically designed to operate around 1550 nm as this is the region of the infrared spectrum that is used for telecommunications.  As the fibre’s environment changes (temperature, strain etc.) the peak wavelength of the filter shifts.  The transmission through a single device can be used for single point strain, temperature and vibration sensing.  If several devices are multiplexed it is possible to create a distributed sensor.  The Ultimate goal of the project is to produce a distributed sensing prototype where vibrational analysis can be used to spot vibrational irregularities with an aim toward fault detection.  There will be some flexibility as to what structure the students wish to monitor.

Requirements: Basic knowledge of signal Processing, Familiarity with programming (Python), Knowledge of optics and fiber optics is an asset.

The project involves the development of a fiber optic-based data transmission system using an Arduino microcontroller. The objective is to design and implement a setup that enables high-speed data transfer and remote control over fiber optic cables, The project consists of designing the circuit for interfacing the Arduino with fiber optic transceivers, programming the microcontroller for data encoding and decoding, and ensuring reliable data communication over varying distances. Key challenges include optimizing the Arduino code for efficient data handling, integrating suitable optoelectronic components, and managing and quantifying signal integrity under different conditions. More advanced aspects could involve investigation into different signal modulation schemes to increase data capacity.

Requirements: Experience in Arduino programming, circuit design, and working with optoelectronic components. Familiarity with fiber optic communication principles and data transmission protocols is an asset.  Additional skills in signal processing, Python/Matlab for data analysis, and experience in 3D printing.

Low frequency Raman (LFR) spectroscopy, a technique that probes intermolecular vibrations, finds various applications in biomedical and pharmaceutical research. It can play a fundamental role in optimizing drug performance and ultimately improving patient outcomes. LFR measurements involve irradiating the sample under study with a single frequency laser, collecting the scattered light, and measuring its spectral content with a spectrometer. A major challenge of this technique is to detect extremely small Raman peaks in the immediate spectral vicinity of the elastic scattering peak, which is typically well over a million times larger. The proposed project consists of developing and demonstrating an auto-tuned ultranarrow notch filter for LFR based on a strain-tuned FBG.

Requirements: Optics and Sensing, hardware design and testing, data analysis and programming

Students will design circuit components for the front end of a 30GHz wireless transceiver module such as low noise amplifiers, mixers, voltage-controlled oscillators and power amplifiers.  We will use a Global Foundries 45nm CMOS SOI technology suitable for this work.  Students will perform circuit simulations first at the schematic level and then as the designs progress, we will use EM simulations to model the 3D behavior of all passives fabricated on the chip in preparation for fabrication.

Neural networks are information processing systems whose design is inspired by the studies of the ability of the human brain to learn from observations and to generalize by abstractions. The fact that neural networks can be trained to learn any nonlinear input-output relationship from corresponding data has led to their use in a number of areas such as pattern recognition, speech processing, control, and biomedical engineering. In the last decade, neural networks have been used in the computer-aided design (CAD) of RF/microwave components and circuits.  Neural networks can be trained to learn component and circuit data, and trained neural networks can be used during circuit design to provide instant answers to the task it learned.  In this project, the student group will develop a computer program to train and test neural works, and to use the program to create neural networks for modeling high-frequency electronic devices and circuits.

Microprocessor-Based Project:

This is intended to allow students who have finished ELEC 3907 to use their 3rd year project as a starting point, and to expand the project depth and scope into a new level.  Major technical design contents, new features and capability are expected from the 4th year project.

High-precision frequency sources are often required for different communication systems where the quality of the source needs to be evaluated. For this purpose, spectrum analyzers are often used in a lab setting. However, such solutions are not applicable for on-chip measurements. We aim to build an all-digital solution that can be easily put on a chip for mission mode diagnosis.

Requirements:

Background knowledge: Linear signal systems, Fourier transform

Circuit Background : Analog circuits, Digital circuits

Wireless sensor networks are enabled by low-cost, compact, integrated, and low power sensors and radio transceivers. They represent a key technology supporting growth of the internet of things.

Sensor networks find application in many fields including building automation, civil infrastructure monitoring, industrial monitoring, environmental and wildlife monitoring, and health care.

Any of these applications can benefit if decisions can be made in real-time on-device, such that only critical information needs to be relayed. This requires efficient machine learning (ML) models that can be deployed ‘at the edge’.

The objective of the project will be to design and implement a set of sensor nodes suited to an application such as air quality monitoring. The focus of the group will be on designing sensor elements and signal conditioning circuits that will be implemented on custom designed printed circuit boards and tested. Depending on the group’s background and interest they will attempt to recognize key sensor data patterns using efficient ML models deployed on the sensor node.

This interdisciplinary project is intended for a group of students who have sound background in physical electronics, some understanding of sensor systems, data acquisition and analysis, and are ready to design a novel wearable sensor system to detect one or more physical and chemical parameters in sweat. The project activities include sensor device design and fabrication work, sensor calibration and optimization, data analysis, and sensor interface design work.

The team will have a unique opportunity to deliver a real-world sensory system prototype for a novel health monitoring application. The team will be assisted by one PhD student from the Organic Sensors and Devices Lab and the Carleton Microfab team in sensor design and fabrication.

Requirements:

• Some knowledge of Cadence or Spice tool
• Some knowledge of a PCB layout software
• Good understanding of electronic materials and devices
• Some knowledge and experience working with Arduino or Raspberry Pi systems
• Willingness to spend time in a lab environment for device and system testing and data collection

Leveraging the SERializer/DESerializer (SERDES) capabilities of an FPGA platforms we will build a high speed communications link to transport video data.  The project can accommodate a number of areas of digital/system design included the SERDES design itself,  video protocol design and implementation and video transport building blocks such as routers, buffers and encoders/decoders.

Requirements: Students must have successfully completed ELEC3500 or equivalent

The project involves integration of an FMCW radar based on PlutoSDR module (from Analog Devices) on a standard commercial drone to create RF imagery of typical indoor or outdoor environments. The project consists of software and hardware integration of the base 2.4 GHz radar configuration with the drone, and involves investigating issues related to in-flight power supply and data retrieval, in addition to maintaining mechanical stability dependent on device integration. Further aspects involve, constructing a complete in-house radar system to replace the PlutoSDR module and exploring further extensions into higher frequency bands for better radar performance.

Requirements: Experience in PCB design, circuit assembly, RF integration and general testing desirable. Experience in Python/Matlab for RADAR software interface development is also desired. Further experience in 3D printing and mechanical assembly is a plus.

A project involving numerical simulation of a physical system will be undertaken. The project will be suitable for Electrical and Engineering Physics students, with the possible involvement of SREE students. The project will be defined by the students in conjunction with myself and can range from materials to systems. Example of projects subjects are numerical simulation of electro/optic circuits, thermal/electrical systems, electro-magnetics and device modeling of lasers. Methodologies can include finite difference, analytical techniques and machine learning/neural networks. The project is usually done in Matlab and strong programming skills are an asset. I am open to student organized projects if the primary methodology is numerical/analytical modeling. Please contact me if you have an idea.

Requirements: Good programming skills.

Nowadays, the increasing integration of renewable distributed generators is not only limited to power systems at high and medium scales, but also includes low voltage small scale systems such as at household l evel. For example, since 2009, Ontario Power Authority (OPA) has launched the micro feed in tariff (microFIT) program to provide opportunities for homeowners, farmers and small business owners to develop “micro” renewable electricity generation projects 10 kilowatts or less in size). Meanwhile, there are also a variety of electricity appliances at houses, including electrical vehicles, energy storages, lights, dryers, washers, TVs, etc. This kind of small scale power systems including both small scale pow er generators and electricity loads are called nanogrids. In specific, the residential nanogrids are usually single phase units. It is challenging to maintain the stable and economical operation of the single phase nanogrids. In this project , active power regulator will be designed to maintain the stable operation of the single phase nanogrid and thus enhance the power quality provided by the single phase nanogrid.

Requirements: The prerequisites for this project are ELEC 3508 (Power Electronics) and ELEC4602 (Electrical Power Engineering)

Objectives: is to develop an electric vehicle charging system that utilizes solar panel, battery, AC/DC converters, regulator circuitry, copper coils, and controller. The solar panel will be used to power the battery through a controller.
The core idea of this project is to harness solar energy using high-efficiency solar panels, store the captured energy in a battery storage system, and utilize this stored energy to wirelessly charge electric scooters. The system is designed to be autonomous, sustainable, and user-friendly, addressing the need for eco-friendly and efficient charging solutions in urban environments.

Technical Requirements:

• Solar Panels: High-efficiency solar panels to maximize energy capture.
• Battery Storage: Lithium-ion battery system for energy storage.
• Wireless Charging Module: Inductive charging technology for contactless energy transfer.
• Power Management System: Efficient power management to regulate energy flow between solar panels, battery storage, and charging module.
• Control System: Microcontroller-based system for managing the operations of the charging station.
• User Interface: LCD/Touchscreen display for user interaction and system monitoring.
• Safety Features: Overcharge protection, short-circuit protection, and temperature control.
• Structural Design: Weather-resistant housing for solar panels and charging components.

Functional Requirements:

• Energy Capture and Storage: System must capture solar energy and store it in batteries efficiently.
• Wireless Charging: System must provide reliable wireless charging for electric scooters.
• User Notifications: Display system status and provide notifications to users.
• Data Logging: Log energy production, storage, and usage data for analysis.

Knowledge of electromagnetic theory is a must , understanding of AVR programming, converters, solar panels, battery management systems and motor controls is required

• Simulation and design of transmitter and receiver coils
• Battery and solar panel selection
• Simulation and development of power regulatory circuit
• Simulation analysis of transmitter and receiver coil mismatching

• Build a solar power control module with MPPT
• Build a safety and protection circuit for the station
• Program AVR controller
• Build Charging/Discharging management module

Background: During the last decades, researchers have been trying to study and replicate biological neural systems to understand natural intelligence. Apart from its medical/biological applications, from an information processing viewpoint, it can eventually lead to a new generation of computational devices. Resembling to biological central nervous system, such computational devices open new horizons intelligent, low power, fast and tolerant to both hardware and data failures.

Brief description: Building a platform for artificial creatures using a Game Engine (GE). This platform will be used as an environment to develop, simulate, and study behavior of Spiking Neural Networks (SNN). Platform must be capable of running evolutionary algorithms, i.e. Genetic Algorithm (GA). As a result of simulation, successful creatures (SNN structures) will be selected to be implemented on hardware as a proof of concept.

Goals/Tasks:

• A modular simulation platform based on a game engine.
• SNN-based artificial creatures design for the simulator.
• Basic competition-fitness scenarios for the artificial creatures in the simulator environment.
• Genetic algorithm model and simulation of SNN-based creatures in the simulator.
• Hardware design and HDL simulation of SNN-based creatures.
  Hardware synthesis of the most successful creatures on FPGA.

Requirements:

• Computer programming (C++ or Python depending on the game engine platform).
• Hardware design of SNN and HDL description and simulation.
• Working and design implementation on FPGA.

Analog and mixed-signal circuits for interfacing with a memristor crossbar array in a neuromorphic computing system will be implemented. These circuits are critical as they enable the system to store weights in the memristors and perform readout functions to determine computational results. The team will initially use discrete off-the-shelf components to build these circuits on a printed circuit board (PCB) or breadboard. This approach allows for rapid prototyping and testing of various circuit designs to find the most effective configuration for interfacing with the memristor crossbar array.Once a functional design is validated using discrete components, the team will transition to using Cadence and Synopsys software to design an integrated circuit (IC) version of the interface circuits. While the project timeline does not permit actual fabrication of the IC (unless we can figure out how to use the Department’s in-house fab), the design phase will provide valuable experience in IC design and simulation. By working on both discrete and integrated versions, the team will ensure that the final system is robust and capable of efficient operation within the constraints of the project.

The skillset developed in this subproject will be related to analog and mixed-signal circuit design, including simulation and layout of integrated circuits using industry standard tools from Cadence and Synopsys. A more detailed description of the project is available at the external link, or for even more detail contact me directly.

Requirements: Interest in circuit design is a must. It would be useful to take ELEC4609 in the Fall term and ELEC4707 in the Winter term. If you have experience with Cadence tools like Virtuoso or Maestro that would be very helpful.

This project is aimed at the design and evaluation of thin film elements that will act as “memristors” to be used in arrays to form Cellular Automata or Neural Networks. The devices will be printed onto flexible substrates and designed to be integrated with other printable circuit components. The work will involve a combination of:

• material selection and formulation
• device design and layout
• device fabrication and testing

The students will be involved in all aspects of the work, as listed above. In particular they will be responsible for designing test structures compatible with the printing/processing equipment available and then printing and testing the optical and electrical characteristics of their designs. In conjunction with the supervisor(s) they will ensure compatibility with other printable components and materials. They will also be responsible for the interfacing of their devices/device arrays to the required control and sensing electronics designed by other students in the group and required for the operation of the overall project.