In this project, the student will be designing an anti-reflective coating with different patterns for solar cells using SolidWorks, 3D/4D printing them, and characterizing their transmittance and reflectance spectra using UV-Vis-NIR spectroscopy.
Previous Research Projects
At the CCSL, we are engaged in research and teaching on wireless communication methods for future wireless communication systems. In future wireless communication 5G and beyond, an extremely high number of heterogeneous devices, such as smartphones, sensors, robots, and vehicles, will communicate with each other.
You will explore how machine learning accelerators on FPGAs can be used for accelerated inference, with consideration for a connected setting where clients need to send data to an inference engine implemented on FPGA.
This project aims to develop an intelligent system to detect a person’s posture when sitting in a wheelchair. The main use of the proposed system is to detect an improper posture to prevent major health issues.
Extreme-value theory provides parametric statistical models to describe the behavior of extreme events, such as floods or heat waves, but their application to complex non-stationary datasets, with non-linear and/or non-additive effects of covariates, has been very limited so far.
The main crucial and challenging issue in solar energy production is the intermittency of the photovoltaic (PV) system due to weather conditions. In particular, the variation of the temperature and irradiance can have a profound impact on the quality of electric power production.
Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With the increase of installed WWTPs worldwide, the modeling and forecast of their energy consumption have become a critical factor in WWTP design to meet environmental and economic requirements.
Accurate prediction of wind power is important in sustainably integrating wind power in a smart grid. The goal of this project is to design an efficient approach for Wind Power Prediction using machine learning models.
Accurate forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials andstaff). The goal of this project is to apply deep learning methods (e.g., LSTM, BiLSTM) for COVID-19 transmission forecasting.
The project aims at developing models and algorithms for robot manipulator control to grasp and use mechanical tools. Going beyond simple grasping of objects, this project will explore possibilities to use standard robot grippers along with a set of mechanical tools in robotic assembly applications.
The project investigates the possibilities for more efficient training of deep learning models at the edge. It will explore how low-power devices can contribute to the training of models without the use of high-powered GPUs.
High-density non-volatile memories and ultra-low power-area efficient logic elements are the main components of the next generation neuromorphic computing which has been introduced in recent years to break the Von Neumann throughput bottleneck.
Implement an XR (VR/AR) visualization system that will be able to request to see any known molecule that is used in the high-school study curriculum and you will be able to see this molecular from various angles and under different visual representations.
Finding evidence for life, resource, and water on Mars has been a decades-long ambition for our human beings, which has spent billions of dollars to send machines wheeling over, poking, and probing the Red Planet.
The micro-LED displays are the next-generation displays for AR and VR. A key technology is the development of InGaN-based red LEDs instead of the current InGaP-based red LEDs. We have achieved the red LEDs, but these are required to improve their efficiency and narrower FWHMs. The SSI student will develop the novel LEDs together with our members.