This project explores innovative solutions to retrofit an existing building, Building E1 at the National University of Singapore, into a Net Zero Energy Building (NZEB) by targeting four key subsystems: Plug Load, Lighting, Air Conditioning and Mechanical Ventilation (ACMV), and Solar Power generation. By integrating technologies such as TP-Link smart plugs, PIR motion and daylight sensors, hybrid fans, cool paints, and an expanded solar PV system, the team aims to reduce energy consumption and maximise renewable energy generation.

The team consists of ECE students (Chua Pei Da Bernard, Tan Ze Jun, Ong Celine & Lee Wei Jian). 

A Qt-based C++ software providing an intuitive interface that lets users dynamically connect circuits to test various configurations of a RISC-V microarchitecture processor. It functions as a cycle accurate simulator for educational purposes.

The project was developed by ECE student (Hao Lei). 

The capstone team designed and developed the sensors, power supply, communication and firmware for the joint Cansat Team (ECE and EDIC). The team has successfully prototyped and integrated all the necessary sensor and electronic. In addition, the capstone team has successfully demonstrated the data transmission from Cansat electronic system to the ground station software that was developed by their teammate. The printed circuit board (PCB) will be fabricated once the necessarily Cansat mechanical testing has been completed.

The team consists of ECE students (Gavin Chia Chui En, Neo Jun Qiao & Ee Ansheng). 

Traditional architectures separate memory and computation, resulting in high energy costs and latency due to constant data shuttling. As AI expands into edge applications like wearables and IoT, there's an urgent need for low-power, high-performance alternatives to GPU-based inference systems.

This project explores analog compute-in-memory (CIM) using mem-transistor-based processing elements, where data is processed directly within memory. Leveraging the natural summing behavior of analog circuits, CIM significantly reduces power consumption for AI workloads.

a CIM emulator integrated with a PYNQ-Z2 FPGA was developed, offloading the MAC-heavy fully connected layer of a CNN. The result: a live digit recognition demo that showcases the feasibility and efficiency of CIM for edge AI inference.

The team consists of ECE students (Wang Yanxiao Austin, Men Jiying, Pier Chia Shao Rong & Zhang Tianyue). 

Power Quality Events (PQEs) such as voltage sags, interruptions, harmonics, and transients can critically impact device performance and business operations. For example, during a voltage sag, constant power loads demand higher current, potentially overwhelming the power supply and reducing productivity. Additionally, with increasing adoption of renewable energy, power converters introduce harmonics—non-useful current components that cause I²R losses and equipment overheating.

This project presents a real-time AI-driven Smart Power Quality Analyzer designed to detect and classify PQEs to enable timely corrective actions. It integrates data generation, processing, and machine learning-based classification of various PQEs.

The trained model was deployed using a real-time simulation setup with the Ametek AST3003 (for signal generation) and dSPACE (for interfacing). The system accurately identifies and displays PQEs in real time, enabling continuous, intelligent monitoring of power quality.

The team consists of ECE students (Clinton Yong Tzen Wei, Wong Zheng Wei, Wu Jialiang & Thirumoorthy Iswarya). 

In this project, we aim to support Singapore’s ‘30 by 30’ food security initiative which is to produce 30 per cent of its nutritional needs locally by 2030. Our project focuses on developing an Artificial Intelligence of Things (AIoT) system using image processing and machine learning (ML) to determine the optimal lighting conditions for crops at different growth stages. Data captured by the microcontrollers is transmitted to a cloud server, where it is processed, stored and analysed. Based on these insights, the lighting intensities and wavelengths are automatically adjusted to enhance crop yield while reducing energy and labour costs. Key efforts included configuring cloud communication with the ESP32-CAM, setting up SQL databases for data storage, integrating ML models for lighting optimisation and also creating a user interface using Power Apps. The casing for the ESP32 camera was also an important detail to ensure the sufficient protection and accessibility in a real-world farm setting. Together, these developments culminated in a functional, scalable AIoT system suitable for modern indoor farming applications. With continued innovation and collaboration, this work lays the foundation for smarter, more efficient, and sustainable indoor farming practices.

The team consists of ECE students (Goh Jun Heng, Khong Chun Kit, Malcolm Ang Shang Hui & Neo Kai En, Alyndia). 

This paper explores the use of various Symbolic Regression techniques, most notably Symbolic Boolean Regression and Symbolic Regression Enhanced Decision Trees, to develop interpretable models for the prognostication of 90-Day PFO in SICH patients.  Traditional Machine Learning models and the Uni-ACS framework, an algorithm to develop Clinical Scoring Systems from traditional Machine Learning models, were also applied to evaluate the competitiveness of the Symbolic Regression techniques.  The trade-off between predictive power and complexity was investigated for all the developed models, and the clinical feasibility of each model was evaluated to determine if less complex models were necessarily more interpretable and practical.

The project was developed by ECE student (Mahesh Kridhay). 

The project addresses a persistent challenge in the fast-growing air cargo industry as the sector still relies heavily on paper-based processes, the load sheet. Load sheets are critical safety documents that are manually updated by ground staff which often leads to miscommunication. In 2020 alone, 32% of 375 loading-related incidents were due to load sheets. Hence, the project explores the implementation of computer vision to accurately detect and interpret handwritten annotations on load sheets.

The project was developed by ECE student (Paul Khoo Bob June) in collaboration with Boeing.

Nanosphere photolithography is a scalable, cost-effective fabrication technique for periodic nanostructures via the creation of photonic nanojets (PNJ). PNJ formation follows Mie scattering theory to produce subwavelength high intensity beams when light is focus through a spherical nanopartical. In this project, fabrication of unique subwavelength nanostructures by manipulating the sphere to wavelength ratio (swr) and the number of sphere layers was explored. Simulation was carried out via the finite-difference time-domain method using MIT Electromagnetic Equation Propagation (MEEP) software. Nanosphere layers with diameters of 0.5 to 3 µm were deposited on resist coated silicon substrates -- corresponding to swr of 1.3 to 8, with a 375 nm laser wavelength. Physical patterns were fabricated by optimizing the exposure dose and development time in order for the patterns to be formed in resist. Results show double-layered hexagonally close packed (hcp) 3um nanospheres create additional high and low intensity regions around each PNJ. These results provide a new strategy for creating novel subwavelength nanostructures over relatively large areas through multi-layer nanosphere photolithography through controlling sphere layering and size selection.

The project was developed by ECE student (Ip Wing Fai Ronald). 

Multi-agent robot systems have gained significant attention in autonomous robotics, especially for applications like Intelligence, Surveillance, and Reconnaissance (ISR). ISR plays a crucial role in military and security operations by enabling real- time monitoring, data collection, and threat detection over large areas. This project demonstrates a low-cost, scalable drone fleet system for real-time ISR tasks. With Skybrush for coordination and Neural Radiance Fields for 3D mapping, the system achieves reliable performance in flight, video latency, and environmental reconstruction. 

The project was developed by ECE student (NeoSiao Bee, Dawn).