Projects in Communications and Information Systems

This project aims to explore and implement accelerators on FPGAs or ASICs for Combinatorial Optimization Problems, such as Boolean Satisfiability (SAT) problems. The design flow includes writing RTL Verilog code, developing FPGA commands, compiling and programming the FPGA board. Innovative solutions may have the opportunity to be implemented in GlobalFoundries 12nm FinFET technology and tested in the lab.

Required Knowledge & Skills:

• Digital circuits
• FPGA development
• ModelSim, Quartus
• Verilog, SystemVerilog

Supervisor: Prof. Shiyu Su

Email: shiyu.su@uwaterloo.ca

Artificial Intelligence (AI) systems, particularly those based on deep neural networks (DNNs), can be interpreted as nonlinear function approximators. The expressive power of AI in engineering applications largely stems from its capacity to handle complex nonlinear relationships. In current DNN architectures, this nonlinearity is primarily introduced through fixed activation functions such as ReLU, Sigmoid, Tanh, GELU, Swish, and Leaky ReLU. While these functions are simple and efficient, they offer limited flexibility and nonlinear expressiveness. As a result, large-scale DNNs with many layers and parameters are often required to model intricate nonlinear phenomena, leading to increased computational cost and training complexity, and often without significantly improving approximation quality. 

To address these limitations, we have recently developed an analytic, learnable activation function capable of approximating any continuous nonlinear function to arbitrary precision. This development opens the door to a new class of compact, high-capacity neural networks with greatly enhanced nonlinear modeling power. When integrated with mathematical frameworks such as the Kolmogorov Representation Theorem, this novel activation mechanism enables the design of small-scale yet powerful DNNs that can approximate complex engineering functions that are otherwise intractable or require prohibitively large models. 

Leveraging our learnable activation functions, this project aims to develop a new AI framework tailored for engineering applications, where more efficient, compact, and theoretically grounded AI systems can be built for engineering design, control, and optimization. 

Supervisor: Prof. En-Hui Yang 
Email: ehyang@uwaterloo.ca 

The goal of this course project is to empower students with antenna design skills to meet the increasing demand for custom wireless internet of things (IoT) devices. The students will decide upon a given IoT application. The students will then use a conceptual CAD model for the IoT device and utilize numerical computer aided design tools (Ansys HFSS & Keysight ADS) to design a suitable antenna solution.

Supervisor: Prof. G. Shaker (Adjunct)
Email: gshaker@uwaterloo.ca

The goal of this course project is to familiarize students with antenna concepts that affect the performance of wireless devices. The students will learn how to perform basic antenna measurements in an anechoic chamber. The students will then need to propose a setup that characterizes/demonstrates the properties of an antenna system and provide detailed material to explain these properties. These properties include (but are not limited to) impedance, efficiency, frequency of operation, bandwidth, gain, polarization, beam width, RCS, noise immunity, and data throughput.

Supervisor: Prof. G. Shaker (Adjunct)
Email: gshaker@uwaterloo.ca

It is hypothesized that the gradient map of a benign image will be very different from that of a corresponding adversarial example in terms of their statistic properties, spatial shapes, and visualization. This project will first analyze the differences between the gradient maps of benign images and the gradient maps of corresponding adversarial examples. Based on the analysis, the project will then explore novel ways to detect adversarial examples through gradients. 

Supervisor: Prof. En-Hui Yang 
Email: ehyang@uwaterloo.ca 

This project aims to develop high-speed, low-noise integrated circuits for highly integrated and scalable quantum computer systems. It involves behavioral modeling and transistor-level analog/mixed-signal/RF integrated circuit design using advanced technologies, including GlobalFoundries 22nm FDX-SOI and 12nm FinFET technology. An NDA is required to access these technologies. Students will have the opportunity to participate in a tape-out.

Required Knowledge & Skills:

• Analog/mixed-signal/RF integrated circuit design
• Signal processing
• Cadence
• Python, or MATLAB

Supervisor: Prof. Shiyu Su

Email: shiyu.su@uwaterloo.ca

Deep neural networks (DNNs) are vulnerable to adversarial examples, maliciously modified raw input data which is imperceptible to human vision, but once fed into DNNs, can lead DNNs to produce incorrect outputs. The existence and easy construction of adversarial examples pose significant security risks to DNNs, especially in safety-critical applications, including visual object recognition and autonomous driving. The objective of this project is to model adversarial perturbations in DNNs through statistical analysis and DNN visualization. The established model will provide a basis for developing a radically different approach for detecting adversarial examples. 

Supervisor: Prof. En-Hui Yang 
Email: ehyang@uwaterloo.ca 

The objective of this project is to develop novel methodologies for image and video processing, optimization, compression, transmission, and streaming based on advanced technologies including perceptually motivated and deep learning approaches. Working with a group of experienced researchers and fellow students, the student will carry out research in the forms of algorithm and software development, experiment design and setup, perceptual testing, and data processing and analysis

Supervisor: Prof. Zhou Wang
Email: zhou.wang@uwaterloo.ca

Deep neural networks (DNNs) are vulnerable to adversarial examples, maliciously modified raw input data which is imperceptible to human vision, but once fed into DNNs, can lead DNNs to produce incorrect outputs. The existence and easy construction of adversarial examples pose significant security risks to DNNs, especially in safety-critical applications, including visual object recognition and autonomous driving. One way to partially mitigate this problem is to formulate deep learning as a type of minimax problem instead of the standard minimization problem. The objective of this project is to explore and implement effective methods for solving such a minimax problem, yielding robust deep learning.

Supervisor: Prof. En-Hui Yang
Email: ehyang@uwaterloo.ca

The blockchain privacy is implemented  by a zero knowledge succinct noninteractive argument of knowledge (zkSNARK) proof system.  FRI (fast Reed Solomon code Proximity)  protocol is a popular protocol employed in a number of efficient and practical zkSNARK systems. The project is to implement this protocol and test the performance when it is embedded into the existing zero knowledge proof systems for blockchain privacy.

Supervisor: Prof. Guang Gong
Email: ggong@uwaterloo.ca

Summary:

“A word’s meaning is characterized by the contexts in which it appears.”— Zellig Harris (1954).

Modern large language models (LLMs) such as GPT, LLaMA, and BERT have revolutionized language understanding by embedding Harris's insight into their core: meaning emerges from word co-occurrence and distributional similarity. However, despite their success, current LLMs suffer from a crucial shortcoming—they lack a principled mechanism to compute the actual contextual meaning of a word given its surrounding text.

In these models, the final-layer representation of a word token is optimized for predicting the next token rather than for explicitly capturing the conditional semantics of a word in context. As a result, polysemy resolution, precise semantic interpretation, and context-sensitive word understanding remain unsolved in a strict sense.

To overcome this limitation, we have recently developed a method that directly tackles the problem of extracting the true contextual meaning of a word from its surrounding text. Building on this foundation, this project aims to construct a new generation of LLM-based dictionaries that:

• derives word senses from a semantic universe encoded in high-quality, large-scale written corpora (e.g., FineWeb-Edu);
• clusters, for each word, its usage-based probability distributions to discover the number and content of senses automatically;
• disambiguates the meaning of a word in any given sentence through algorithmic inference, rather than relying on user-selected, human-curated glosses;
• deliver contextually accurate definitions of a word at the point of use—for both human readers and downstream applications

This work not only pushes forward the frontier of dictionary construction but also lays the groundwork for semantic organization and understanding of unstructured textual data.

Supervisor: Prof. En-Hui Yang
Email: ehyang@uwaterloo.ca

While deep learning-based artificial intelligence (AI) has propelled the information age to unprecedented heights, its current trajectory—characterized by scaling up massive deep neural networks (DNNs) and leveraging vast datasets—faces significant limitations. These include enormous demands for energy, computing resources, and high-throughput networking, alongside persistent challenges in AI interpretability and security. Moreover, the high costs associated with this approach restrict accessibility to a few large players, limiting its societal impact and potential to create broader economic opportunities. 

To address these challenges, this project aims to explore and develop alternative AI paradigms that prioritize efficiency, interpretability, security, and inclusivity. By leveraging concepts and techniques from information science and probability theory, the project will initially focus on enhancing the nonlinearity, structural properties, and training/inference efficiencies of DNNs, a critical step towards achieving the final goal. 

Supervisor: Prof. En-Hui Yang 
Email: ehyang@uwaterloo.ca 

This project focuses on developing various integrated circuit building blocks for a wideband analog-to-digital converter (ADC) for blocker-resilient wireless communication. The design includes sub-ADCs, digital-to-analog converters, analog filters, and digital signal processing units. The workflow covers behavioral modeling, schematic implementation, layout, and post-layout optimizations using GlobalFoundries 22nm FDX-SOI technology. An NDA is required to access this technology. Students will have the opportunity to participate in a tape-out.

Required Knowledge & Skills:

  •  Strong understanding of analog and mixed-signal integrated circuits.

  •  Experience with Cadence and MATLAB.

Supervisor: Prof. Shiyu Su

Email: shiyu.su@uwaterloo.ca