INSys Lab
The Intelligent Neural & Signal Systems Laboratory (INSys Lab) develops efficient, interpretable, and hardware-aware methods for intelligent signal processing and edge computing under strict power, latency, and memory constraints.
Overview
INSys Lab adopts a systems-level research perspective that spans algorithm design, evaluation methodology, and deployment-aware considerations. The lab’s research emphasizes approaches that are not only accurate, but also practical for embedded, edge, and cyber-physical platforms.
Research Focus
- Intelligent RF signal analysis and modulation classification
- Lightweight and hardware-aware neural and statistical learning models
- Signal-domain feature extraction and interpretable learning frameworks
- Edge intelligence for embedded and resource-constrained systems
- Robust and trustworthy intelligent signal processing architectures
Tools and Platforms
- Python and MATLAB for signal analysis and algorithm development
- RF signal simulation and statistical feature extraction pipelines
- Embedded and edge AI development environments
- Hardware-aware evaluation workflows for runtime and resource analysis
Research Philosophy and Methodology
INSys Lab emphasizes principled and deployment-conscious research methodologies. Rather than relying exclusively on large or opaque models, the lab prioritizes approaches that balance performance, efficiency, and interpretability.
- Integrating signal processing insights with learning-based models
- Designing algorithms with explicit consideration of hardware constraints
- Evaluating methods using accuracy, runtime, memory, and power metrics
- Bridging theoretical foundations with system-level validation
Student Involvement and Mentoring
INSys Lab provides hands-on research opportunities for undergraduate and graduate students through independent study, senior design projects, and directed research.
Details of current and previously mentored students are available on the Scholars page.
Scholarly Output
Research conducted within INSys Lab contributes to peer-reviewed journal articles, conference publications, and technical reports in signal processing, machine learning, and hardware-aware systems.
A complete list of publications is available on the Publications page.
Collaboration and Engagement
INSys Lab welcomes collaboration with academic researchers, industry partners, and government laboratories interested in intelligent signal systems, RF analysis, and edge computing. Collaborative activities may include joint research projects, co-supervised students, or exploratory system-level studies.
Lab Status
INSys Lab is currently in its establishment phase. Research activities are conducted through simulation-driven workflows and prototype-based experimentation, with laboratory infrastructure development ongoing.
Contact
Prospective students and collaborators are encouraged to reach out via email.
Institutional Affiliation
INSys Lab is a faculty-led research group within the Department of Electrical Engineering at Illinois State University.