Research Lab
🌈 Multispectral Analysis
Multispectral analysis involves interpreting data from multiple bands of the electromagnetic spectrum — visible, infrared, thermal, and radar — to identify chemical, physical, or structural properties of surfaces. gn to site with Framer, the web builder for creative pros.
How AI is integrated
We apply deep convolutional networks and Transformer-based architectures enhanced with physical constraints (e.g. radiative transfer models, atmospheric absorption coefficients). PENN aligns these models with Maxwell’s equations and optics principles, allowing precise calibration and interpretation of reflectance data.
We leverage advanced AI techniques to enhance spectral analysis and environmental monitoring.
This domain focuses on the analysis of audio signals including mechanical vibrations, ultrasonic pulses, and environmental noise.
How AI is integrated
We use AI models structured with wave equations (Helmholtz and Navier-Stokes where applicable) to extract signal features while denoising through physics-guided filters. PENN architectures reduce false positives in complex environments by recognizing the propagation behaviors of sound waves.
We leverage advanced AI techniques to enhance spectral analysis and environmental monitoring.
🧠 NeuroSignal AI
Electroencephalography (EEG) captures electrical activity of the brain via electrodes on the scalp. It is highly sensitive, low-SNR data requiring precise decoding.
How AI is integrated
We train deep neural networks using priors from neural mass models (e.g. Jansen-Rit or Kuramoto models), allowing the system to interpret brainwave patterns in alpha, beta, theta, and gamma bands with high specificity.
We leverage advanced AI techniques to enhance spectral analysis and environmental monitoring.
Modern systems deploy dozens of heterogeneous sensors (temperature, pressure, gas, optical, motion) which produce asynchronous, high-volume time-series data.
How AI is integrated
We create hybrid neural architectures that fuse temporal and spatial data across multiple sensor types. PENN models embed governing equations (Fourier’s law, Darcy’s law, etc.) as regularizers, enabling robust anomaly detection and prediction.
We leverage advanced AI techniques to enhance spectral analysis and environmental monitoring.
