Adaptive power management in Electric Vehicles (EVs) requires accurate power prediction. Although deep learning models have emerged as highly effective for time-series forecasting in this domain, their performance is prone to degradation when exposed to data with distributions di…
Hyperspectral sensing enables material identification; however, state-of-the-art spectrometers are costly and bulky, which limits their use in mobile applications. We address this by proposing sparse spectrum reconstruction from narrowband photocurrents using a pseudoinverse-guid…