Edge-side Prognostics and Health Management (PHM) for Electric Vehicles (EVs) demands vehicle sub-systems Remaining Useful Life (RUL) predictors running on automotive-grade Microcontroller Units (MCUs) under tight memory, latency, and energy budgets. Classical Knowledge Distillation (KD) often degrades long-horizon accuracy and fails on the complex forecasting-to-decision pipeline for Silicon-Carbide (SiC) traction-inverter power modules. We propose Neuro-Modulated Knowledge Distillation (NM-KD), distilling a Multi-Scale Temporal Fusion Transformer (TFT-MS) into a lightweight model deployable on MCUs. NM-KD adapts loss weights via learned gates, bounds student sensitivity with a Lipschitz regularizer, and leverages Stochastic Weight Averaging (SWA) modulation. The TFT-MS teacher is benchmarked against several architectures spanning six design families, while the student selection is validated against five MCU-deployable alternatives. On SiC power devices, the student matches short-horizon performance and outperforms the teacher at long horizon (50-step accuracy 95.13% vs. 94.32%). On-target deployment reduces non-volatile memory by 83.12%, latency by 81.73%, and energy by 60.57%. Cross-batch generalization on 21 modules from two independent manufacturing lots confirms robustness to process variability. A lightweight temporal remapping converts accelerated-cycle predictions into field-relevant RUL estimates. NM-KD enables fast, low-power, accurate PHM on automotive Electronic Control Units (ECUs).

Speaker recognition is the task of identifying or verifying a person’s identity using their voice. This problem involves challenges like variations in speech due to emotional states, health conditions, heterogeneity of microphone models, different environments and background noise. Accurate speaker recognition is critical for security, personalization, and forensic applications. Applying a CNN with Monte Carlo dropout can enhance Speaker Recognition by enabling robust uncertainty-aware predictions, making the presented architecture particularly effective for smaller, noisy datasets without the need for large-scale pre-training. This approach helps mitigate overfitting and improves generalization, making it effective in handling diverse speech patterns. The designed deep learning model showcases superior performance in multiple dimensions, achieving a peak validation accuracy of 93.27% for speaker recognition on a specific dataset recorded in the wild by phone, and 0.030 of EER, showing competitive performance with respect to state-of-the-art baselines.

The aims of this research are to introduce a new transparent control paradigm and to consolidate Grid resources by a statistical approach based on Agents and on Queuing Theory. These goals are obtained through the creation of a new category of Agents called "Prophet Agents" with an agent-based peer-to-peer framework named JADE, created using the Java programming language. The goal of Prophet Agents inside a Grid is to estimate workload and completion time of a given job class in a specific class of servers. Moreover, agents are used to automate the "matchmaking" process and to consolidate Grid resources by adding new computing servers if necessary.