Wi-Fi Channel State Information (CSI) is a robust, privacy-preserving modality for Human Activity Recognition (HAR). Since raw CSI suffers from hardware desynchronizations and noise, preprocessing is vital. This study conducts an empirical ablation of CSI preprocessing using a fixed Two-Stream 2D CNN(Convolutional Neural Network) to quantify its impact on classification accuracy and latency. Results reveal that preprocessing, apart from architectural complexity, is the primary driver of the accuracy-latency trade-off. Computationally heavy methods like Hampel filtering introduce massive latency (>162 ms) without accuracy gains. In contrast, lightweight frequency-domain filtering consistently yields superior results. Specifically, dual-stream Butterworth bandpass filtering achieves 96.43% accuracy with only 49.08 ms latency. These findings demonstrate that isolating motion-relevant frequencies enables efficient, high-performance HAR suitable for real-time edge deployment.
Yiğit Can Çelik, Erdem Bera Emiroğlu, Sajjad Baghaee et al.· Signal Processing and Commun...· 0 citations
Deploying reinforcement learning (RL) agents for heating, ventilation, and air-conditioning (HVAC) control across climatically diverse buildings is challenging, as policies trained in one climate often fail to generalize to others. We investigate transfer and meta-reinforcement learning for building climate control using Sinergym with a custom EnergyPlus model of a real room, comparing Double Deep Q-Network (DDQN) and Model-Agnostic Meta Learning DDQN (MAML-DDQN) across three transfer scenarios on a target very hot climate. Fine-tuned DDQN reduces temperature violations by a factor of 35 over the from-scratch baseline and improves reward by 86% over zero-shot transfer, while MAML-DDQN converges in less than half the fine-tuning episodes and exhibits substantially lower variance across seeds. Results highlight a key trade-off: MAML-DDQN is preferable at adaptation-focused deployments, while fine-tuned DDQN is superior when maximum performance is the objective.
Ulaş Filiz, Sajjad Baghaee, Mubeen Ahmed Fayyaz et al.· Signal Processing and Commun...· 0 citations