Liquid Crystal Reconfigurable Intelligent Surfaces (LC-RIS) are crucial for future millimeter-wave and terahertz wireless systems due to their ability to dynamically control electromagnetic waves. As LC-RIS requires very fast operation, an appropriate voltage supply with good response time must be developed to adjust each element independently. In this work, we present the design and implementation of a custom voltage supply tailored for LC-RIS applications. Our system integrates four daisy-chained LTC2688 digital-to-analog converters (DACs), providing 64 independently adjustable voltage outputs. We achieve a worst-case update time of 273 µs for all outputs, using a Raspberry Pi Pico as the SPI master with minimal C code. The voltage supply meets critical system demands, including the generation of 1 kHz square waveforms, a voltage range of ±10 V, and sub-millisecond response times, while maintaining ease of control through a programmable interface. We also developed a dedicated on-board power supply to meet the LTC2688’s multiple power rail requirements, enabling the entire system to operate from a single external input between 3.7V and 18 V. Furthermore, our design is scalable, allowing multiple boards to be daisy-chained for larger LC-RIS arrays. Our solution demonstrates a compact, fast, and scalable voltage control system suitable for high-performance LC-RIS platforms.

This paper addresses the growing privacy concerns associated with Wi-Fi sensing, a technology that uses existing wireless infrastructure to extract sensitive information such as human presence, motion, breathing, and typing. Although Wi-Fi sensing enables cost-effective and scalable applications in smart homes, healthcare, and security, its passive nature allows adversaries to eavesdrop on Wi-Fi transmissions and analyze signal patterns for private data extraction. While researchers have proposed various defense mechanisms to counter these threats, a unified framework for categorizing and comparing these approaches, particularly in terms of their trade-offs and practicality, has been notably absent. This paper aims to fill this gap by providing a structured categorization of existing defense mechanisms against Wi-Fi sensing, including a detailed analysis of their limitations, and informing the design of more robust and privacypreserving solutions. The key contributions include a comprehensive taxonomy of defense mechanisms based on their core strategy and operational layer, a comparative analysis of their effectiveness, feasibility, and trade-offs, and an identification of open challenges for future research.

WiFi sensing has shown great promise in applications such as activity recognition, human identification, and health monitoring. However, models trained on Channel State Information (CSI) data often suffer from poor generalizability due to high variance and inconsistent reporting practices, especially in small-data regimes. Despite increasing attention to deep learning-based approaches, the community lacks standardized guidelines on handling variance and overfitting across architectures and datasets. In this work, we first review existing robustness strategies employed in related fields to identify techniques suitable for WiFi sensing. Based on this survey, we systematically benchmark five representative methods—stability training, mixup, coupled weight decay, early stopping, and label smoothing—selected for their theoretical grounding and prior success in mitigating variance. Our evaluation spans three model types (LeNet, LSTM, CNN+GRU) and two CSI datasets (Widar, NTU-Fi), using stratified 5-fold cross-validation with repeated trials to ensure reliable variance estimation. Results show that (1) stability training consistently improves moderately performing models, (2) coupled weight decay is especially effective for LSTMs, and (3) combining techniques can harm performance in near-saturated scenarios. Our findings offer actionable, architectureand dataset-specific guidelines for improving robustness and reproducibility in WiFi sensing research.

Liquid-crystal reconfigurable intelligent surfaces (LC-RIS) need hundreds of stable bias voltages, yet most existing controllers are slow, expensive, or hard to scale. This thesis prototypes a lean 64-channel driver that combines one 1 kHz NE555 square-wave source with per-channel amplitude control via daisy-chained AD5263 digital potentiometers and OPA4197 buffers. Each channel supplies a continuous ±10 V swing in 256 steps, and a Raspberry Pi Pico streams updates in 31 μs, fast enough not to limit the LC’s millisecond-scale response. The work is still a proof of concept: it has been validated only with 64 channels on the bench, long-term drift and true large-panel scaling remain open questions. Nevertheless, the prototype points toward a practical, low-cost path for future LC-RIS control.

Wi-Fi sensing poses a serious threat to privacy due to its passive and covert nature. Nonetheless, the field of defenses is largely underdeveloped. This survey draws from the vast field of radar systems and their state-of-the-art countermeasures to discover potentially new Wi-Fi sensing defenses. The study identifies four particularly promising approaches: false target generation for deceptive jamming, reconfigurable intelligent surfaces (RIS) for dynamic signal manipulation using metasurfaces, encrypted waveform design for secure transmission, and hybrid region-based techniques for spatial access control. By transferring insights from radar systems to the context of Wi-Fi sensing, this work lays the groundwork for the future development of practical and resilient privacy-preserving defenses.