This paper presents a power-efficient and quickly-settled chopper-stabilized capacitively coupled instrumentation amplifier (CCIA) for neural recording applications. To achieve a relatively-low high-pass corner frequency while achieving fast-settling, a duty-cycled resistor (DCR) based very-large time constant (VLT) integrator is proposed. By stacking inverters and splitting the input capacitor network, the input stage of the CCIA achieves four-time current reuse, significantly improving the power-efficiency. A prototype in 180-nm CMOS technology has 2.2µV input-referred noise (IRN) with 5kHz bandwidth (BW) while consuming only 2.92µA current from a 1.2V supply, achieving a noise efficiency factor (NEF) of 2.1 and a power efficiency factor (PEF) of 5.2. Simulations show that it can settle within 10ms after powering on with a maximum electrode DC offset of 50 mV.

Memory Electromagnetic Radiation (EMR) allows attackers to manipulate the DRAM of infiltrated systems to leak sensitive secret information. Although most of the existing works have demonstrated its feasibility, practical concerns, such as the ideal electromagnetic environment and stationary attacking layout, make the covert channel attack less convincing, especially in vulnerable sites such as offices and data centers. This work removes the above impractical assumptions to uncover the potential of memory EMR by proposing the first parallel EMR covert communication protocol. Our design reshapes the current "1-to-1" covert communication mode to "n-to-1" mode via a novel pattern-based 2-dimensional symbol encoding scheme, allowing multiple victim computers to simultaneously perform data exfiltration to one attacker (the receiver) without mutual interference. Meanwhile, this novel scheme design also enables the very first mobile attacker, i.e., a smartphone connected to a software-defined radio (SDR) dongle, to capture parallel memory EMR signals in a volatile environment. Extensive experiments are conducted to verify the performance in a volatile environment with different parameter configurations, distances, motion modes, shielding materials, orientations, hardware configurations, and SDR platforms. Our experimental results demonstrate that FreeEM can support up to 4 parallel memory EMR transmissions to achieve an overall throughput of 625Kbps and a decoding accuracy of 96.88%. The maximum communication distance can reach up to 20 meters.

Human faces have been widely adopted in many applications and systems requiring a high-security standard. Although face authentication is deemed to be mature nowadays, many existing works have demonstrated not only the privacy leakage of facial information but also the success of spoofing attacks on face biometrics. The critical reason behind this is the failure of liveness detection in biometrics. This work advances most biometric-based user authentication schemes by exploring dynamic biometrics (human facial activities) rather than traditional static biometrics (human faces). Inspired by observations from psychology, we propose the mmFaceID to leverage humans' dynamic facial activities when performing word reading for achieving robust, highly accurate, and effective user authentication via mmWave sensing. By addressing a series of technical challenges of capturing micro-level facial muscle movements using a mmWave sensor, we build a neural network to reconstruct facial activities via estimated expression parameters. Then, unique features can be extracted to enable robust user authentication regardless of relative distances and orientations. We conduct comprehensive experiments on 23 participants to evaluate mmFaceID in terms of distances/orientations, length of word lists, occlusion, and language backgrounds, demonstrating an authentication accuracy of 94.7%. We also extend our evaluation in a real IoT scenario. By speaking real IoT commends, the average authentication accuracy can reach up to 92.28%.

The Rolling Shutter (RS) effect and motion blur are common challenges in images captured by CMOS cameras during dynamic scenes. Inspired by biological vision principles, event cameras capture intensity changes asynchronously with low latency, providing valuable insights into image degradation during exposure. This study addresses the dual challenges of rolling shutter correction and deblurring using event data, merging them into a unified one-stage network. This streamlined approach reduces cumulative errors and inference time compared to traditional two-stage methods. To achieve this, we introduce an Event Representation for Rolling Shutter Deblurring, which explicitly models the conversion relationship between the input RS blurry frame and the latent image using events. To enhance the fusion of image and event information, we present a Time-guided Cross-Modal Attention module. Furthermore, we improve performance by incorporating a Multi-Scale Context-Aware Transformer Block, effectively addressing varying degrees of distortion and blurriness using a multi-scale attention mechanism. Extensive experiments validate that our method outperforms existing state-of-the-art approaches.