Sensors

Offline handwriting functions as a practical biometric sensing modality for identity verification and controlled stress testing of recognition pipelines, yet recovering writer-specific stroke appearance from only a few enrollment samples remains difficult. In trajectory-based rendering used to probe such sensing systems, stroke width is often fixed or only weakly adapted, so the sensed thickness cue fails to reflect individual writing habits. The key obstacle is an operational reliability imbalance: a learned width estimator and handcrafted geometric priors disagree in trustworthiness across writers and even along a single stroke, which makes a constant fusion rule brittle for few-shot personalization. We propose a confidence-guided dynamic fusion framework whose primary method is a deterministic confidence-to-$\beta$ map (not a learned gate): point-wise pseudo-label confidence from reference images sets per-point blending weights between learned and rule-based widths, with “reliability” denoting this operational blending prior. On 73 HWDB writers under a strict writer-disjoint protocol with 240 adaptation runs, dynamic fusion reduces writer-level variance and cross-seed instability relative to fixed fusion while retaining competitive point-wise accuracy. A security-oriented sensing analysis further contrasts detector responses under generic versus writer-adapted rendering, showing that the framework can serve as a controllable probe of verification sensitivity. The method plugs into existing trajectory generators and supports reproducible evaluation of personalization fidelity and biometric sensing robustness.

Random numbers play a vital role in modern information security, where quantum random number generators (QRNGs) based on single-photon avalanche diodes (SPADs) offer a promising physical platform for quantum random-number generation. However, practical SPAD QRNGs can exhibit temporal correlation arising from finite photon-event statistics and detector nonidealities. To overcome these limitations, this paper presents a high-speed, 256-channel SPAD QRNG chip, featuring a 256 × 512-pixel array integrated with a rolling-sampling time-division multiplexing architecture. By virtue of the rolling-sampling mechanism, the single-pixel sampling interval is extended to 40.96 μs, which substantially reduces the measured temporal correlation by increasing the per-pixel revisit interval while preserving aggregate throughput. Within one complete operating cycle, the chip generates 256 random bits simultaneously, achieving a maximum aggregate raw output bitrate of 3.2 Gb/s without digital post-processing. An FPGA-based test platform was constructed for experimental verification. Measurements demonstrate that under standard indoor illumination (8 W/m²), the generated raw bitstreams pass all 15 NIST SP 800–22 statistical randomness tests at 3.2 Gb/s without post-processing. Furthermore, under controlled 530 nm illumination, the measured lag-1 serial autocorrelation coefficient decreases below the 10⁻³ reference level at an incident optical power of 11.6 μW. The proposed chip offers a low-optical-power, high-throughput solution for integrated hardware security.

Background: Traditional scalp electroencephalography (EEG), though clinically validated, presents significant limitations, including lengthy setup, patient discomfort, and limited mobility. These factors hinder its utility for long-term ambulatory monitoring. In-ear EEG offers a promising alternative by leveraging the ear canal’s anatomical proximity to the temporal lobes and natural mechanical stability. To address this, we developed SeizEAR, a standardized, three-size in-ear EEG system using paste-based electrodes with cross-ear referencing. Methods: This pilot study evaluated SeizEAR devices featuring small, medium, and large earpieces with superior and inferior paste-based electrodes in each ear, configured in a cross-ear reference scheme. It included five healthy adults who underwent recording sessions of approximately three hours (2.4 to 3.3 h). Electrode impedance was monitored against the acquisition system’s threshold indicator, with simultaneous scalp EEG from T7/T8 electrodes serving as the reference. Participants rated comfort on a 0–5 Likert scale every 30 min. Results: In-ear recordings showed moderate, band-dependent correspondence with ipsilateral temporal scalp electrodes, strongest in the delta and alpha bands and weakest in beta, and correspondence was consistently higher against a geometrically matched T7–T8 bipolar derivation than against a single referenced electrode. In-ear alpha reactivity to eye closure was present but attenuated relative to scalp. Comfort ratings remained high over the final two hours of wear, with no reported adverse events. Conclusions: SeizEAR acquired in-ear EEG that corresponded moderately with temporal scalp EEG and was well-tolerated over multi-hour wear in healthy adults. These preliminary findings support further development of a standardized in-ear EEG system and require confirmation in an adequately powered sample.