To meet pose-control and vibration-suppression requirements in confined spaces, a compact, noncontact six-degree-of-freedom (6-DoF) displacement-sensing method is proposed. The method is based on laser reflection and a position-sensitive detector (PSD) and features an adjustable incidence angle. An adjustable-incidence-angle PSD–corner-cube retro-reflector (CCR) configuration is devised, which reduces the PSD’s spatial footprint to 10.4% of that of a conventional layout. Building on this configuration, an analytical model is derived that maps the target’s 6-DoF displacement to the PSD spot motion as a function of the fixed relative pose between the PSD and the CCR mounted on the target. The model is linearized under the small-angle assumption. Experiments show an accuracy of 5.89 μm for translation within ±1.5 mm and 0.0027° for rotation within ±0.5°. The method couples a compact architecture with high precision and provides both a theoretical basis and an engineering-ready pathway for high-bandwidth pose sensing in confined spaces.

An integrated QR-based identification and multi-participant quiz system is developed for classroom and competition scenarios. It reduces the check-in latency, removes fixed buzz-in timing, and lifts hardware-imposed limits on the participant count. On the software side, a MATLAB-R2022b-based module integrates the generation and recognition of linear barcodes and QR Codes, enabling fast, accurate acquisition of contestant information while reducing the latency and error risk of manual entry. On the hardware side, control circuits for compulsory and buzz-in modules are designed and simulated in Multisim-14.3. To accommodate diverse scenarios, the team-versus-team buzz-in mode is extended to support two- or three-member teams. Functional tests demonstrate the stable display of key states—including contestant identity, buzz-in priority group ID, and response duration. Compared with typical MCU-channel-based designs, the proposed system relaxes hardware-channel constraints, decoupling the participant count from fixed input channels. It also overcomes fixed-timing limitations by supporting scenario-dependent configuration. The Participant Information Registration subsystem achieved a mean accuracy of 86.7% and a mean per-sample computation time of 14 ms. The 0–99 s configurable timing aligns with question difficulty and instructional procedures. It enhances fairness, adaptability, and usability in formative assessments and competition-based learning.