Jiha Kim 김지하

IEEE 802.11bn Ultra High Reliability (UHR) extends multi-link operation toward a reliability-oriented MAC, but multi-link contention under the EMLSR active-link constraint resists closed-form analysis. We develop a Markov/fixed-point closure for three-band MLDs coexisting with single-link legacy stations: exact under saturated, per-link-independent STR; reducing to an availability-based approximation under saturated EMLSR with an explicit validity range; and extending to unsaturated traffic through per-link Bernoulli arrivals. On top of it we build a controller that adapts the per-link minimum contention window from MAC observables inside the standard EDCA/EMLSR loop. Against SLD, STR-MLD and static-EMLSR baselines it raises throughput by 10.6%, lowers 95th-percentile HOL-service delay by 28%, and improves Jain's fairness from 0.910 to 0.954 at 48 stations. Triangulation across the analytical model, a discrete-event simulator and ns-3 closes within a 2–4% mean relative gap.

IEEE 802.11bn defines ultra-high reliability through per-BSS deadline-violation-rate targets. We study these constraints in dense multi-band deployments with heterogeneous AP link sets. LyMAPPO combines independent per-AP PPO with drop-inclusive rate-form Lyapunov duals, one dual-derived congestion price per beacon for cross-BSS coordination, and an auxiliary lower-tail quantile critic. Training and evaluation use an ns-3-in-the-loop environment with 16 APs, three bands, shared-buffer multi-link operation and Markov channels. Across 16 seeds with 110 s episodes, LyMAPPO achieves 15.1/16 feasible BSSs and a network p99 of 0.0017 — 6.1× to 25× lower than three static baselines. The drift-plus-penalty analysis accounts for clipping and establishes a run-time dual-stability condition under the Slater assumption.

STCA-ViT is a hybrid framework integrating a VideoMAE ViT-Base backbone with bidirectional spatio-temporal cross-attention, Sobel-based boundary detection for motion gating, and YOLO-based person attention in parallel. It adds residual contribution fusion with gradient reversal for feature diversity, orthogonal regularization, a multi-head ensemble classifier, and an optional weakly-supervised temporal action localization head that recycles motion features into frame-level scores without temporal annotations. STCA-ViT reaches 90.46% Top-1 on HMDB-51 — above VideoMAE V2 ViT-g (86.08%) — with 98.29% on UCF-101, 79.44% on Kinetics-400 and 65.01% on Something-Something-v2, using 110M parameters and 305 GFLOPs.

WiFi channel state information supports device-free 3D pose estimation, but posture and motion remain hard to recover across subjects and scenes. On MM-Fi, recent estimators' Procrustes-aligned errors (99.8–106.2 mm) sit close to a mean-posture reference (99.4–103.6 mm), indicating much of the reported gain comes from localization. PhysRoute-Fi separates CSI into physical components and assigns them to pose factors: sanitized per-antenna phase is fused with an amplitude-led composite representation for location, while the composite representation drives a graph-convolutional posture decoder. With one-time enrollment, a motion-consistency loss recovers about 93% of the aggregate inter-frame displacement magnitude and reaches 102.7 mm MPJPE and 65.5 mm PA-MPJPE.

Weakly-supervised video anomaly detection localizes frame-level anomalies using only video-level binary labels. Existing methods treat anomaly detection and risk anticipation as independent tasks, discarding cross-task predictive cues. SEAR jointly performs multi-horizon risk anticipation and MIL-based detection through a shared representation via three contributions: a Cross-Task Anticipation Gate, a zero-initialized scalar that modulates detection features with anticipation risk scores; Temporal Contrastive MIL, a hinge-loss objective separating peak anomaly scores from all normal scores; and Curriculum Pseudo-Labeling with Negative Mining. On UCF-Crime, SEAR reaches 85.36% frame-level AUC with frozen dual encoders (~0.59M trainable parameters), and 87.78% AUC under triple-encoder fusion — the highest among frozen-encoder methods. It detects 89% of anomalous videos before annotated event onset.