Unknown tag identification plays a pivotal role in radio frequency identification (RFID) systems, but it has not been fully investigated. This paper proposes a filter-based and parallel unknown tag identification protocol (FPUI) for open RFID systems. The FPUI adopts an RSQF-based fingerprint filter to reconcile the collision slots and discriminate the known tags from unknown tags. Meanwhile, it collects the IDs of unknown tags in parallel. FPUI achieves high performance through the following three steps: (1) adopting the RSQF-based filter to build an indicator vector, thus improving the space efficiency; (2) building a fingerprint filter to discriminate known tags from unknown tags, thus reducing the false positive rate; (3) employing a parallel identification scheme to collect the IDs of unknown tags, thus improving identification efficiency. The identification time of our protocol was minimized by conducting a theoretical analysis of the relevant parameters. Furthermore, the performance of our protocol was evaluated by conducting a wide range of simulation experiments. The theoretical analysis and simulation results indicated that our protocol significantly outperformed the current advanced protocols.

Temporal action localization (TAL) requires a detector to recognize action categories and estimate temporal boundaries in untrimmed videos. Mamba supports linear-complexity long-sequence modeling, yet a uniform allocation of state-space operators does not explicitly differentiate the context requirements associated with temporal scales and prediction branches. We present Multi-Scale Decoupled Routing Mamba (MSDR-Mamba), a multi-scale branch-decoupled routing state-space detector. The method combines a phase-dilated multi-rate Mamba temporal pyramid, multi-band state-time initialization, level-wise local–global gating guided by duration priors, and a branch role-decoupled head. The final head uses CNNs for classification and center-offset estimation, with Mamba used for class-specific start/end boundary neighbor modeling. With frozen InternVideo2-6B features on THUMOS14, MSDR-Mamba achieves a five-threshold mAP of 73.09%, exceeding TriDet by 0.43 percentage points. Supplementary experiments on ActivityNet-1.3 and P²ANet further evaluate the complete configuration under longer-duration and dense short action distributions. The results support scale- and branch-aware state-space modeling as a practical design strategy for TAL.