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This paper addresses the critical challenge of supporting next-generation ultra-reliable and low-latency communication (xURLLC) within integrated sensing and communication (ISAC)-enabled vehicle-to-infrastructure (V2I) networks. We incorporate channel evaluation and retransmission mechanisms for real-time reliability enhancement. Using stochastic network calculus (SNC), we establish a theoretical framework to derive upper bounds for the peak age of information violation probability (PAVP) via characterized sensing and communication moment generation functions (MGFs). By optimizing these bounds, we develop power allocation schemes that significantly reduce the statistical PAVP of sensory packets in such networks. Simulations validate our theoretical derivations and demonstrate the effectiveness of our proposed schemes.

Existing 3D human pose estimators face challenges in adapting to new datasets due to the lack of 2D-3D pose pairs in training sets. To overcome this issue, we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis \textbf{D}omain \textbf{A}daptation} (\textbf{PoSynDA}) framework to bridge this data disparity gap in target domain. Typically, PoSynDA uses a diffusion-inspired structure to simulate 3D pose distribution in the target domain. By incorporating a multi-hypothesis network, PoSynDA generates diverse pose hypotheses and aligns them with the target domain. To do this, it first utilizes target-specific source augmentation to obtain the target domain distribution data from the source domain by decoupling the scale and position parameters. The process is then further refined through the teacher-student paradigm and low-rank adaptation. With extensive comparison of benchmarks such as Human3.6M and MPI-INF-3DHP, PoSynDA demonstrates competitive performance, even comparable to the target-trained MixSTE model\cite{zhang2022mixste}. This work paves the way for the practical application of 3D human pose estimation in unseen domains. The code is available at https://github.com/hbing-l/PoSynDA., Comment: Accepted to ACM Multimedia 2023; 10 pages, 4 figures, 8 tables; the code is at https://github.com/hbing-l/PoSynDA

Accurate facial landmark detection is critical for facial analysis tasks, yet prevailing heatmap and coordinate regression methods grapple with prohibitive computational costs and quantization errors. Through comprehensive theoretical analysis and experimentation, we identify and elucidate the limitations of existing techniques. To overcome these challenges, we pioneer the application of True-Range Multilateration, originally devised for GPS localization, to facial landmark detection. We propose KeyPoint Positioning System (KeyPosS) - the first framework to deduce exact landmark coordinates by triangulating distances between points of interest and anchor points predicted by a fully convolutional network. A key advantage of KeyPosS is its plug-and-play nature, enabling flexible integration into diverse decoding pipelines. Extensive experiments on four datasets demonstrate state-of-the-art performance, with KeyPosS outperforming existing methods in low-resolution settings despite minimal computational overhead. By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection. The code is available at https://github.com/zhiqic/KeyPosS., Comment: Accepted to ACM Multimedia 2023; 10 pages, 7 figures, 6 tables; the code is at https://github.com/zhiqic/KeyPosS

Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as it cannot account for changes in object appearance between frames. To this end, we revamped the tracking framework with Progressive Context Encoding Transformer Tracker (ProContEXT), which coherently exploits spatial and temporal contexts to predict object motion trajectories. Specifically, ProContEXT leverages a context-aware self-attention module to encode the spatial and temporal context, refining and updating the multi-scale static and dynamic templates to progressively perform accurately tracking. It explores the complementary between spatial and temporal context, raising a new pathway to multi-context modeling for transformer-based trackers. In addition, ProContEXT revised the token pruning technique to reduce computational complexity. Extensive experiments on popular benchmark datasets such as GOT-10k and TrackingNet demonstrate that the proposed ProContEXT achieves state-of-the-art performance., Comment: Accepted at ICASSP 2023, source code is at https://github.com/zhiqic/ProContEXT

Molecular communication underpins nano-scale communications in nanotechnology. The combination of multinanomachines to form nano-networks is one of the main enabling methods. Due to the importance of source localization in establishing nano-networks, this paper proposes a cooperative source localization method for Molecular Communication via Diffusion (MCvD) systems using multiple spherical absorption receivers. Since there is no exact mathematical expression of the channel impulse response for multiple absorbing receivers, we adopt an empirical expression and use Levenberg-Marquardt method to estimate the distance of the transmitter to each receiver, based on which the location of the transmitter is obtained using an iterative scheme where the initial point is obtained using a multi-point localization method. Particle based simulation is carried out to evaluate the performance of the proposed method. Simulation results show that the proposed method can accurately estimate the location of transmitter in short to medium communication ranges.

Vehicular communication is an important application of the fifth generation of mobile communication systems (5G). Due to its low cost and energy efficiency, intelligent reflecting surface (IRS) has been envisioned as a promising technique that can enhance the coverage performance significantly by passive beamforming. In this paper, we analyze the outage probability performance in IRS-assisted vehicular communication networks. We derive the expression of outage probability by utilizing series expansion and central limit theorem. Numerical results show that the IRS can significantly reduce the outage probability for vehicles in its vicinity. The outage probability is closely related to the vehicle density and the number of IRS elements, and better performance is achieved with more reflecting elements.