Generating compact polygonal models from point clouds is a key problem in 3D vision and computer graphics. However, due to inherent limitations of LiDAR scanning (e.g. range constraints and occlusions), critical scene information is often missing, leading to degraded reconstruction accuracy. To address this, we propose a plane assembling strategy that effectively recovers missing details while maintaining model compactness. We classify all the planes extracted from the scene into three categories: highly visible, barely visible, and invisible. The invisible planes, which are recovered by scene structure analysis, indicate the missing details. The three types of planes correspond to the three growth priorities. Each plane grows according to the priority level, and the space is partitioned progressively, that is, the hierarchical partition. Subsequently, we generate a watertight polygonal mesh from the partition via a min-cut-based optimization. Finally, comparisons on public datasets show the effectiveness and superiority of our method against mainstream approaches.

With the growing number of users in multi-user multiple-input multiple-output (MU-MIMO) systems, demodulation reference signals (DMRS) are efficiently multiplexed in the code domain via orthogonal cover codes (OCC) to ensure orthogonality and minimize pilot interference. In this paper, we investigate uplink DMRS-based channel estimation for MU-MIMO systems with Type II OCC pattern standardized in third generation partnership project (3GPP) Release 18, leveraging location-specific statistical channel state information (SCSI) to enhance performance. Specifically, we propose a SCSI-assisted Bayesian channel estimator (SA-BCE) based on the minimum mean square error criterion to suppress the pilot interference and noise, albeit at the cost of cubic computational complexity due to matrix inversions. To reduce this complexity while maintaining performance, we extend the scheme to a windowed version (SA-WBCE), which incorporates antenna-frequency domain windowing and beam-delay domain processing to exploit asymptotic sparsity and mitigate energy leakage in practical systems. To avoid the frequent real-time SCSI acquisition, we construct a grid-based location-specific SCSI database based on the principle of spatial consistency, and subsequently leverage the uplink received signals within each grid to extract the SCSI. Facilitated by the multilinear structure of wireless channels, we formulate the SCSI acquisition problem within each grid as a tensor decomposition problem, where the factor matrices are parameterized by the multi-path powers, delays, and angles. The computational complexity of SCSI acquisition can be significantly reduced by exploiting the Vandermonde structure of the factor matrices. Simulation results demonstrate that the proposed location-specific SCSI database construction method achieves high accuracy, while the SA-BCE and SA-WBCE significantly outperform state-of-the-art benchmarks in MU-MIMO systems.

In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multipleoutput (MIMO) channels without requiring prior knowledge of channel state information (CSI). We start by reviewing the conventional Kalman filtering-based state estimation algorithm, which typically relies on perfect CSI and incurs considerable computational complexity. To overcome the need for CSI, we introduce a novel semantic aggregation method, in which sensors transmit semantic measurement discrepancies to the remote state estimator through analog aggregation. To further reduce computational complexity, we introduce a constant-gain-based filtering algorithm that can be optimized offline using the constrained stochastic successive convex approximation (CSSCA) method. We derive a closed-form sufficient condition for the estimation stability of our proposed scheme via Lyapunov drift analysis. Numerical results showcase significant performance gains using the proposed scheme compared to several widely used methods.

This work explores the data-driven online tracking control problem for linear dynamic systems across multiple-input multiple-output (MIMO) fading channels. Initially, we address the optimal tracking control for a system with known plant dynamics, and design an innovative stochastic-approximation (SA)-based data-driven algorithm that leverage the instantaneous wireless channel state information (CSI). Subsequently, we extend this approach to accommodate unknown plant dynamics by proposing a novel normalized-stochastic-gradient-descent (NSGD)-based algorithm. This algorithm facilitates simultaneous system identification and control in an online setting using the real-time plant state as well as the CSI. Through Lyapunov drift analysis, we establish the asymptotic optimality of our proposed data-driven algorithms. Numerical results and analysis further demonstrate notable performance improvements compared to several leading learning techniques.

Deep neural network (DNN) applications have significantly progressed in voice activity detection (VAD). Most current DNN-based VAD methods ignore the rich audio information in the phase domain. Therefore, applying this auxiliary information rationally and coping with low signal-to-noise ratio (SNR) background noise environments remains one of the challenges for VAD. To address this problem, we propose a VAD model robust to noise called phase continuity-aware self-attentive recurrent network (PC-ARN). For the input of PC-ARN, we draw inspiration from recent speech enhancement research by introducing phase-related features and further employing an adaptive feature selection module (AFSM) to combine magnitude features with it efficiently. The backbone network is an ARN module combining the attention mechanism and recurrent neural network (RNN), which can consider the relationship between local and global information to improve VAD performance competently. Experimental results show that our method has remarkable generalization ability and robustness compared to the traditional VAD techniques.

The swift evolution of Internet of Things (IoT) technologies has facilitated the rapid deployment of heterogeneous devices in industrial systems. In this work, we focus on the identification and temperature tracking control for a furnace system with a single slab and a single heater over a wirelessly connected IoT controller. Specifically, we characterize the IoT-based furnace temperature control system using an integrated-control-and-communication framework that involves thermal modeling of convection and conduction in the furnace plant, as well as wireless modeling of the communication network between the furnace plant and the remote estimator-controller center. Based on the framework, we first propose a novel stochastic-approximation-based online algorithm to learn the optimal temperature tracking control solution for the furnace system with knowledge of the furnace dynamics. After that, we extend the temperature tracking control approach to deal with the furnace system with unknown furnace dynamics and propose a novel normalized-stochastic-gradient-descent (NSGD)-based algorithm to simultaneously identify and control the furnace system at the remote controller in an online manner. Using the Lyapunov stability analysis and ordinary differential equation (ODE) method, we theoretically demonstrate the asymptotic convergence of the proposed learning algorithms. Numerical analysis is conducted for our proposed temperature tracking control scheme and several state-of-the-art schemes. Our results demonstrate that our proposed scheme outperforms the baseline schemes in terms of temperature tracking accuracy and fuel efficiency, in the presence of the wireless interface.

Abstract Talking head generation aims to synthesize a photo‐realistic speaking video with accurate lip motion. While this field has attracted more attention in recent audio‐visual researches, most existing methods do not achieve the simultaneous improvement of lip synchronization and visual quality. In this paper, we propose Wav2Lip‐HR, a neural‐based audio‐driven high‐resolution talking head generation method. With our technique, all required to generate a clear high‐resolution lip sync talking video is an image/video of the target face and an audio clip of any speech. The primary benefit of our method is that it generates clear high‐resolution videos with sufficient facial details, rather than the ones just be large‐sized with less clarity. We first analyze key factors that limit the clarity of generated videos and then put forth several important solutions to address the problem, including data augmentation, model structure improvement and a more effective loss function. Finally, we employ several efficient metrics to evaluate the clarity of images generated by our proposed approach as well as several widely used metrics to evaluate lip‐sync performance. Numerous experiments demonstrate that our method has superior performance on visual quality and lip synchronization when compared to other existing schemes.

With the rapid development of mobile computing, mission-critical Internet of Things (IoT) systems have become popular. Typical mission-critical IoT systems may contain complicated unknown and unstable elements and it is of particular importance to identify and stabilize them as unstable systems may experience catastrophic consequences. We consider the identification and optimal control for a mission-critical IoT system over multiple-input–multiple-output (MIMO) fading channels. First, we focus on the optimal control of the mission-critical IoT system, assuming that the system dynamics are known, and propose a novel stochastic-approximation-based algorithm to learn the optimal control solution for the IoT controller in an online manner. Second, we extend the optimal control framework to deal with the unknown mission-critical IoT system and propose a novel normalized-stochastic-gradient-descent-based algorithm to simultaneously identify and control the system in an online manner. Using the Lyapunov stability analysis, we theoretically show the asymptotic optimality of the proposed learning algorithms. Numerical results are analyzed for our proposed scheme and for several state-of-the-art learning schemes in terms of the computational complexity, convergence, and stability performance. Specifically, the proposed scheme can be implemented more than 50% faster than the state-of-the-art learning schemes. Moreover, the system identification performance of the proposed scheme can achieve a normalized system identification mean square error (MSE) of around 0.01 in 100 iterations. This is a substantial improvement compared to the baseline algorithms, where the normalized system identification MSE diverges.

We consider remote state estimation for an industrial Internet-of-Things (IoT) system, where the plant dynamics are monitored by a number of distributed industrial IoT sensors. We propose an “estimation friendly” remote state estimation framework, which not only maintains low computational complexity but also provides better estimation stability performance. Specifically, we propose a novel over-the-air-aggregation-based multiple access, which enhances the observability performance of the state estimation system and hence, provides better estimation stability. Additionally, exploiting the sparsity in the observation matrix induced by the over-the-air-aggregation-based multiple access, we propose a low-complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the quasi-diagonal transformation of the aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact maximum a posterior estimation. Using the Lyapunov drift analysis, we derive the closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. The numerical results demonstrate that the proposed scheme has a low computational complexity. Furthermore, it is scalable with the number of sensors and has a low power consumption.

In this paper, we consider a mission-critical remote state estimation system with asynchronous massive access of the IoT sensors. We focus on remote state estimation stability of the system in the presence of asynchronous access of the sensors. Exploiting the sparsity in the observation matrix induced by the asynchronous access, we propose a low complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the Gaussian-elimination-based quasi-diagonalization of the oversampled aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact MAP estimation. Using Lyapunov drift analysis, we derive closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. We show that our proposed scheme can achieve significant performance gain over various state-of-the-art baselines for the large-scale system under asynchronous massive access.