Mobile Crowdsensing (MCS) has recently emerged as an effective paradigm for large-scale traffic state estimation and prediction in Intelligent Transportation System (ITS). By integrating data from vehicular sensors and mobile devices, MCS enables real-time and fine-grained traffic monitoring. However, MCS data are often sparse, incomplete, and noisy due to limited participant coverage and unstable sensing environments, which degrades the accuracy and robustness of traffic flow prediction. To address these challenges, this paper proposes a Neighborhood Perception Enhanced Multi-Factor Causal Gated Network (NP-MCGN) for traffic flow prediction based on sparse MCS data. The model incorporates a Neighborhood Perception (NP) module that integrates graph convolution with neighbor-weighted estimation to repair missing data, and a Multi-Factor Spatial Encoding (MFSE) module that fuses distance, correlation, and dynamic structure graphs via graph attention and Mamba state space modeling to capture multi-source spatial dependencies. Furthermore, a Causal Temporal Encoding (CTE) module couples causal convolution with the Mamba architecture to model both short-term fluctuations and long-term temporal evolution, while a Gated Recurrent Decoding (GRD) module dynamically fuses spatial–temporal representations for multi-step forecasting. Extensive experiments on seven real-world MCS-based datasets, including PEMS03, PEMS04, PEMS07, PEMS08, SZ-TAXI, METR-LA, and PEMS-BAY, demonstrate that NP-MCGN consistently outperforms state-of-the-art models under both complete and missing data conditions, verifying its robustness and applicability in realistic MCS environments.

The advancement of technology nodes has intensified the focus on mixed-cell-height circuit design, posing challenges to traditional legalization techniques. In this paper, we propose a novel and efficient accelerated Newton-based matrix splitting (ANMS) iteration method to address the mixed-cell-height circuit legalization problem. Our approach reformulates this problem into a generalized absolute value equation and leverages matrix splitting and the latest estimate vector to enhance computational efficiency. We also introduce a relaxation variant within the ANMS framework, namely, the accelerated Newton-based successive overrelaxation (ANSOR) method, which is particularly effective in scenarios requiring high computational performance and precise parameter tuning. The proposed method achieves linear computational complexity. Furthermore, we perform an in-depth analysis of the sufficient convergence conditions for the ANMS method and optimize cells that have excessive displacement. Experimental results show that the proposed ANMS method achieves a speedup of 1.09× – 4.94× compared to state-of-the-art methods, while maintaining the quality of solution. This makes it highly suitable for addressing complex placement design challenges.

Traffic flow prediction is essential for Internet of Vehicles (IoV) systems as it provides accurate historical and real-time data for both past and future scenarios, enhancing transportation efficiency, reducing congestion, and improving road safety. However, real-world traffic flow prediction remains challenging due to the uncertainty of noise data. Most current graph convolution-based methods depend on heavily preprocessed, smoothed data, which can obliterate critical features, thus hampering accurate predictions in noisy settings. To address this challenge, a novel denoising attention enhanced recurrent multigraph convolutional network (DA-RMN) is proposed for traffic flow prediction. DA-RMN mainly consists of the denoising diffusion gated attention fusion (DDGAF) module and the recurrent multigraph convolution (RMGC) module. The DDGAF module, which includes the denoising diffusion probabilistic model (DDPM) and gated attention fusion (GAF) modules, is engineered to mitigate noise in traffic flow data and enhance the extraction of essential trend features. The DDPM module is tasked with restoring data to a noise-free state, effectively filtering out distortions and inaccuracies. Furthermore, the GAF module focuses on amplifying the detection of essential trend features, ensuring that subtle but critical patterns are not overlooked. The RMGC module consists of a multigraph convolution network based on the gated recurrent unit, residual connection, and fully connected layer to enhance the multigraph spatial-temporal feature of the DDGAF module. Extensive experiments on five real-world datasets demonstrate that DA-RMN outperforms the state-of-the-art model.

The evolution of advanced technology nodes has prompted a shift toward mixed-cell-height circuit design, while the introduction of technology and fence region constraints further increases the complexity of placement. In this article, we innovatively transform the mixed-cell-height circuit legalization problem into a generalized absolute value equation (GAVE) and propose a novel and effective robust Newton (RN) iteration method to address the challenge of the legalization problem. 1 First, the window-based cell insertion technique is applied to obtain the initial cell row allocation and cell order, and the cells are allocated to the matching region based on the R-tree structure. Then, the legalization problem of cells within the region is transformed into a GAVE, and an RN iteration method is proposed to solve the GAVE. Finally, the maximum displacement cells and technology violation cells are optimized based on a greedy method. Experimental results confirm the efficiency and robustness of the proposed method compared with the state-of-the-art methods.