Abstract-Through-silicon-via (TSV) enables vertical connectivity between stacked chips or interposer and is a key technology for 3-D integrated circuits (ICs). While arrays of TSVs are needed in 3-D IC, there only exists a frequency-dependent resistance, inductance, conductance and capacitance circuit model for a pair of TSVs with coupling between them. In this paper, we develop a simple yet accurate circuit model for a multiport TSV network (e.g., coupled TSV array) by decomposing the network into a number of TSV pairs and then applying circuit models for each of them. We call the new model a pair-based model for the multiport TSV network. It is first verified against a commercial electromagnetic solver for up to 20 GHz and subsequently employed for a variety of examples for signal and power integrity analysis.
Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation. In this paper, we propose a scribble-based weakly supervised road surface extraction method named ScRoadExtractor, which learns from easily accessible scribbles such as centerlines instead of densely annotated road surface ground-truths. To propagate semantic information from sparse scribbles to unlabeled pixels, we introduce a road label propagation algorithm which considers both the buffer-based properties of road networks and the color and spatial information of super-pixels. The proposal masks generated from the road label propagation algorithm are utilized to train a dualbranch encoder-decoder network we designed, which consists of a semantic segmentation branch and an auxiliary boundary detection branch. We perform experiments on three diverse road datasets that are comprised of highresolution remote sensing satellite and aerial images across the world. The results demonstrate that ScRoadExtractor exceed the classic scribble-supervised segmentation method by 20% for the intersection over union (IoU) indicator and outperform the state-of-the-art scribble-based weakly supervised methods at least 4%.
Road surface extraction from high-resolution remote sensing images has many engineering applications; however, extracting regularized and smooth road surface maps that reach the human delineation level is a very challenging task, and substantial and time-consuming manual work is usually unavoidable. In this paper, to solve this problem, we propose a novel regularized road surface extraction framework by introducing a graph neural network (GNN) for processing the road graph that is preconstructed from the easily accessible road centerlines. The proposed framework formulates the road surface extraction problem as two-sided width inference of the road graph, and consists of a convolutional neural network (CNN)-based feature extractor and a GNN model for vertex attribute adjustment. The CNN extracts the high-level abstract features of each vertex in the graph as the input of the GNN, and also the road boundary features that allow us to distinguish roads from the background. The GNN propagates and aggregates the features of the vertices in the graph to achieve global optimization of the regression of the regularized widths of the vertices. At the same time, a biased centerline map can also be corrected based on the width prediction result. To the best of the authors' knowledge, this is the first study to have introduced a GNN to regularized human-level road surface extraction. The proposed method was evaluated on four diverse datasets, and the results show that the proposed method comprehensively outperforms the recent CNN-based segmentation methods and other regularization methods in the intersection over union (IoU) and smoothness score, and a visual check shows that a majority of the prediction results of the proposed method approach the human delineation level.
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