In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.

No abstract

Chip floorplanning has long been a critical task with high computation complexity in the physical implementation of VLSI chips. Its key objective is to determine the initial locations of large chip modules with minimized wirelength while adhering to the density constraint, which in essence is a process of constructing an optimized mapping from circuit connectivity to physical locations. Proven to be an NP-hard problem, chip floorplanning is difficult to be solved efficiently using algorithmic approaches. This paper presents GraphPlanner, a variational graph convolutional network-based deep learning technique for chip floorplanning. GraphPlanner is able to learn an optimized and generalized mapping between circuit connectivity and physical wirelength, and produce a chip floorplan using efficient model inference. GraphPlanner is further equipped with an efficient clustering method, a unification of hyperedge coarsening with graph spectral clustering, to partition large-scale netlist into high-quality clusters with minimized inter-cluster weighted connectivity. GraphPlanner has been integrated with two state-of-the-art mixed-size placers. Experimental studies using both academic benchmarks and industrial designs demonstrate that compared to state-of-the-art mixed-size placers alone, GraphPlanner improves placement runtime by 25% with 4% wirelength reduction on average.