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Recent progress in additive manufacturing (AM) has transformed how complex bioinspired engineering structures are designed and produced. Nonetheless, these structures present substantial difficulties in mathematical modeling and computational analysis. The main goal of this paper is to develop an effective computational method to predict the dynamic response of graphene platelets (GPLs)-reinforced functionally graded triply periodic minimal surface (FG-TPMS) plates (referred to as GPLR-FG-TPMS plates) supported by PF. To achieve this, we employ TrSDT within the IGA framework for efficiency and accuracy. The study establishes advanced plate models by combining three sheet-based TPMS configurations with three porosity distributions and three GPL distribution variations through the plate thickness. Then, we consider the effects of geometrical parameters, material properties, and BCs on free vibration and the dynamic response of GPLR-FG-TPMS plates supported by PF. The findings greatly improve our understanding of the complex dynamic responses of GPLR-FG-TPMS plates, paving the way for applications in various fields in the future.

Lane detection is one of the most fundamental problems in the rapidly developing field of autonomous vehicles. With the dramatic growth of deep learning in recent years, many models have achieved a high accuracy for this task. However, most existing deep-learning methods for lane detection face two main problems. First, most early studies usually follow a segmentation approach, which requires much post-processing to extract the necessary geometric information about the lane lines. Second, many models fail to reach real-time speed due to the high complexity of model architecture. To offer a solution to these problems, this paper proposes a lightweight convolutional neural network that requires only two small arrays for minimum post-processing, instead of segmentation maps for the task of lane detection. This proposed network utilizes a simple lane representation format for its output. The proposed model can achieve 93.53% accuracy on the TuSimple dataset. A hardware accelerator is proposed and implemented on the Virtex-7 VC707 FPGA platform to optimize processing time and power consumption. Several techniques, including data quantization to reduce data width down to 8-bit, exploring various loop-unrolling strategies for different convolution layers, and pipelined computation across layers, are optimized in the proposed hardware accelerator architecture. This implementation can process at 640 FPS while consuming only 10.309 W, equating to a computation throughput of 345.6 GOPS and energy efficiency of 33.52 GOPS/W.