Autonomous Vehicles (AVs) are redefining the transportation sector through their ability to navigate, make decisions, and complete autonomous tasks. For accurate perception and comprehension of the surroundings, the A...
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Autonomous Vehicles (AVs) are redefining the transportation sector through their ability to navigate, make decisions, and complete autonomous tasks. For accurate perception and comprehension of the surroundings, the AVs heavily rely on segmenting high-resolution 3D point cloud data provided by Light Detection and Ranging (LiDAR) sensors for discerning objects and other environmental features. However, the current vehicular segmentation approaches experience shortcomings in data insufficiency, computational performance, and precision concerns. Hence, to counteract these limitations, the paper proposes a Semantic Segmentation approach using Ball-Pivoting algorithm and U-Net (SSBU) that harmoniously combines the Ball-Pivoting surface reconstruction algorithm and 3D U-Net to enhance image characteristics, leading to highly accurate outcomes with optimal cost efficiency. This SSBU integration involves carrying out augmentations and pre-processing of the raw LiDAR data to transform them into voxels through the process of Voxelization. The voxels are further improved through a surface reconstruction technique that utilizes the Ball Pivoting algorithm (bpa). The resulting 3D model is analyzed using 3D U-Net deep learning architecture for robust and real-time interpretation. The implementation has produced a mean Intersection Over Union (IoU) of 83.3 over the NuScenes data and 69.7 on the KITTI dataset, outperforming the state-of-the-art.
The fabrication of a flexible electronic system which consists of a flexible polyimide (PI) substrate and a rigid silicon thin chip is demonstrated. The 50 mu m thick rigid silicon chips are bonded to the PI substrate...
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The fabrication of a flexible electronic system which consists of a flexible polyimide (PI) substrate and a rigid silicon thin chip is demonstrated. The 50 mu m thick rigid silicon chips are bonded to the PI substrate using a die attach film (20 mu m) and electrically interconnected to an adjacent metal pad which is deposited on the PI substrate and silicon chips. A 3D step covered interconnection consisted of silver nanoparticles and polymer are achieved by using an electrohydrodynamic system. The mechanical and electrical characteristic was investigated by performing a Kelvin resistance measurement on the jetted lines while the substrate was subjected to bending modes of varying diameters.
In this paper, we propose a new reduced-complexity decoding algorithm of Low-Density Parity-Check (LDPC) codes, called Belief-Propagation-Approximated (bpa) algorithm, which utilizes the idea of normalization and tran...
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In this paper, we propose a new reduced-complexity decoding algorithm of Low-Density Parity-Check (LDPC) codes, called Belief-Propagation-Approximated (bpa) algorithm, which utilizes the idea of normalization and translates approximately the intricate nonlinear operation in the check nodes of the original BP algorithm to only one operation of looking up the table. The normalization factors can be obtained by simulation, or theoretically. Simulation results demonstrate that bpa algorithm exhibits fairly satisfactory bit error performance on the Additive White Gaussian Noise (AWGN) channel.
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