Interfacing anatomically conformal electronic components, such as sensors, with biology is central to the creation of next-generation wearable systems for health care and human augmentation applications. Thus, there is a need to establish computer-aided design and manufacturing methods for producing personalized anatomically conformal systems, such as wearable devices and human-machine interfaces (HMIs). Here, we show that a three-dimensional (3D) scanning and 3D printing process enabled the design and fabrication of a sensor-integrated anatomical human-machine interface (AHMI) in the form of personalized prosthetic hands that contain anatomically conformal electrode arrays for children affected by amniotic band syndrome, a common birth defect. A methodology for identifying optimal scanning parameters was identified based on local and global metrics of registered point cloud data quality. This method identified an optimal rotational angle step size between adjacent 3D scans. The sensitivity of the optimization process to variations in organic shape ( i . e ., geometry) was examined by testing other anatomical structures, including a foot, an ear, and a porcine kidney. We found that personalization of the prosthetic interface increased the tissue-prosthesis contact area by 408% relative to the non-personalized devices. Conformal 3D printing of carbon nanotube-based polymer inks across the personalized AHMI facilitated the integration of electronic components, specifically, conformal sensor arrays for measuring the pressure distribution across the AHMI ( i . e ., the tissue-prosthesis interface). We found that the pressure across the AHMI exhibited a non-uniform distribution and became redistributed upon activation of the prosthetic hand’s grasping action. Overall, this work shows that the integration of 3D scanning and 3D printing processes offers the ability to design and fabricate wearable systems that contain sensor-integrated AHMIs.

Current FPGA placement algorithms estimate the routability of a placement using architecture-specific metrics. The shortcoming of using architecture-specific routability estimates is limited adaptability. A placement algorithm that is targeted to a class of architecturally similar FPGAs may not be easily adapted to other architectures. The subject of this paper is the development of a routability-driven architecture adaptive FPGA placement algorithm called Independence. The core of the Independence algorithm is a simultaneous place-and-route approach that tightly couples a simulated annealing placement algorithm with an architecture adaptive FPGA router (Pathfinder). The results of our experiments demonstrate Independence's adaptability to island-style FPGAs, a hierarchical FPGA architecture (HSRA), and a coarse-grained reconfigurable architecture (RaPiD). The quality of the placements produced by Independence is within 1.2% of the quality of VPR's placements, 17% better than the placements produced by HSRA's placer, and within 0.7% of RaPiD's placer. Further, our results show that Independence produces clearly superior placements on routing-poor island-style FPGA architectures.

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Reconfigurable hardware is ideal for use in Systems-on-a-Chip, as it provides hardware speeds as well as the benefits of post-fabrication modification. However, not all applications are equally suited to any one reconfigurable architecture. Therefore, the Totem Project focuses on the automatic generation of customized reconfigurable hardware. This paper details our first attempts at the design of algorithms for automatic generation of customized flexible routing architectures. We show that these algorithms provide results with a low area overhead compared to the custom-designed RaPiD routing architecture, as well as the flexibility needed to handle some application modifications.