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  1. Current hand wearables have limited customizability, they are loose-fit to an individual's hand and lack comfort. The main barrier in customizing hand wearables is the geometric complexity and size variation in hands. Moreover, there are different functions that the users can be looking for; some may only want to detect hand's motion or orientation; others may be interested in tracking their vital signs. Current wearables usually fit multiple functions and are designed for a universal user with none or limited customization. There are no specialized tools that facilitate the creation of customized hand wearables for varying hand sizes and providemore »different functionalities. We envision an emerging generation of customizable hand wearables that supports hand differences and promotes hand exploration with additional functionality. We introduce FabHandWear, a novel system that allows end-to-end design and fabrication of customized functional self-contained hand wearables. FabHandWear is designed to work with off-the-shelf electronics, with the ability to connect them automatically and generate a printable pattern for fabrication. We validate our system by using illustrative applications, a durability test, and an empirical user evaluation. Overall, FabHandWear offers the freedom to create customized, functional, and manufacturable hand wearables.« less
  2. Recent advances in big spatial data acquisition and deep learning allow novel algorithms that were not possible several years ago. We introduce a novel inverse procedural modeling algorithm for urban areas that addresses the problem of spatial data quality and uncertainty. Our method is fully automatic and produces a 3D approximation of an urban area given satellite imagery and global-scale data, including road network, population, and elevation data. By analyzing the values and the distribution of urban data, e.g., parcels, buildings, population, and elevation, we construct a procedural approximation of a city at a large-scale. Our approach has three mainmore »components: (1) procedural model generation to create parcel and building geometries, (2) parcel area estimation that trains neural networks to provide initial parcel sizes for a segmented satellite image of a city block, and (3) an optional optimization that can use partial knowledge of overall average building footprint area and building counts to improve results. We demonstrate and evaluate our approach on cities around the globe with widely different structures and automatically yield procedural models with up to 91,000 buildings, and spanning up to 150 km 2 . We obtain both a spatial arrangement of parcels and buildings similar to ground truth and a distribution of building sizes similar to ground truth, hence yielding a statistically similar synthetic urban space. We produce procedural models at multiple scales, and with less than 1% error in parcel and building areas in the best case as compared to ground truth and 5.8% error on average for tested cities.« less
  3. The microstructural optimization of porous lithium ion battery electrodes has traditionally been driven by experimental trial and error efforts, based on anecdotal understanding and intuition, leading to the development of useful but qualitative rules of thumb to guide the design of porous energy storage technology. In this paper, an advanced data-driven framework is presented wherein the effect of experimentally accessible microstructural parameters such as active particle morphology and spacial arrangement, underlying porosity, cell thickness, etc. , on the corresponding macroscopic power and energy density is systematically assessed. For the Li x C 6 | LMO chemistry, an analysis performed onmore »53 356 battery architectures reported in the literature revealed that for commercial microstructures based on oblate-shaped particles, lightly textured samples deliver higher power and energy density responses as compared to highly textured samples, which suffer from large polarization losses. In contrast, high aspect ratio prolate-shaped particles deliver the highest energy and power density, particularly in the limit of wire-like morphologies. Polyhedra-based colloidal microstructures demonstrate high area densities, and low tortuosities, but provide no appreciable power and energy density benefit over currently manufactured particle morphologies. The developed framework enables to establish general microstructure design guidelines and propose optimal electrode microstructures based on the intended application, given an anode and cathode chemistry.« less
  4. Procedural modeling has produced amazing results, yet fundamental issues such as controllability and limited user guidance persist. We introduce a novel procedural system called PICO (Procedural Iterative Constrained Optimizer) using PICO-Graph, a procedural model designed with optimization in mind. PICO enables the exploration of generative designs by combining user and environmental constraints into a single framework and using optimization without the need to write procedural rules. The PICO-Graph is a data-flow procedural model consisting of a set of geometry-generating operation nodes. The forward generation is initiated by sending geometric objects from initial nodes. These objects travel through the graph, triggeringmore »generation of more objects along the way. We combine the PICO-Graph with evolutionary optimization that allows for exploration of the generated models and the generation of variants. The user defines the geometry-generating operations and the set of constraints; e.g, whether an existing object should be supported by the generated model, whether symmetries exist, etc. PICO then generates geometric models that fulfill the constraints through optimization, allowing interactive user control of constraints. We show PICO on a variety of examples, including generation of procedural chairs, generation of support structures for 3D printing, or generation of procedural terrains matching a given input.« less