G-ID: identifying 3D Prints using slicing parameters

We present G-ID, a method that utilizes the subtle patterns left by the 3D printing process to distinguish and identify objects that otherwise look similar to the human eye. The key idea is to mark different instances of a 3D model by varying slicing parameters that do not change the model geometry but can be detected as machine-readable differences in the print. As a result, G-ID does not add anything to the object but exploits the patterns appearing as a byproduct of slicing, an essential step of the 3D printing pipeline. We introduce the G-ID slicing & labeling interface that varies the settings for each instance, and the G-ID mobile app, which uses image processing techniques to retrieve the parameters and their associated labels from a photo of the 3D printed object. Finally, we evaluate our method’s accuracy under different lighting conditions, when objects were printed with different filaments and printers, and with pictures taken from various positions and angles

Postpartum-onset and childhood sexual trauma in a patient with skin picking disorder: a case report

Women are at risk for the development of psychiatric disorders, particularly depression and psychosis in the postpartum period. Few studies have examined anxiety disorders or obsessive-compulsive and related disorders during pregnancy and the postpartum period. The individuals with skin-picking disorder (SPD) frequently have childhood history of sexual abuse. To best of our knowledge, there is no report in literature on postpartum-onset SPD to date. We here report a case of SPD initially presented a postpartum onset and exacerbated following a sexual trauma long years after her delivery. She had no previous history of trauma or psychiatric diagnoses. Pregnancy and sexual trauma seem to be associated with occurrence and relapsing of SPD in this case

SensiCut: Material-Aware Laser Cutting Using Speckle Sensing and Deep Learning

Laser cutter users face difficulties distinguishing between visually similar materials. This can lead to problems, such as using the wrong power/speed settings or accidentally cutting hazardous materials. To support users, we present SensiCut, an integrated material sensing platform for laser cutters. SensiCut enables material awareness beyond what users are able to see and reliably differentiates among similar-looking types. It achieves this by detecting materials' surface structures using speckle sensing and deep learning. SensiCut consists of a compact hardware add-on for laser cutters and a user interface that integrates material sensing into the laser cutting workflow. In addition to improving the traditional workflow and its safety1, SensiCut enables new applications, such as automatically partitioning designs when engraving on multi-material objects or adjusting their geometry based on the kerf of the identified material. We evaluate SensiCut's accuracy for different types of materials under different sheet orientations and illumination conditions

Psychogenic blepharospasm associated with Meige’s syndrome: a case report

Here we report a patient who presented a co-occurrence of Meige’s syndrome and psychogenic blepharospasm. At the first assessments, neurologists excluded conversion disorder because of the presence of a conflict and stress, absence of any markers for Meige’s syndrome, and a non-response to Botulinum toxin treatment. We determined bilateral blepharospasm, and oromandibular dystonia by neurological examination and EMG. The patient was diagnosed as primary Meige’s syndrome by the neurologists. Blepharospasm, which is triggered by emotional stress, caused secondary gains against her family. We decided that the patient had both psychogenic blepharospasm and Meige’s syndrome, which co-occurred nearly at the same interval three years ago. Similar to the seizure–pseudoseizure association, we supposed that Meige’s syndrome and concomitant psychogenic blepharospasm may indicate a coexistence of medical and conversion symptoms as in epileptic patients

FolDTronics Demo: Creating 3D objects with integrated electronics using foldable honeycomb structures

We present FoldTronics, a 2D-cutting based fabrication technique to integrate electronics into 3D folded objects. The key idea is to cut and perforate a 2D sheet to make it foldable into a honeycomb structure using a cutting plotter; before folding the sheet into a 3D structure, users place the electronic components and circuitry onto the sheet. The fabrication process only takes a few minutes enabling users to rapidly prototype functional interactive devices. The resulting objects are lightweight and rigid, thus allowing for weight-sensitive and force-sensitive applications. Finally, due to the nature of the honeycomb structure, the objects can be folded flat along one axis and thus can be efficiently transported in this compact form factor. We describe the structure of the foldable sheet, and present a design tool that enables users to quickly prototype the desired objects. We showcase a range of examples made with our design tool, including objects with integrated sensors and display elements.Japan Science and Technology Corporation. Exploratory Research for Advanced Technology research funding program (Grant no. JPMJER1501

InfraredTags: Embedding Invisible AR Markers and Barcodes Using Low-Cost, Infrared-Based 3D Printing and Imaging Tools

Existing approaches for embedding unobtrusive tags inside 3D objects require either complex fabrication or high-cost imaging equipment. We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed as part of objects, and detected rapidly by low-cost near-infrared cameras. We achieve this by printing objects from an infrared-transmitting filament, which infrared cameras can see through, and by having air gaps inside for the tag's bits, which appear at a different intensity in the infrared image. We built a user interface that facilitates the integration of common tags (QR codes, ArUco markers) with the object geometry to make them 3D printable as InfraredTags. We also developed a low-cost infrared imaging module that augments existing mobile devices and decodes tags using our image processing pipeline. Our evaluation shows that the tags can be detected with little near-infrared illumination (0.2lux) and from distances as far as 250cm. We demonstrate how our method enables various applications, such as object tracking and embedding metadata for augmented reality and tangible interactions.Comment: 12 pages, 10 figures. To appear in the Proceedings of the 2022 ACM Conference on Human Factors in Computing System