Lighty: A Painting Interface for Room Illumination by Robotic Light Array

ABSTRACT We propose an AR-based painting interface that enables users to design an illumination distribution for a real room using an array of computer-controlled lights. Users specify an illumination distribution of the room by painting on the image obtained by a camera mounted in the room. The painting result is overlaid on the camera image as contour lines of the target illumination intensity. The system runs an optimization interactively to calculate light parameters to deliver the requested illumination condition. In this implementation, we used actuated lights that can change the lighting direction to generate the requested illumination condition more accurately and efciently than static lights. We built a miniature-scale experimental environment and ran a user study to compare our method with a standard direct manipulation method using widgets. The results showed that the users preferred our method for informal light control. We propose an augmented reality (AR) user interface called Lighty that enables users to easily design an illumination distribution for a real room using an array of computer-controlled lights. Users specify which area of the room is to be well-lit and which is to be dark by painting an illumination distribution on a tablet device displaying an image obtained by a camera mounted in the room. The system runs an optimization to calculate the light parameters and then illuminates the room. Our method is inspired by the goal-based lighting optimization approach in computer graphics SYSTEM OVERVIEW Our overall system is shown in USER INTERFACE The user interface is shown i

3D Finger CAPE:clicking action and position estimation under self-occlusions in egocentric viewpoint

CAPE could be applied to the selection process in an arm reachable AR/VR space. Abstract — In this paper we present a novel framework for simultaneous detection of click action and estimation of occluded fingertip positions from egocentric viewed single-depth image sequences. For the detection and estimation, a novel probabilistic inference based on knowledge priors of clicking motion and clicked position is presented. Based on the detection and estimation results, we were able to achieve a fine resolution level of a bare hand-based interaction with virtual objects in egocentric viewpoint. Our contributions include: (i) a rotation and translation invariant finger clicking action and position estimation using the combination of 2D image-based fingertip detection with 3D hand posture estimation in egocentric viewpoint. (ii) a novel spatio-temporal random forest, which performs the detection and estimation efficiently in a single framework. We also present (iii) a selection process utilizing the proposed clicking action detection and position estimation in an arm reachable AR/VR space, which does not require any additional device. Experimental results show that the proposed method delivers promising performance under frequent self-occlusions in the process of selecting objects in AR/VR space whilst wearing an egocentric-depth camera-attached HMD

A Nanophase-Separated, Quasi-Solid-State Polymeric Single-Ion Conductor: Polysulfide Exclusion for Lithium–Sulfur Batteries

Formation of soluble polysulfide (PS), which is a key feature of lithium sulfur (Li–S) batteries, provides a fast redox kinetic based on a liquid–solid mechanism; however, it imposes the critical problem of PS shuttle. Here, we address the dilemma by exploiting a solvent-swollen polymeric single-ion conductor (SPSIC) as the electrolyte medium of the Li–S battery. The SPSIC consisting of a polymeric single-ion conductor and lithium salt-free organic solvents provides Li ion hopping by forming a nanoscale conducting channel and suppresses PS shuttle according to the Donnan exclusion principle when being employed for Li–S batteries. The organic solvents at the interface of the sulfur/carbon composite and SPSIC eliminate the poor interfacial contact and function as a soluble PS reservoir for maintaining the liquid–solid mechanism. Furthermore, the quasi-solid-state SPSIC allows the fabrication of a bipolar-type stack, which promises the realization of a high-voltage and energy-dense Li–S battery

Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs

This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program’s performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20–85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5–2.4 mm), followed by lumbosacral landmarks (median error 2.1–3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4–4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements