Shallow2Deep: Indoor scene modeling by single image understanding

Dense indoor scene modeling from 2D images has been bottlenecked due to the absence of depth information and cluttered occlusions. We present an automatic indoor scene modeling approach using deep features from neural networks. Given a single RGB image, our method simultaneously recovers semantic contents, 3D geometry and object relationship by reasoning indoor environment context. Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling. It involves multi-level convolutional networks to parse indoor semantics/geometry into non-relational and relational knowledge. Non-relational knowledge extracted from shallow-end networks (e.g. room layout, object geometry) is fed forward into deeper levels to parse relational semantics (e.g. support relationship). A Relation Network is proposed to infer the support relationship between objects. All the structured semantics and geometry above are assembled to guide a global optimization for 3D scene modeling. Qualitative and quantitative analysis demonstrates the feasibility of our method in understanding and modeling semantics-enriched indoor scenes by evaluating the performance of reconstruction accuracy, computation performance and scene complexity

Biomechanical responses of the cornea after small incision lenticule extraction (SMILE) refractive surgery based on a finite element model of the human eye

Purpose: To investigate the biomechanical responses of the human cornea after small incision lenticule extraction (SMILE) procedures, especially their effects of SMILE surgery on stress and strain. Methods: Based on finite element analysis, a three-dimensional (3D) model of the human eye was established to simulate SMILE refractive surgery procedures. Stress and strain values were calculated by inputting the intraocular pressure (IOP). Results: After SMILE refractive surgery procedures, the stress and strain of the anterior and posterior corneal surfaces were significantly increased. The equivalent stress and strain on the anterior and posterior corneal surfaces increased with increasing diopter and were concentrated in the central area, whereas the values of stress and strain at the incision site on the anterior surface of the cornea were approximately 0. Compared with the anterior corneal surface, the stress and strain of the posterior surface were larger. Increasing IOP caused an approximately linear change in stress and a nonlinear increase in corneal strain. In addition, we found that the incision sizes and direction had less of an influence on stress and strain. In summary, SMILE surgery increased the equivalent stress and strain on the human cornea. Conclusions: The equivalent stress and strain of the anterior and posterior human corneal surfaces increased after SMILE refractive surgery; these increases were particularly noticeable on the posterior surface of the cornea

High-sensitivity and throughput optical fiber SERS probes based on laser-induced fractional reaction method

Surface-enhanced Raman scattering (SERS) is widely used in many fields, such as biosensors, medical diagnostics, materials science, and food security. Here, we report a low-cost, high-throughput laser-induced fractional reaction method for optical fiber SERS probes. Under laser irradiation, the local thermal effect and the electromagnetic interaction between nanoparticles effectively contribute to the formation and growth of silver nanoparticles on the optical fiber facet. Sodium dodecyl sulfate (SDS) solution with a concentration of 2 mM is employed as a surfactant to control the shape and size of the silver nanoparticles. A detection limit of 1.0 × 10−11 M for R6G is achieved, which is, as far as we know, the highest sensitivity that laser-induced fabricated optical fiber SERS probes have achieved. The SERS enhancement factors (EFs) are calculated to be 6.795 × 1011. The SERS intensity of R6G at peaks of 621 cm−1, 1281 cm−1, and 1359 cm−1 are measured with probes fabricated under the same condition, and showed perfect repeatability with an RSD of less than 4.5%. This new method shows effectively in fabricating optical fiber SERS probes with high sensitivity and good repeatability

Full-frame and high-contrast smart windows from halide-exchanged perovskites

Window glazing plays a crucial role in modulating indoor light and heat transmission, which is beneficial for energy saving. Here, Liu et al. report a full-frame and high-contrast smart windows made of perovskite photovoltaic and ion-gel electrochromic components to realise self-adjusting brightness and temperature regulator

Hole-Transporting Low-Dimensional Perovskite for Enhancing Photovoltaic Performance

Halide perovskites with low-dimensionalities (2D or quasi-2D) have demonstrated outstanding stabilities compared to their 3D counterparts. Nevertheless, poor charge-transporting abilities of organic components in 2D perovskites lead to relatively low power conversion efficiency (PCE) and thus limit their applications in photovoltaics. Here, we report a novel hole-transporting low-dimensional (HT2D) perovskite, which can form a hole-transporting channel on the top surface of 3D perovskite due to self-assembly effects of metal halide frameworks. This HT2D perovskite can significantly reduce interface trap densities and enhance hole-extracting abilities of a heterojunction region between the 3D perovskite and hole-transporting layer. Furthermore, the posttreatment by HT2D can also reduce the crystal defects of perovskite and improve film morphology. As a result, perovskite solar cells (PSCs) can effectively suppress nonradiative recombination, leading to an increasement on photovoltage to >1.20 V and thus achieving >20% power conversion efficiency and >500 h continuous illumination stability. This work provides a pathway to overcome charge-transporting limitations in low-dimensional perovskites and delivers significant enhancements on performance of PSCs

One-Dimensional (NH=CINH3)3PbI5 Perovskite for Ultralow Power Consumption Resistive Memory

Organic-inorganic hybrid perovskites (OIHPs) have proven to be promising active layers for nonvolatile memories because of their rich abundance in earth, mobile ions, and adjustable dimensions. However, there is a lack of investigation on controllable fabrication and storage properties of one-dimensional (1D) OIHPs. Here, the growth of 1D (NH=CINH3)3PbI5 ((IFA)3PbI5) perovskite and related resistive memory properties are reported. The solution-processed 1D (IFA)3PbI5 crystals are of well-defined monoclinic crystal phase and needle-like shape with the length of about 6 mm. They exhibit a wide bandgap of 3 eV and a high decomposition temperature of 206°C. Moreover, the (IFA)3PbI5 films with good uniformity and crystallization were obtained using a dual solvent of N,N-dimethylformamide (DMF) and dimethyl sulfoxide (DMSO). To study the intrinsic electric properties of this anisotropic material, we constructed the simplest memory cell composed of only Au/(IFA)3PbI5/ITO, contributing to a high-compacted device with a crossbar array device configuration. The resistive random access memory (ReRAM) devices exhibit bipolar current-voltage (I-V) hysteresis characteristics, showing a record-low power consumption of ~0.2 mW among all OIHP-based memristors. Moreover, our devices own the lowest power consumption and “set” voltage (0.2 V) among the simplest perovskite-based memory devices (inorganic ones are also included), which are no need to require double metal electrodes or any additional insulating layer. They also demonstrate repeatable resistance switching behaviour and excellent retention time. We envision that 1D OIHPs can enrich the low-dimensional hybrid perovskite library and bring new functions to low-power information devices in the fields of memory and other electronics applications

An ultrasmall organic synapse for neuromorphic computing

Abstract High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks