Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spotting

One difficult problem of keyword spotting is how to miniaturize its memory footprint while maintain a high precision. Although convolutional neural networks have shown to be effective to the small-footprint keyword spotting problem, they still need hundreds of thousands of parameters to achieve good performance. In this paper, we propose an efficient model based on depthwise separable convolution layers and squeeze-and-excitation blocks. Specifically, we replace the standard convolution by the depthwise separable convolution, which reduces the number of the parameters of the standard convolution without significant performance degradation. We further improve the performance of the depthwise separable convolution by reweighting the output feature maps of the first convolution layer with a so-called squeeze-and-excitation block. We compared the proposed method with five representative models on two experimental settings of the Google Speech Commands dataset. Experimental results show that the proposed method achieves the state-of-the-art performance. For example, it achieves a classification error rate of 3.29% with a number of parameters of 72K in the first experiment, which significantly outperforms the comparison methods given a similar model size. It achieves an error rate of 3.97% with a number of parameters of 10K, which is also slightly better than the state-of-the-art comparison method given a similar model size

Ordered transparent conductive oxides (TCOs) for applications to photoelectrochemistry

A method for fabricating high quality photonic TCO (transparent conductive oxide) films of macroporous FTO (fluorine doped tin oxide) (mac-FTO) and hollow sphere AZO (aluminum doped zinc oxide) (hs-AZO) is described. The films were used as electrodes to support photoelectrochemical reactions relevant to energy research. Methods have been developed for conformal coating the TCO electrodes with photoactive materials including CdS, Fe2O3 and C3N4. Previous literature describing photonic mac-FTO films generally show poor conductivity and optical properties, which limit the performance of structured TCOs in supporting photoelectrochemistry. Optimizing the synthesis and processing conditions gives high quality optical and conductive films of mac-FTO. Coating films with dispersed nanoparticles of CdS shows that the mac-FTO supports charge carrier transport to the contact and is not just a structural support for continuous conductive films of photoactive materials. Coating to maximise photocurrent gives over 9 mA cm-2 for conformally coated CdS@mac-FTO under visible light (> 420 nm) through a simple approach, showing an improvement in comparison to previous CdS literature work on structured electrodes. The new hs-AZO TCO also supports photocurrents up to 7.8 mA cm-2 after CdS coating. Both FTO and AZO show significant photocurrent enhancement in comparison to planar FTO analogues. In addition to CdS, methods were developed to conformally coat the organic photocatalyst C3N4 and the metal oxide Fe2O3 onto mac-FTO which showed enhanced photocurrent compared to planar analogues. Enhancements were typically in the range x (CdS), y (C3N4), and z (Fe2O3) which reflect the increase in surface area and greater loading of photoactive material. Potential photonic enhancements were not determined, however there is clearly scope for increasing the photocurrent per illuminated surface area using structured TCO electrodes

Analytical modeling of Lamb wave propagation in composite laminate bonded with piezoelectric actuator based on Mindlin plate theory

Dynamic analysis of plate structures based on the Mindlin plate theory has become one of the usual modeling methods for the structural health monitoring (SHM) of composite structures in recent years. Compared to the classical plate theory (CPT) based on Kirchhoff hypothesis, the Mindlin plate theory considers the influence of transverse shear deformation and moment of inertia on displacements. Thus it is more suitable for dynamic analysis of composite laminate with low transverse shear stiffness and large transverse shear deformation. Combining the adhesive layer coupling model of the piezoelectric actuator with the Mindlin plate theory, the dispersion curve of Lamb wave in any direction and mechanical parameters of any point in the composite are obtained, and thus after the substitution of boundary condition, the modeling of piezoelectric wafer excited Lamb wave propagating in composite laminate is realized. The validation experiment is performed on a carbon fiber composite laminate. It proves that the analytical modeling effectively reflects the propagation characteristics of Lamb wave in composite laminate and promotes the engineering application of SHM

Enhancing photoelectrochemical CO2 reduction with silicon photonic crystals

The effectiveness of silicon (Si) and silicon-based materials in catalyzing photoelectrochemistry (PEC) CO2 reduction is limited by poor visible light absorption. In this study, we prepared two-dimensional (2D) silicon-based photonic crystals (SiPCs) with circular dielectric pillars arranged in a square array to amplify the absorption of light within the wavelength of approximately 450 nm. By investigating five sets of n + p SiPCs with varying dielectric pillar sizes and periodicity while maintaining consistent filling ratios, our findings showed improved photocurrent densities and a notable shift in product selectivity towards CH4 (around 25% Faradaic Efficiency). Additionally, we integrated platinum nanoparticles, which further enhanced the photocurrent without impacting the enhanced light absorption effect of SiPCs. These results not only validate the crucial role of SiPCs in enhancing light absorption and improving PEC performance but also suggest a promising approach towards efficient and selective PEC CO2 reduction

Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery

Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery. One popular robotic system is the da Vinci surgical platform, which allows preoperative information to be incorporated into live procedures using Augmented Reality (AR). Scene depth estimation is a prerequisite for AR, as accurate registration requires 3D correspondences between preoperative and intraoperative organ models. In the past decade, there has been much progress on depth estimation for surgical scenes, such as using monocular or binocular laparoscopes [1,2]. More recently, advances in deep learning have enabled depth estimation via Convolutional Neural Networks (CNNs) [3], but training requires a large image dataset with ground truth depths. Inspired by [4], we propose a deep learning framework for surgical scene depth estimation using self-supervision for scalable data acquisition. Our framework consists of an autoencoder for depth prediction, and a differentiable spatial transformer for training the autoencoder on stereo image pairs without ground truth depths. Validation was conducted on stereo videos collected in robotic partial nephrectomy.Comment: A two-page short report to be presented at the Hamlyn Symposium on Medical Robotics 2017. An extension of this work is on progres