Reference guided image super-resolution via efficient dense warping and adaptive fusion

Due to the limited improvement of single-image based super-resolution (SR) methods in recent years, the reference based image SR (RefSR) methods, which super-resolve the low-resolution (LR) input with the guidance of similar high-resolution (HR) reference images are emerging. There are two main challenges in RefSR, i.e. reference image warping and exploring the guidance information from the warped references. For reference warping, we propose an efficient dense warping method to deal with large displacements, which is much faster than traditional patch (or texture) matching strategy. For the SR process, since different reference images complement each other, and have different similarities with the LR image, we further propose a similarity based feature fusion strategy to take advantage of the most similar reference regions. The SR process is realized by an encoder–decoder network and trained with pixel-level reconstruction loss, degradation loss and feature-level perceptual loss. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art SR methods in both subjective and objective measurements.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

Fabrication of paper based carbon/graphene/ZnO aerogel composite decorated by polyaniline nanostructure: Investigation of electrochemical properties

The cellulose derived carbon/graphene/ZnO aerogel composite was prepared as an electrode in order to investigate the electrochemical properties. Carbon aerogel was synthesized using paper as an available cellulose source, and the composite was obtained through a new and simple preparation method including the immersion of monolithic carbon aerogel in graphene oxide/Zn²⁺ suspension and subsequent chemical reduction and freeze drying. The morphology, functional groups and crystalline structure of the samples were studied with Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction Spectroscopy (XRD), respectively. Electrochemical performance of the prepared binder free electrodes was examined using Cyclic Voltammetry (CV), Galvanostatic Charge-Discharge (GCD) and Electrochemical Impedance Spectroscopy (EIS). The data revealed that flexible carbon/graphene/ZnO composite resulted in a low density (0.035 g cm⁻³) electrode with the capacitance of 900 mF cm⁻² at a high current density of 10 mA cm⁻², lower IR drop and high cyclic stability (capacitance retention of 96%) after 1000 cycles, at 10 mA cm⁻². These features were due to the presence of 3D porous conductive network, highly reduced graphene oxide, and the formation of ZnO nanoparticles on graphene sheets. Moreover, polyaniline (PANI) was introduced to carbon/graphene/ZnO composite electrode using electro-oxidation method at different reaction time and aniline concentration in order to achieve remarkably improved capacitance of 2500 mF cm⁻² (at 10 mA cm⁻²) and low charge transfer resistance. Also, after the supercapacitor device assembly, the capacitance was retained. Based on the results, the synthesized composite is a promising material for new generation of lightweight freestanding electrodes with the high electrochemical performance.     

Prelabeling Expansion Single-Molecule Localization Microscopy with Minimal Linkage Error

Expansion microscopy combined with single-molecule localization microscopy (ExSMLM) has a potential for approaching molecular resolution. However, ExSMLM faces multiple challenges such as loss of fluorophores and proteins during polymerization, digestion or denaturation, and an increase in linkage error arising from the distance between the fluorophore and the target molecule. Here, we introduce a trifunctional streptavidin to link the target, fluorophore and gel matrix via a biotinylizable peptide tag. The resultant ExSMLM images of vimentin filaments demonstrated high labeling efficiency and a minimal linkage error of ∼5 nm. Our ExSMLM provides a simple and practical means for fluorescence imaging with molecular resolution.     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

Simply constructed highly dispersed cobalt nanoparticles in diverse N-doped graphitic carbon with remarkable performances for water electrolysis

Metal/carbon composite materials are highly promising electrocatalysts for water electrolysis. In this work, three composites of metal cobalt nanoparticles highly dispersed in N-doped carbon materials were facilely constructed by pyrolysis of different phenylenediamine based Schiff base-Co complexes (PDBs). Interestingly, the composites derived from PDBs based on different phenylenediamine exhibited different morphologies. The superior case is that rodlike composite catalyst was derived from o-phenylenediamine based PDBs. The obtained catalyst exhibited remarkable performances for both cathodic hydrogen evolution reaction (HER) and anodic oxygen evolution reaction (OER), as well as overall water electrolysis. Only 172 and 289 mV of overpotentials and 1.57 V of cell voltage were exhibited at 10 mA cm^?2 for HER, OER and water splitting in 1.0 M KOH, respectively. The catalyst also displayed robust stability and high Faraday efficiency, and thus are potential high-performance catalyst for commercial water electrolysis.     

Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.     

High quality 3D reconstruction based on fusion of polarization imaging and binocular stereo vision

Polarization imaging can retrieve inaccurate objects’ 3D shapes with fine textures, whereas coarse but accurate depths can be provided by binocular stereo vision. To take full advantage of these two complementary techniques, we investigate a novel 3D reconstruction method based on the fusion of polarization imaging and binocular stereo vision for high quality 3D reconstruction. We first generate the polarization surface by correcting the azimuth angle errors on the basis of registered binocular depth, to solve the azimuthal ambiguity in the polarization imaging. Then we propose a joint 3D reconstruction model for depth fusion, including a data fitting term and a robust low-rank matrix factorization constraint. The former is to transfer textures from the polarization surface to the fused depth by assuming their relationship linear, whereas the latter is to utilize the low-frequency part of binocular depth to improve the accuracy of the fused depth considering the influences of missing-entries and outliers. To solve the optimization problem in the proposed model, we adopt an efficient solution based on the alternating direction method of multipliers. Extensive experiments have been conducted to demonstrate the efficiency of the proposed method in comparison with state-of-the-art methods and to exhibit its wide application prospects in 3D reconstruction.     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.