WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Synthesis of thermoresponsive copolymer/silica-coated magnetite nanoparticle composite and its application for heavy metal ion recovery

To enhance chemical stability and suppress of aggregation of magnetite nanoparticles (MNPs), which are used as a support for thermoresponsive copolymer immobilization, silica coating of the MNPs is applied via the electrooxidation method. Although the resulting silica coated-MNPs also formed aggregates, the size distribution of the aggregate shifted to smaller size range. Because of that, the surface area available for copolymer immobilization increased approximately 6.7 times at maximum as compared with that of the uncoated MNPs. It contributed to the increase of the amount of the immobilized copolymer on the silica-coated MNPs, which is approximately four times larger than that on the uncoated MNPs. Fe₃O₄ dissolution test confirmed enhancement of chemical stability of MNPs. The thermoresponsive copolymer immobilized on the silica-coated MNPs shows the ability to recycle Cu(II) ion from Cu(II) containing solution by changing temperature with significantly shorter time than those in other thermoresponsive adsorbents in gel form.     

Achieving fabrication of highly transparent Y2O3 ceramics via air pre-sintering by deionization treatment of suspension

An easy albeit quite effective deionization suspension treatment was adopted to alleviate the detrimental effects related to the hydrolysis of Y₂O₃ in an aqueous medium. Fabrication of highly transparent Y₂O₃ ceramics with a fine grain size via air pre-sintering and post–hot isostatic pressing (HIP) treatment without using any sintering additive was achieved using the treated suspensions. The hydrolysis issue of Y₂O₃ powder in an aqueous medium was effectively alleviated by using deionization treatment, and a well-dispersed suspension with a low concentration of dissolved Y³⁺ species was obtained. The dispersed suspensions were consolidated by the centrifugal casting method, and the green bodies derived from the suspension of 35.0 vol% solid loading showed an improved homogeneity with a relative density of 52.1%. Fully dense Y₂O₃ transparent ceramic with high transparency was obtained by pre-sintering consolidated green compacts at a low temperature of 1400°C for 16 h in air followed by a post-HIP treatment at 1550°C for 2 h under 200 MPa pressure. The sample had a fine average grain size of 690 nm. The in-line transmittance of the sample reached 83.3% and 81.8% at 1100 nm and 800 nm, respectively, very close to the theoretical values of Y₂O₃.     

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.     

Synthesis of succinic acid-based polyamide through direct solid-state polymerization method: Avoiding cyclization of succinic acid

Succinic acid is an important synthetic monomer but it is difficult to use it as a precursor for synthesizing high molecular weight polyamide, due to its tendency to perform intra-cyclization reaction at high temperature. In order to solve this problem, in this paper, the direct solid-state polymerization (DSSP) method with the initial reactant, nylon salt which was composed of 1, 5-diaminopentane, succinic acid, and terephthalic acid, was applied to synthesize the bio-based copolyamide PA 5T/54. In comparison with the conventional melting polymerization method, the DSSP method can prevent the cyclization reaction of succinic acid effectively due to the lower reacting temperature as well as the restriction effect of the nylon salt. As a result, the product fabricated by DSSP method has higher molecular weight and much lighter color from red to white. Therefore, the DSSP method is advantageous for the synthesis of the polymers or copolymers composed of the succinic acid as the monomer. Furthermore, the polymerization mechanism proposed in this work can serve as a guidance for the design of the molecular structure and control of the polymerization process.     

食品安全国家标准中维生素的测定方法及进展

人体所必需的营养素除了蛋白质、脂类、糖类等能量物质外,维生素也是不可或缺的营养物质,是评价其营养质量的关键指标。检测食品中的维生素含量,是有效保证其质量与安全的必要条件。因此,对现有国家标准食品中维生素的检测方法进行综述,总结各方法的前处理及检出限等,为增强检测工作效率,以及食品检测方法的研究和发展提供参考。     

浅谈压力铸造的全自动化生产

近年来,国家对制造业的数字化、网络化、智能化越来越重视,特别是《中国制造2025》的发布,更是促进整个制造业向智能制造迈进,铸造行业也应该紧跟步伐。在传统压力铸造行业中压铸机的加料、取件,切边机的上件、下件,加工机床的上件、下件,都是依靠人来完成,大大制约了行业的发展。本文阐述了压力铸造的全自动化生产,为压力铸造行业的转型升级提供借鉴。     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

Single image dehazing using a new color channel

Images with hazy scene suffer from low-contrast, which reduces the visible quality of the scene, thus making object detection a more challenging task. Low-contrast can result from foggy weather conditions during image acquisition. Dehazing is a process of removal of haze from the photography of a hazy scene. Single-image dehazing based on dark channel priors are well-known techniques in this field. However, the performance of such techniques is limited to priors or constraints. Moreover, this type of method fails when images have sky-region. So, a method is proposed, which can restore the visibility of hazy images. First, a hazy image is divided into blocks of size 32 × 32, then the score of each block is calculated to select a block having the highest score. Atmospheric light is calculated from the selected block. A new color channel is considered to remove atmospheric scattering, obtained channel value and atmospheric light are then used to calculate the transmission map in the second step. Third, radiance is computed using a transmission map and atmospheric light. The illumination scaling factor is adopted to enhance the quality of a dehazed image in the final step. Experiments are performed on six datasets namely, I-HAZE, O-HAZE, BSDS500, FRIDA, RESIDE dataset and natural images from Google. The proposed method is compared against 11 state-of-the-art methods. The performance is analyzed using fourteen quantitative evaluation metrics. All the results demonstrate that the proposed method outperforms 11 state-of-the-art methods in most of the cases.     

Effect of milling ball size on the densification and optical properties of transparent Y2O3 ceramics

In this study, we report highly transparent Y₂O₃ ceramics fabricated by hot-pressing only at 1500 °C without a HIP treatment, featuring in-line transmittance levels of 77% and 84% at a wavelength of 400 and 1100 nm, respectively with the grain size suppressed to 710 nm. The effect of the ball size during the grinding of Y₂O₃ powders on the correlation between the thus-prepared Y₂O₃ powders and the optical properties of the hot-pressed samples is demonstrated for the first time. With a decrease in the diameter of the ZrO₂ balls from 5 mm to 1 mm, the milling efficiency was enhanced and admirable transparency of Y₂O₃ was attained at a short milling time. However, several micron-sized pores remained in the transparent specimens prepared with 1 mm balls, originating from the inhomogeneously packed region of the green body. Finally, the 2 mm was found to be optimum for obtaining a fine-grained and pore-free microstructure with the best in-line transmittance of Y₂O₃ ceramics.