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International audience; With the development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. The primary value of AR is to promote the fusion of digital contents and real-world environments, however, studies on how this fusion will influence the Quality of Experience (QoE) of these two components are lacking. To achieve better QoE of AR, whose two layers are influenced by each other, it is important to evaluate its perceptual quality first. In this paper, we consider AR technology as the superimposition of virtual scenes and real scenes, and introduce visual confusion as its basic theory. A more general problem is first proposed, which is evaluating the perceptual quality of superimposed images, i.e., confusing image quality assessment. A ConFusing Image Quality Assessment (CFIQA) database is established, which includes 600 reference images and 300 distorted images generated by mixing reference images in pairs. Then a subjective quality perception experiment is conducted towards attaining a better understanding of how humans perceive the confusing images. Based on the CFIQA database, several benchmark models and a specifically designed CFIQA model are proposed for solving this problem. Experimental results show that the proposed CFIQA model achieves state-of-the-art performance compared to other benchmark models. Moreover, an extended ARIQA study is further conducted based on the CFIQA study. We establish an ARIQA database to better simulate the real AR application scenarios, which contains 20 AR reference images, 20 background (BG) reference images, and 560 distorted images generated from AR and BG references, as well as the correspondingly collected subjective quality ratings. Three types of full-reference (FR) IQA benchmark variants are designed to study whether we should consider the visual confusion when designing corresponding IQA algorithms. An ARIQA metric is finally proposed for better evaluating the perceptual quality of AR images. Experimental results demonstrate the good generalization ability of the CFIQA model and the state-of-the-art performance of the ARIQA model. The databases, benchmark models, and proposed metrics are available at: https://github.com/DuanHuiyu/ARIQA.

Transition metal hydroxides are a kind of promising electrode material in electrochemical energy storage, but the poor conductivity limits their application. Lanthanides are good proton conductors and can usually improve the intrinsic conductivity of other materials. By integrating the merits of lanthanide elements and transition metal hydroxide, we designed lanthanum oxide nickel hydroxide composites (LONH) with unique ultrathin triangle nanosheet morphology via a controllable synthetic strategy for high-performance supercapacitors. When the LONH is used as positive electrode material in aqueous asymmetric supercapacitor, it reveals an energy density (107.8 W h kg−1 at 800 W kg−1), rate performance (86.9% retention at 4 kW kg−1) and outstanding cycle stability (more than 90% retention after 3,000 cycles). This work confirms that compositing La2O3 and Ni(OH)2 can significantly improve the supercapacitor performance of both pristine La2O3 and transition metal hydroxide composites. We hope this work would offer a good prospect for developing other lanthanide-transition metal hydroxide composites as an attractive class of electrode materials in electrochemical energy storage.

Developing filtering materials with high permeation flux and contaminant removal rate is of great importance for oily wastewater remediation. Herein, a robust three-dimensional (3D) activated carbon fibers (ACFs) based composite with uniformly grown layered double hydroxide (LDH) on the surface was successfully constructed through a feasible hydrothermal strategy. The LDH with a high surface energy and vertically aligned structure could provide superhydrophilicity to ACFs. Systematic investigation confirmed that the 3D material could overcome the size mismatch between the ACFs macropores and tiny emulsified droplets through the combination of size-sieving filtration on the surface and oil droplet coalescence in the fiber network. This process efficiently separated the intractable surfactant-stabilized oil-in-water emulsions with high permeation flux (up to 4.16 × 106 L m−2 h−1 bar−1). Notably, the LDH also had well-dispersed catalytic active sites, which could initiate advanced oxidation processes (AOPs) to efficiently eliminate various types of water-soluble organic pollutants (e.g., pharmaceuticals, phenolic compounds and organic dyes). The resulting modified ACFs exhibited exceptional removal rates for both oil and organic pollutants in the complex sewage during the continuous filtration process. These versatile abilities integrated with the facile preparation method reported herein provide outstanding prospects for the large-scale treatment of oily wastewater.

By integrating the merits of lanthanide elements and quantum dots, we firstly design CeO2 quantum dots doped Ni-Co hydroxide nanosheet via a controllable synthetic strategy, which exhibits a large specific capacitance (1370.7 F/g at 1.0 A/g) and a good cyclic stability (90.6% retention after 4000 cycles). Moreover, we assemble an aqueous asymmetric supercapacitor with the obtained material, which has an extremely high energy density (108.9 Wh/kg at 378 W/kg) and outstanding cycle stability (retaining 88.1% capacitance at 2.0 A/g after 4000 cycles).

The focus of this article is to analyze and predict the visual attention of children with Autism Spectrum Disorder (ASD) when looking at human faces. Social difficulties are the hallmark features of ASD and will lead to atypical visual attention toward various stimuli more or less, especially on human faces. Learning the visual attention of children with ASD could contribute to related research in the field of medical science, psychology, and education. We first construct a Visual Attention on Faces for Autism Spectrum Disorder (VAFA) database, which consists of 300 natural scene images with human faces and corresponding eye movement data collected from 13 children with ASD. Compared with matched typically developing (TD) controls, we quantify atypical visual attention on human faces in ASD. Statistics show that some high-level factors such as face size, facial features, face pose, and facial emotions have different impacts on the visual attention of children with ASD. Combining the feature maps extracted from the state-of-the-art saliency models, we get the visual attention model on human faces for individuals with ASD. The proposed model shows the best performance among all competitors. With the help of our proposed model, researchers in related fields could design specialized education contents containing human faces for the children with ASD or produce the specific model for rapidly screening ASD using their eye movement data.

Designing nanocomposites with good electrochemical properties is one of the challenges in constructing supercapacitors. Adjustable metal-organic frameworks (MOFs) have potential research value in improving charge storage and transfer due to their multi-porosity. Moreover, MOFs can serve as a precursor to various derivatives. Herein, a series of core-shell structures with macro-microporous ZIF-67 (M-ZIF-67) as the core and layered double hydroxide (LDH) as the shell were synthesized based on polystyrene spheres (PSs) template via a simple ion etching method. As a result, the sample of M-ZIF-67@LDH4 shows a specific capacitance of 597.6 F g-1 at 0.5 A g-1 and a high rate retention of 92% at 3 A g-1 .

Objective. This study uses statistics to analyze the diagnostic significance of ultrasonic exploration for rotator cuff injuries. Methods. For this study, 50 patients with rotator cuff injury or shoulder impingement syndrome admitted to the hospital from September 2017 to January 2019 were selected as the targets of this discussion. The general clinical materials of the patients were retrospectively analyzed, and ultrasound was performed for them. The results of the examination and arthroscopy are compared with the final pathological results. Results. The diagnostic sensitivity of ultrasound in the diagnosis of partial rupture and complete rupture of the supraspinatus tendon was 100%, and the specificity was 55.55%. The diagnostic sensitivity of partial rupture and complete rupture of the subscapular tendon was 100%, and the specificity was 42.8%; there was no significant difference compared with the joint mirror examination, and there was no statistical significance. Conclusion. The thesis adopts ultrasound exploration for patients with rotator cuff injuries with high diagnostic sensitivity. It is a reliable and effective clinical diagnosis method, which is worthy of clinical application and promotion.

Eye tracking of preferential attention to biological motion is an important approach to Autism Spectrum Disorder (ASD) research and early diagnosis. However, the current method has all time-series information averaged as statistic information, which losing information and features for classification in high-dimensional space. Novel biological motion stimuli with well-designed time-series information could make Insistence on Sameness (IoS) of ASD as an efficient feature for ASD screening. Display area zoning and encoding for eye-tracking data processing improve the spatial resolution of the eye tracking information processing than averaging by side, making it possible to get more information and features for classification from ASD eye-tracking data.

This paper presents a novel method to identify children with Autism Spectrum Disorder (ASD) based on the stimuli with gaze-following. Individuals with ASD are characterized by having atypical visual attention patterns, especially in social scenes. Gaze-following is considered to be a key element in understanding social scenarios, and it is reasonable to use stimuli with gaze-following to identify the children with ASD. Thus in this paper, we first construct a dataset of eye movements in gaze-following scenes for children with ASD (i.e., GazeFollow4ASD dataset), including 300 images with gaze-following information inside them and the corresponding eye movement data collected from 8 children with ASD and 10 healthy controls. We propose a novel deep neural network (DNN) model to extract discriminative features and classify children with ASD and healthy controls on single images. The proposed model shows the best performance among all compared methods on all datasets.

Developing bifunctional catalysts for both the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) is crucial for obtaining hydrogen easily by water splitting to promote electrochemical energy conversion in fuel cells. In this paper, we report a facile approach for the synthesis of Pd nanoparticle assemblies (NPAs) with porous structure as catalysts for both the HER and the ORR with desirable electrocatalytic activities and long-term stability. For the HER, the porous Pd NPAs exhibit a low onset potential, a small Tafel slope of 30 mV dec–1, and an exceptionally low overpotential of about 80 mV at a current density of 100 mA cm–2, and these porous Pd NPAs maintain their catalytic activity for at least 1000 cyclic voltammetry (CV) cycles of durability in 0.5 m H2SO4. As for the ORR, the porous Pd NPAs offer a remarkable catalytic activity with a diffusion-limiting current density of 3.85 mA cm–2, a half-potential of 0.837 V, and an onset potential of 0.926 V at 1600 rpm with a scan rate of 5 mV s–1. This study enables the design of novel bifunctional electrocatalysts in the renewable energy field.

Social difficulties are the hallmark features of Autism Spectrum Disorder (ASD) and can lead to atypical visual attention towards stimuli. Eye movements encode rich information about attention and psychological factors of an individual, which could help to characterize the traits of ASD. Learning atypical eye movements of the individuals with ASD towards various stimuli is important and has many application scenarios. However, due to the lack of open datasets, research in this sense is still limited. In this work, we present an open dataset of eye movements of children with Autism Spectrum Disorder. It consists of 300 natural scene images and the corresponding eye movement data collected from 14 children with ASD and 14 healthy controls. In particular, fixation maps and scanpaths are available in the dataset. Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD. The dataset can be accessed in http://doi.org/10.5281/zenodo.2647418

Autism spectrum disorder (ASD) as a kind of mental disorder, has become an internationally recognized serious public health problem. Paintings of autistic children have not been compared systematically to those from Typically Developed (TD) children. In this work, we construct an ASD painting database which contains 478 paintings drawn by ASD individuals and 490 drawn by the TD group. Through subjective and objective analysis, some significant hallmarks, such as structuring logic, face, repetitive structure, composition location, edge completeness, etc. are found within the ASD paintings. We further train a classifier of ASD and TD painters using those extracted features, which shows encouraging accuracy as a potential screen tool for ASD. This work sheds light on understanding the uniqueness of autistic children through their paintings. The database will be released to the public.

As is known to us, people with Autism Spectrum Disorder (ASD) have atypical visual attention towards stimuli. Learning the visual attention of people especially, children, with ASD contribute to related research in the field of medicine and psychology. In this paper, we first construct a saliency prediction for children with autism (SPCA) database, which is the first of its kind and consists of 500 images and the corresponding eye tracking data collected from 13 different children with ASD. We compare the performance of five state-of-the-art deep neural networks (DNN)-based saliency prediction approaches with their original networks and the fine-tuned networks on our database. We predict the atypical visual attention of children with ASD for the first time and get the best saliency prediction results for individuals with ASD so far.

Omnidirectional images and videos can provide immersive experience of real-world scenes in Virtual Reality (VR) environment. We present a perceptual omnidirectional image quality assessment (IQA) study in this paper since it is extremely important to provide a good quality of experience under the VR environment. We first establish an omnidirectional IQA (OIQA) database, which includes 16 source images and 320 distorted images degraded by 4 commonly encountered distortion types, namely JPEG compression, JPEG2000 compression, Gaussian blur and Gaussian noise. Then a subjective quality evaluation study is conducted on the OIQA database in the VR environment. Considering that humans can only see a part of the scene at one movement in the VR environment, visual attention becomes extremely important. Thus we also track head and eye movement data during the quality rating experiments. The original and distorted omnidirectional images, subjective quality ratings, and the head and eye movement data together constitute the OIQA database. State-of-the-art full-reference (FR) IQA measures are tested on the OIQA database, and some new observations different from traditional IQA are made. The OIQA database will be released to facilitate further research.

To achieve the simultaneous and unobtrusive breathing rate (BR) and heart rate (HR) measurements during nighttime, we leverage a far-infrared imager and an infrared camera equipped with IR-Cut lens and an infrared lighting array to develop a dual-camera imaging system. A custom-built cascade face classifier, containing the conventional Adaboost model and fully convolutional network trained by 32K images, was used to detect the face region in registered infrared images. The region of interest (ROI) inclusive of mouth and nose regions was afterwards confirmed by the discriminative regression and coordinate conversions of three selected landmarks. Subsequently, a tracking algorithm based on spatio-temporal context learning was applied for following the ROI in thermal video, and the raw signal was synchronously extracted. Finally, a custom-made time-domain signal analysis approach was developed for the determinations of BR and HR. A dual-mode sleep video database, including the videos obtained under environment where illumination intensity ranged from 0 to 3 Lux, was constructed to evaluate the effectiveness of the proposed system and algorithms. In linear regression analysis, the determination coefficient (R2) of 0.831 had been observed for the measured BR and reference BR, and this value was 0.933 for HR measurement. In addition, the Bland-Altman plots of BR and HR demonstrated that almost all the data points located within their own 95% limits of agreement. Consequently, the overall performance of the proposed technique is acceptable for BR and HR estimations during nighttime.

Immersive videos are important components of virtual reality. However, when watching immersive videos, simulator sickness (SS) sometimes occurs. In addition, when there are visual oscillations (VOs) in immersive videos, visually induced motion sickness (VIMS) will make the videos’ quality of experience (QoE) worse. Most of existing VIMS studies under controlled experimental conditions use virtual patterns, e.g., black and white stripes. However, QoE of immersive real scenes are far more complicate than visual patterns. Therefore, in this paper, we mainly concern QoE degradations caused by VIMS in immersive videos with real scenes. To get controlled VOs, we add shake to panoramic videos captured by still camera and conduct subjective experiments among 15 individuals. We find that the velocity (frequency) of VOs, the time we immersed and the scenes’ contents will all influence the level of VIMS. And the QoE of immersive videos with real life content will be influenced correspondingly.

To estimate heart rate (HR) during night remotely and unobtrusively, we use a far-infrared camera and an RGB-Infrared camera to develop a dual-mode imaging system. The RGB or infrared images are first registered by the far-infrared images through the affine transformation. A custom-made cascade face classifier, which contains the conventional Adaboost model and fully convolutional network, was applied for the detection of the face in registered infrared images. The fully convolutional network was trained by 32K images from the PASCAL dataset. Subsequently, two facial tissues viz., mouth and nose were determined by the discriminative regression via the coordinate conversions of three selected landmarks. The spatio-temporal context learning was utilized to track the mouth and nose regions in the far-infrared image sequence. Then, the raw image feature was extracted from these two regions of interest. Finally, a state-of-art signal analysis method was explored to calculate HR in the time domain of the extracted raw signal. With respect to the validation experiment, we established the dual-mode sleep video database to verify the performances of the proposed system and algorithms. All videos in database were filmed under the environments where actual illumination intensity ranged from 0 to 3 Lux. The obtained results demonstrated that the determination coefficient (R2) was 0.933 for HR estimation in linear regression analysis. The Bland-Altman analysis showed that almost all the data points located within the 95% upper and lower limits of agreement, which were 4.293 and −5.293 bpm, respectively. Therefore, the proposed technique is efficient for the non-contact and unobtrusive HR estimation during night.

This paper presents a new database, Immersive Video Quality Assessment Database 2017 (IVQAD 2017), intended for immersive video quality assessment in virtual reality environment. Video quality assessment (VQA) plays an important role in video research fields. Nowadays virtual reality technology have been widely used and playing videos in virtual reality visual system is becoming more and more popular. However, existing research in VQA fields mainly focus on traditional videos. In this paper, we build the IVQAD which contains 10 raw videos and 150 distorted videos. Bit rate, frame rate and resolution were considered as quality degradation factors. All the videos were encoded with MPEG-4. Subjects were asked to assess the video under virtual reality environment and mean opinion score (MOS) was derived by computing. Using IVQAD 2017, researchers can explore the influence of resolution, video compression and video packet loss on immersive videos' quality.