No abstract

In this study, we demonstrate an electrically driven, polarization-controlled metadevice to achieve tunable edge-enhanced images. The metadevice was elaborately designed by integrating single-layer metalens with a liquid-crystal plate to control the incident polarization. By modulating electric-driven voltages applied on the liquid-crystal plate, the metalens can provide two polarization-dependent phase profiles (hyperbolic phase and focusing spiral phase). Therefore, the metalens can perform two-dimensional focusing and spatial differential operation on an incident optical field, allowing dynamic switching between the bright-field imaging and the edge-enhanced imaging. Capitalizing on the compactness and dynamic tuning of the proposed metadevice, our scheme carves a promising path to image processing and biomedical imaging technology.

Meta-optics based on metasurfaces that interact strongly with light has been an active area of research in recent years. The development of meta-optics has always been driven by human’s pursuits of the ultimate miniaturization of optical elements, on-demand design and control of light beams, and processing hidden modalities of light. Underpinned by meta-optical physics, meta-optical devices have produced potentially disruptive applications in light manipulation and ultra-light optics. Among them, optical metalens are most fundamental and prominent meta-devices, owing to their powerful abilities in advanced imaging and image processing, and their novel functionalities in light manipulation. This review focuses on recent advances in the fundamentals and applications of the field defined by excavating new optical physics and breaking the limitations of light manipulation. In addition, we have deeply explored the metalenses and metalens-based devices with novel functionalities, and their applications in computational imaging and image processing. We also provide an outlook on this active field in the end.

Background The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG. Objective This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time. Methods The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15). Results The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was “acceptable,” with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype. Conclusions A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.

BACKGROUND The Australian Dietary Guidelines (ADGs) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADGs is poor in Australia. Given following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADGs. OBJECTIVE This study described the development process and design of a prototype mobile app for personalized dietary advice based on the ADGs for adults in Australia, with the aim to explore the usability of the prototype. The goal of the prototype was to provide personalized evidence-based support for self-managing food choices in real time. METHODS The guidelines of the design science paradigm were applied to guide the design, development and evaluation of a progressive web app using the Amazon Web Services Elastic Compute Cloud services via iterations. The Nutrition Care Process elements, the strategies of cognitive behavioral theory and the ADGs were translated into the prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior change. A cross-modal image-to-recipe retrieval under Apache-2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semi-structured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (n =15). RESULTS The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goals setting for food choices, as well as the provision of recipe ideas and information on the ADGs. The overall prototype quality score was ‘acceptable’ with a median of 3.46 (interquartile range: 2.78, 3.81) of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (interquartile range: 2.75, 4.08) of 5 points. In-depth interviews identified the use of gamifications for tracking food choices and innovation in image-based dietary assessment as the main drivers of the positive user experience of using the prototype. CONCLUSIONS A novel evidence-based prototype mobile app was successfully developed leveraging cross-disciplinary collaboration. The detailed description of the development process and design of the prototype enhances transparency of the prototype and provides detailed insights into its creation. This study provides a valuable example of developing novel, evidence-based apps for personalized dietary advice on food choices using recent advancement in computer vision. A revised version of the prototype is currently under development.