To support children’s food interaction and enhance their understanding of food through morphing food technology, we develop a design exploration through the Research through Design (RtD) methodology. Our exploration integrates four stages: (1) defining design objectives through empathy with stakeholders, (2) investigating morphing food materials to understand their deformation mechanisms, (3) designing and iteratively developing tools based on user feedback, and (4) conducting a workshop-based evaluation. Our design outcome is a toolkit, comprising a morphing food library, trigger tools, and instructional interfaces. The workshop showed that through interaction with morphing food, children learned not only scientific principles but also developed culinary skills, as well as the diversity of food forms and functions. We discussed the detailed findings, insights, and implications for future design.

Shape-changing interfaces use physical changes of shape as input or output to convey information, and interact with users. Plants are natural shape-changing interfaces, expert in adjusting their shape or modality to adapt to the environment. In this paper, plant-derived natural shape-changing phenomena are systematically analyzed. Then, several corresponding plant-inspired design strategies for shape-changing interfaces are summarized with recent advancements including material selections and syntheses, fabrication methods, and actuating mechanisms. Practical applications across diverse domains aim to prove the advantages and potential of plant-inspired shape-changing interfaces in agriculture, healthcare, architecture, robotics, etc. Furthermore, the opportunities and challenges are also discussed, such as design thinking in interdisciplinary tasks, dynamic behavior and control principles, novel materials and processes, application scenario and functionality matching, and large-scale application requirements. This paper is expected to inspire in-depth research on plant-inspired shape-changing interfaces.

This paper presents MagneDot, a novel method for making interactive magnetic shape displays through an integrated fabrication process. Magnetic soft materials can potentially create fast, responsive morphing structures for interactions. However, novice users and designers typically do not have access to sophisticated equipment and materials or cannot afford heavy labor to create interactive objects based on this material. Modified from an open-source 3D printer, the fabrication system of MagneDot integrates the processes of mold-making, pneumatic extrusion, magnetization, and actuation, using cost-effective materials only. By providing a design tool, MagneDot allows users to generate G-code for fabricating and actuating displays of various morphing effects. Finally, a series of design examples demonstrate the possibilities of shape displays enabled by MagneDot.

In this paper, we present X-Hair, a method that enables 3D-printed hair with various forms, properties, and functions. We developed a two-step suspend printing strategy to fabricate hair-like structures in different forms (e.g. fluff, bristle, barb) by adjusting parameters including Extrusion Length Ratio and Total Length. Moreover, a design tool is also established for users to customize hair-like structures with various properties (e.g. pointy, stiff, soft) on imported 3D models, which virtually shows the results for previewing and generates G-code files for 3D printing. We demonstrate the design space of X-Hair and evaluate the properties of them with different parameters. Through a series of applications with hair-like structures, we validate X-hair’s practical usage of biomimicry, decoration, heat preservation, adhesion, and haptic interaction.

Existing methods in human-computer interaction to enhance children’s eating habits predominantly rely on digital interactive technologies, which pose the risk of increasing sensory stimulation and diverting children’s attention away from the food itself. Drawing inspiration from shape-changing food research, we propose an approach that combines deformable wafer paper for food preparation. We summarize the principles of wafer paper controllable deformation and develop a toolkit to facilitate its use. We support children in creating personalized, transformable food items using this method, aiming to provide a playful, convenient, and safe food-making experience tailored for children, thereby enhancing children’s mealtime engagement and habits.

The majority of current dimension reduction methods are restricted to the use of spectral information, when the spatial information is left out. In order to overcome this defect, two different solutions: patch-based and tensor-patch-based approaches, were studied in this paper. This paper applies the two solutions to a group of graph-based dimension reduction methods. We found that the patch-based and tensor-patch-based variations greatly boost the final classification results by 5%-15% from the traditional methods. As graph-based methods heavily rely on the calculation of adjacency graphs/weight matrices, this paper proposed the use of a new method: weighted region covariance matrix, to produce the adjacency graphs/weight matrices. In results, the newly proposed method can further improve the dimension reduction results in both the patch-based and tensor-patch-based methods. To reduce the intense computation in the adjacency graphs/weight matrix calculation, the principle component analysis (PCA) is proposed by this paper as a preprocess step.

In recent years, a new genre of hyperspectral unmixing methods based on nonnegative matrix factorization (NMF) have been proposed. Unlike traditional spectral unmixing methods, the NMF-based hyperspectral unmixing methods no longer depend on pure pixels in the original image. The NMF is based on linear algebra, which requires that the hyperspectral data cube is converted from 3-D cube to a 2-D matrix. Due to this conversion, the spatial information in the relative positions of the pixels is lost. With the emergence of multilinear algebra, the tensorial representation of hyperspectral imagery that preserves spectral and spatial information has become popular. The tensor-based spectral unmixing was first realized in 2017 using the matrix-vector nonnegative tensor factorization (MVNTF) decomposition. Using the construction of MVNTF spectral unmixing, this letter proposes to integrate three additional constraints (sparseness, volume, and nonlinearity) to the cost function. As we show in this letter, we found that the three constraints greatly improved the impervious surface area fraction/classification results. The constraints also shortened the processing time.

The future launch of EnMAP satellite in 2019 will enable the acquirement of hyperspectral data for obtaining more accurate relevant surface parameters on a global scale. This paper will discuss the application of EnMAP in extracting impervious surface area (ISA) from urban-rural gradient scenes. Considering the need for ISA extraction in urban studies, the choice of endmembers are made of five mixed types: vegetation, high and low albedo (impervious surface area), soil, and water. This research uses the robust non-negative matrix factorization to simultaneously generate endmembers and the corresponding abundances. It has been found that the strategy of mixed endmembers is a reasonable approach in median spatial resolution spaceborne imagery. Even with the median resolution of 30m, the EnMAP derived ISA is comparable to the result from high spatial resolution (1m) airborne data.

Snow accumulation to the ice sheet offsets ice losses near the margin, and characterizing ice sheet accumulation rate is necessary for understanding ice sheet mass balance and predicting future sea level rise. Ice penetrating radar systems enable the measurement of ice sheet properties beneath the surface, including internal layering. This study concentrates on mapping the depth of internal layers, and linking the layers to a chronology that allows snow accumulation rates over particular time periods to be determined. This study focuses on one particular ice penetrating radar system: Snow Radar from the Center for Remote Sensing of Ice Sheet (CReSIS). The measurement error from the radar data process has been evaluated and quantified. A difference about 0.017 m caused by manual process in annual accumulation was identified between the radar derived data and true values. The chronology of Snow Radar detected layers is validated to be annual using nearby ice core data and the results of a regional climate model.