Creating interactive papercrafts often involves the processes of craft making (e.g., folding, cutting, gluing, etc.) and fabricating the embedded functional circuitry. These two processes are usually separated in the current practice, potentially making the workflow laborious and affecting the in-paper circuit stability. To address the issue of the separated crafting and circuiting processes, we present LiqMetCraft, a toolkit for creating electronics-embedded papercrafts through an integrated process. The toolkit allows users to construct the craft structure by folding and cutting, and these crafting actions forms the circuit traces simultaneously. This is achieved by using liquid-metal-dyed paper-like fabric which partially becomes conductive due to the cutting/folding-induced pressure while the unpressed parts of the paper remain insulated. The LiqMetCraft toolkit consists of software interfaces for papercraft design and instructions, and hardware components, mainly the liquid-metal-dyed paper-like fabrics and other off-the-shelf components, for physical prototyping. Currently our toolkit supports the making of electronics-embedded paper cutting, origami, and 3D paper models. The user studies with 42 participants shows that the participants quickly learned the toolkit and found the integrated process of circuit assembly and shape formation to be engaging and inspiring.

In recent years, vehicular communications have attached great interests in both academy and industry for its potential of promoting safety and autonomous driving. Unlike classical communication scenarios, in vehicular communications the optimal resource allocation must be accomplished in a real-time manner, in order to maximally reduce the response delay. This presents a substantial challenge for current machine learning based intelligent resource optimization methods which may be sample inefficient, especially when the problem space becomes extremely huge. In this study, we develop a fast reinforcement learning (RL) framework for the real-time resource optimization of vehicular communications, whereby the transmitting power and the accessing frequency channels need to be jointly allocated. The main concept of our new method is that it incorporates a sample efficient structured exploration mechanism in the action space, which firstly ignores the local exploitation but focuses on a randomized global exploration. Thus, our exploration-first method, in contrast to classical exploitation-first RL, can reconstruct the coarse-grained global landscape of a huge Q-table from only the few samples. This learned prior knowledge would remarkably accelerate the convergence of subsequent incremental learning process, by concentrating on the identified attentional subspace of the Q-table. As demonstrated by numerical results, our new method would reduce the time complexity or the response delay by around 10 folds. As such, our fast RL method would have the great potential to such challenging optimization problems whereby the acquisition of massive training samples is time demanding, which hence provides the great promise to the emerging vehicular networks.

Chlorophyll, a key pigment in leaf photosynthesis, is crucial for monitoring tobacco growth, evaluating quality, and determining optimal harvest timing. Hyperspectral remote sensing (HRS) via unmanned aerial vehicles (UAVs) provides a viable method to assess tobacco leaf chlorophyll content (LCC) due to its real-time and high-throughput capabilities. However, the existing spectral feature extraction methods often suffer from variability due to external factors or internal parameters, resulting in unstable results. To address this, we proposed a novel feature construction method, termed segmented fitting for integral (SFI). This method divides the entire spectral curve into five areas based on the spectral response properties of chlorophyll and nitrogen and then selects a suitable fitting function for each area to calculate the integrals. This idea not only fully utilizes the spectral reflectance and curve shape characteristics but also remains largely unaffected by external factors and internal parameters. To further verify the stability and predictive capability of the SFI method, we compared it against three different feature extraction methods, including the successful projections algorithm (SPA), the recursive feature elimination (RFE), and the principal component analysis (PCA), and also with two ensemble learning-based modeling approaches, namely, random forest (RF) and adaptive boosting (AdaBoost). The results demonstrated that the SFI method can effectively reduce data dimensionality and enhance model performance. Finally, we applied the best-performing SFI-AdaBoost model in field prediction to generate chlorophyll distribution and error maps, which closely aligned with actual chlorophyll measurements and demonstrated its potential for practical evaluation of tobacco LCC.