Articles in 2022

Tong Ye and colleagues use a spectrum analyzer and the probability-maintained noise power ratio method to estimate the performance of nonlinear communications systems. The method is verified in seven different nonlinear mechanism and application scenarios.

Izzo and Gómez present a machine learning-based method for obtaining accurate density models of even irregular celestial bodies using minimal prior information. The work is validated on uniform and non-uniform density models of several visited asteroids.

Shi and colleagues create a miniature curvature sensor by integrating a light emitting diode and GaN-based photodetector into a plastic optical fiber. They detect finger motion to show potential for wearable sensing systems.

Asim Waqas and colleagues investigated the relationship between the architectures of deep artificial neural networks (DANNs) and their robustness to noise and adversarial attacks in computer vision. The researchers found that the robustness of DANNs was highly correlated with graph-theoretic measures of entropy and curvature. This finding could help design more robust DANN architectures.

Renn and Gharib experimentally investigate the application of reinforcement learning to provide integrated flow information for aerodynamic control of a wing system in a highly turbulent environment. The results can inform future gust mitigation systems for unmanned aerial vehicles and wind turbines.

Kaitlin Stouffer and colleagues describe Projective Large Deformation Diffeomorphic Metric Mapping (Projective LDDMM), a computational technique to integrate dense 3D tissue level MRI data with sparse measurements from histological or other optical imaging modalities. The authors demonstrate application through projection of neuropathological markers from histological images onto MRI data of the hippocampus.

Ionuţ-Gabriel Farcaş, Gabriele Merlo and colleagues developed a framework for uncertainty quantification and sensitivity analysis at scale by focusing on important input parameters. The framework was demonstrated to reduce computational effort and cost compared to standard methods in a turbulent transport simulation in the context of fusion research.

Roberto Torelli and colleagues propose a numerical framework to characterize fuel injection in internal combustion engines at multiple length and time scales. The approach demonstrates potential for increased fidelity in the flow dynamics by means of an affordable end-to-end methodology that links realistic injection operation to fuel combustion and engine emissions.

Shengnan Wang and colleagues report a digital twin framework of electrical tomography for quantitative imaging of a gas-liquid multiphase flow. This framework enables precise flow profile imaging using low-cost and noninvasive tomography techniques and can be extended to biomedical, aerospace and energy applications.

Bairaktaris and colleagues developed a printable flexible photodetector using a simple, scalable fabrication process. The photodetectors were demonstrated in practice in an augmented paper system. This low-cost technique could be also applied in robust and large-scale user interfaces.

An imaging device inspired by insect stereopsis allows for simultaneous near-distance microscopic imaging, high speed imaging at far distance and 3D depth imaging at intermediate distances. The camera, reported by Kisoo Kim and colleagues, gives clues as to how insects see the world and offers insights for designing compact cameras with multifunctional capabilities.

Shen and colleagues reported an unsupervised generative adversarial network (GAN) to identify patterns in leaves associated with superior mechanical properties and use 3D printing to build architected materials inspired by the patterns. In the future, this approach may be applied more broadly to natural materials to enable efficient algorithmic construction of structures with customized properties and form factors.

Fixed wing drone flight in dense (urban or forest) environments is challenging due to a need for a large area to turn. Inspired by the avian wing morphing, Enrico Ajanic and colleagues proposed and tested a drone with wings capable of folding and pitching, and a tail capable of folding and deflecting as a strategy to increase the roll moment, lift force, and reduce the turn radius. This finding enables agile drone flight within limited space.

Abdel-Rahman and colleagues introduce a discrete modular material-robot system that is capable of serial, recursive (making more robots), and hierarchical (making larger robots) assembly. This is accomplished by discretizing the construction into a feedstock of simple primitive building blocks combined with an algorithm to plan the optimal construction path and assemble the building blocks into functional units and swarms.

Professor John Provis gives a perspective on cement and concrete technology, from ancient masterpieces to a vision of a sustainable future.

Land subsidence adds to the problem of climate-driven sea-level rise in coastal regions. A recent publication in Nature Sustainability has quantified the relative rates of local land subsidence of 48 major coastal cities worldwide. The study found that relative local land subsidence is more spatially variable than IPCC estimates previously suggested, with cities in Asia suffering the most. The findings could refine predictions of relative sea level rise and better guide actions for planning, designing and implementing protection strategies for coastal cities.

Today we present a special issue of Communications Engineering exploring various ways in which engineering researchers are engaging with the challenge of building resilient infrastructure for a sustainable future.

In a recent work published in Nature Communications, Dr. Benjamin Schäfer and colleagues demonstrate the effect of Braess’ paradox in power grids, both in a lab-scale mimic and through real-world simulations of the German power network. The results lay the groundwork for more sustainable grid development.