The popularization and diffusion of compound-eye array camera technology faces formidable challenges. On the one hand, the high-resolution realization of compound-eye array camera systems usually relies on a large-scale number of cameras and high-pixel-density image sensors, with high system complexity and limited imaging real-time. Zoom imaging technology is expected to reduce the number of cameras and the need for sensor pixel density and improve imaging adaptability while taking into account the large field of view and high-resolution imaging capability of the compound eye. However, the traditional mechanical zoom method is slow and lacks dynamic responsiveness, and the introduction of compound-eye array cameras will cause a drastic increase in the size, weight, and power consumption, which makes it difficult to apply to compound-eye array cameras. On the other hand, the compound-eye array camera is susceptible to the interference of the imaging environment during the actual imaging, resulting in the degradation of the imaging quality and difficulty in giving full play to its resolution advantage, and due to the variability of the environmental interference factors and the inherent manufacturing tolerances caused by the variability between the sub-camera units, the traditional image processing algorithms are often difficult to complete the image information demodulation and enhancement of the compound-eye array camera. Therefore, the realization of fast optical zoom and high-fidelity resolution enhancement in compound-eye array cameras remains a key challenge to be solved.

Computer scientists at the University of Bath in the UK have reprogrammed a Roomba to perform four new tasks, showcasing how domestic robots can be harnessed during their regular downtime to make our lives easier. The team also proposes 100 additional ways these devices could be put to work when they would otherwise be inactive.

A new study in Engineering presents GlycoPro, a high-throughput sample-processing platform for multi-glycosylation-omics analysis. It overcomes limitations of existing methods, efficiently processes a large number of samples, and shows promise in breast cancer biomarker discovery, with potential for broader applications in glycosylation-related diseases research.

Scientists at the University of Leicester and NASA’s Glenn Research Center have combined cutting edge radioisotope power system technology with high efficiency power convertor technology

The successful test results demonstrate robustness and reliability for potential future spaceflight missions, and a pathway for applications in space

The Space Nuclear Power team based at Space Park Leicester travelled to NASA Glenn in January 2025 to support the project