Simultaneous localization and mapping (SLAM) is widely used in autonomous driving, augmented reality, and embodied intelligence. In real-world settings, sensor measurements often suffer from substantial clutter (false alarms) and missed detections, which complicate SLAM data association. This complexity manifests as uncertainty in associating observations to landmarks, the possibility of erroneous associations between clutter and landmarks, and the potential absence of landmark observations. Random Finite Set (RFS) theory offers a Bayesian estimation framework well suited to SLAM with uncertain data association and an unknown, time-varying number of landmarks, and has spurred extensive research on RFS-based SLAM methods. Particle-filter-based Probability Hypothesis Density (PHD)-SLAM can effectively estimate the joint probability density of the pose and the map under clutter and missed detections, yielding robust SLAM performance. However, improving the estimation accuracy of particle-filter PHD-SLAM typically requires increasing the number of particles, which rapidly scales the computational cost.

The proliferation of rooftop solar panels and distributed batteries in residential neighborhoods has created new challenges for power grid operators. Blockchain technology is emerging as a promising solution for enabling secure energy trading among these networked communities. However, designing a blockchain system that can handle the real-time operational requirements and cybersecurity concerns of actual power systems remains a critical challenge. To address this issue, researchers at Illinois Institute of Technology developed and tested a permissioned blockchain system on networked microgrids connecting the IllinoisTech campus with the Bronzeville community in Chicago, demonstrating significant cost savings and revenue increases for participating neighborhoods.

Onboard model, capable of providing estimated measurable values and unmeasurable performance parameters of interest with the maximal fidelity, serves as the cornerstone for aircraft engine control and fault diagnosis. As aircraft engine configurations grow increasingly complex to meet the performance specifications of next-generation propulsion systems, significant challenges is proposed to the accuracy and real-time performance of onboard models. Consequently, the development of onboard modeling techniques has become increasingly crucial.

In this study, we propose a spatiotemporal multiplexed photonic reservoir computing (STM-PRC) architecture, specifically designed for parallel prediction of the high-dimensional chaotic dynamics in complex SL networks. 

Researchers at Universidad Carlos III de Madrid (UC3M) have developed a set of innovative methods and algorithms that improve the performance and precision of vehicle design through topological optimization, a mathematical technique that allows designs to be optimized by distributing materials efficiently. The results of the research, which these scientists have applied to the design of parts for a competition motorcycle to reduce its weight while maintaining performance, could have a major impact on sectors such as the automotive and aeronautics industries.

 

Climate change threatens agricultural production across sub-Saharan Africa, where most farmers rely on rainfall. A study by researchers at the University of Göttingen and the European Commission’s Joint Research Centre shows that Ghanaian cocoa farmers who cultivate cocoa under shade trees – a practice known as agroforestry – are better able to withstand periods of reduced rainfall. However, the study also finds that these benefits are confined to Ghana’s wetter regions, which have a climate that better suits growing cocoa. In drier regions, where water is already scarce, the researchers find no significant advantages of agroforestry in maintaining yields during times of less rainfall. The results were published in the journal Agricultural Systems.