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.