Facility emergence evacuation is often a complicated process under extreme conditions. Most of the buildings today use pre-installed signages to guide the emergence evacuation. However, these guidances are sometimes insufficient or misleading, particularly for evacuating from high-rise buildings or complex buildings, such as schools, hospitals, and stadiums. Following a planned route may lead the crowd to move towards dangers, such as smoke and fire. The future emergency guidance system should be more intelligent and be able to guide people to evacuate with a higher survival possibility. This study proposes a real-time building evacuation model with an improved cellular automata (CA) method. This algorithm combines cellular automata with the potential energy field (PEF) model in fluid dynamic theory (FDT) to choose safe paths for the crowd and reduce the possibility of stampedes. Custom-designed wireless sensors, artificial intelligence (A.I.) enhanced surveillance cameras, intelligent emergency signage systems, and edge computing servers are used to sample fire and crowd data, operate the intelligent evacuation algorithm, and guide the crowd with the signage system in real-time conditions. In addition, we performed the algorithm simulation on a two-dimensional plane generated based on the building structure of the Beijing Capital Airport Hospital. The evacuation drill simulations show that the average escape time is significantly shortened with optimal real-time guidance. In one case, a 72% reduction in evacuation time is achieved compared with evacuation using pre-installed signages. The results also demonstrated that the proposed model and system’s evacuation time reduction performance is particularly good in crowded buildings, such as schools or stadiums.

Implantable LED light sources have received a lot of attention in the field of optogenetic neuromodulation. This type of light modulation enables effective stimulation of neurons. However, as optogenetics moves towards clinical trials, combinatorial photostimulation based on arrays of LED light sources has emerged. This approach inevitably brings about a large increase in transient temperature, resulting in the inability to achieve precise stimulation of cells. We designed a wireless optogenetic hardware system to realize the control of the stimulation mode and temperature of the light source array. At the same time, a set of combined photothermal physical model was established to simulate the photothermal response of the whole experiment. The physical model can effectively guide the wireless optogenetic hardware circuits to perform effective stimulation within a controlled temperature range. Our model provides a new technical approach for photothermal studies in optogenetic clinical trials.

With the development of IoT technology, many dimensions of data are generated in the environment where we live. The study of these data is critical to our understanding of the relationships between people and between people and cities. The core components of IoT technology are sensors and control circuits. However, merging various sensor data and real-time data processing is often a difficult problem, usually related to factors such as coverage, lighting conditions, and accuracy of object detection. Therefore, we firstly propose a wireless transmission hardware architecture for data acquisition mainly based on vision sensors, and at the same time, incorporate some sensors for data calibration to improve the accuracy of data detection. The collected data are fed back to the edge computing platform for fast processing. The edge platform is designed with a lightweight target detection model and data analysis model. Through this multidimensional data collection and analysis, a generalised functional model for public space utilization can be fitted, which enables the calculation of utilization rates for any parameter in public space. The technology improves a technical reference for multi-dimensional data collection and analysis.

We propose the use of micro-LED arrays as a powerful tool to achieve precise stimulation of neuronal cells in time and space. In particular, we have developed a control system for in vivo implant stimulation and in vitro neuronal stimulation of organisms, through which up to 4096 individual micro-LED light sources can be controlled. The system can be integrated into microscopes and membrane clamp electrophysiology devices for efficient optogenetic monitoring. The final experiments show that different combinations of irradiation of the photopolar array can be used to effectively stimulate neurons transfected with photosensitive proteins by varying the light power.