airaTrack is a comprehensive personnel tracking system that utilizes AI algorithms developed by our team to extract features from both the "face and figure." It generates unique biometric IDs with a recognition accuracy of up to 99%, all without the need for registering facial data, addressing privacy concerns. Capable of cross-camera searches and matching 10,000 faces per second, it significantly enhances the efficiency of traditional manual matching processes and increases search efficiency and accuracy by 97% enabling automatic path tracking of personnel in the field. The system also generates investigation reports for evidential preservation.

Self-developed wireless low-power high-capacity communication protocol, suitable for medical IoT devices that pursue high battery life and low latency, and can connect 2,000 devices

Reflective LCD, clearly visible under the sun AI vehicle detection, camera technology law enforcement Multiple payments Solar rechargeable battery, no need to pull power

P-Waver Inc. is the global leader of AI on-site earthquake early warning system and structural safety monitoring system, providing earthquake early warning before earthquake, intelligent disaster mitigation measures, and structural safety monitoring after earthquake.

The Smart Dust Sensor technology is an innovative solution for integrating IoT networks into air quality monitoring. Developed collaboratively by the National Science and The National Science and Technology Council (NSTC), in collaboration with Taiwan Instrument Research Institute (TIRI) and Taiwan Semiconductor Research Institute (TSRI) of NARLabs, and leading academic teams in Taiwan, this technology has successfully miniaturized gas sensors while achieving low power consumption and high sensitivity. Its core features include exceptional sensitivity and stability, enabling real-time monitoring of multiple gases. Moreover, the fully domestically developed technology reduces dependence on imports and offers cost-effective sensor modules, driving the widespread adoption of air quality monitoring devices. The Smart Dust Sensor has undergone extensive field testing and third-party certifications across various applications. These include environmental monitoring stations (e.g., Taichung Dali Station), public recreational areas (e.g., odor monitoring in public restrooms), and industrial settings (e.g., semiconductor facilities and wastewater treatment plants). With its proven accuracy, reliability, and traceable data, the technology plays a key role in advancing air quality monitoring solutions, facilitating the development of smart cities, and contributing to improved environmental management.

NU1, for office buildings with mobile access control system, bringing down 70% cost on wire deployment and 80% energy saving while no need for host PC from the very beginning.

Traffic approach warning system is the combination of smart detectors and LED warning signs. The intersection is divided into detection lanes and warning lanes. Smart detectors are installed on the detection lanes. When a vehicle enters the detection area, the signal is transmitted to activate the LED signs on the warning lane to warn drivers to slow down in advance.

The Autonomous AI Surveillance Drone is a major innovation in drone technology, combining multirotor and fixed-wing capabilities. Utilizing white noise principles to enhance lift and efficiency, it enables high-speed flight and wide-area coverage, making it ideal for disaster response, border patrol, and other critical missions. Its core AI algorithm ensures reliability through real-time error detection. If anomalies are detected, the drone activates a safety mode, automatically returning or landing to minimize risks and maintain mission continuity. The flight control system integrates AI chips and edge AI technology, enhancing processing speed while reducing energy consumption for extended, efficient operation. These advancements enable stable autonomous flight even in complex environments.

This exhibition is an atmospheric physical-chemical grid model for prediction of changes in air quality, utilizing data from the U.S. NCEP global weather forecast and regional air pollutant emissions. Local scientific investigation data has been used to adjust and calibrate the model and thereby largely improve the accuracy of the air quality forecast over Taiwan. The 3D display system enables us to understand the transmission processes of cross-border and local air pollution. The system is capable of simulating the changes in air quality over the next three days (72 hours) in Taiwan, with a temporal resolution of 1 hour and a spatial resolution of 3 km. The simulated data can be integrated with air quality data from the Civil IoT Taiwan, providing air quality forecasts every three hours (totaling 72 hours) for six metropolitan areas This system offers regulatory authorities up-to-date information on air quality trends in Taiwan, as well as critical data for formulating control strategies.

Carbon Star is a specialized greenhouse gas (GHG) inventory management platform developed by StarTrust Systems, offering enterprises extensive carbon management capabilities. The platform encompasses organizational greenhouse gas inventories (ISO 14064-1) and product carbon footprint inventories (ISO14067), featuring the most extensive functional modules to enable enterprises to effectively gather carbon emissions data throughout their operations. 1. The platform is equipped with calculation benchmarks for carbon emissions and the latest factor data, facilitating enterprises in promptly collecting and computing data. 2. It integrates comprehensive management functions, including approval workflows and reminders, to assist various departments within enterprises in effectively managing greenhouse gas emissions. 3. Departmental access is segregated to ensure confidentiality across departments, allowing cross-departmental retrieval and referencing of only designated data. This measure guarantees the confidentiality of information from both manufacturing facilities and product domains. 4. Carbon Star automatically generates reports and emission inventories swiftly, streamlining enterprise management and verification processes. 5. Data analysis results are presented through a dashboard interface, empowering enterprises to compare and analyze data efficiently.

Vehicle Classification enables transportation authorities to automatically identify and classify vehicles according to their visual attributes. This technology finds widespread application in traffic management systems, toll collection, parking facilities, and security setups. FETCi leverages AI algorithms and machine learning methodologies to analyze video data collected through LiDAR technology. With an extensive database and repository, FETCi's AI AVC proficiently sorts vehicles into distinct categories, including cars, trucks, motorcycles, buses, and more. By integrating LiDAR technology with AI-powered image recognition, FETCi establishes a vehicle recognition system equipped with self-enhancement and deep learning capabilities.

Wonder Greener believes “straws” should naturally be reaped from soil! We plant “Lepironia articulata”, a kind of water grass, and turn which into 100% green drinking straws with our patented low-carbon process of production; meanwhile, promoting multiple ESG values. A NBS (Nature-based Solution) for sequestrating CO2 is a critical methodology on global carbon-net zero pathway, and currently proved the best practice as well. Growing Lepironia is a kind of “No-till farming”, which means we don’t plow on soil. Without tillage, the potential of soil carbon credits would be huge. We assess the carbon emission of every straw would confidently be “negative” with our low-carbon process of production. Besides throwing back to the farms, the wastes of farming and manufacturing grass straws, and even used straws, can be efficiently recycled to make “Lepironia-algae Tableware” with innovative algae technology. We try to combine “yellow carbon” from soil and “blue carbon” from oceans to create totally bio-degradable and recyclable green materials.