Owing to rapid climate change, large-scale floods have occurred yearly in cities worldwide, causing serious damage. We propose a real-time urban flood-monitoring technology as an urban disaster prevention technology for sustainable and secure smart cities. Our method takes advantage of the characteristic that water flow is regularly detected at a certain distance with a constant Doppler velocity within the radar observation area. Therefore, a pure flow energy detection algorithm in this technology can accurately and immediately detect water flow due to flooding by effectively removing dynamic obstacles such as cars, people, and animals that cause changes in observation distance, and static obstacles that do not cause Doppler velocities. Specifically, in this method, the pure flow energy is detected by generating a two-dimensional range–Doppler relation map using 1 s periodic radar observation data and performing statistical analysis on the energy detected on the successive maps. Experiments to verify the proposed technology are conducted indoors and in real river basins. As a result of conducting experiments in a narrow indoor space that could be considered an urban underpass or underground facility, it was found that this method can detect flooding situations with centimeter-level accuracy by measuring water level and flow velocity in real time from the time of flood occurrence. And the experimental results in various river environments showed that our technology could accurately detect changes in distance and flow speed from the river surface. We also confirmed that this method could effectively eliminate moving obstacles within the observation range and detect only pure flow energy. Finally, we expect that our method will be able to build a high-density urban flood-monitoring network and a high-precision digital flood twin.

The melting layer (ML) is an important region used to describe the transition of hydrometeors from the solid to the liquid phase. It is a typical feature used to characterize the vertical structure of the stratiform precipitation. The present study implements a new automatic melting-layer detection algorithm based on the range-height-indicator–quasi-vertical profile (R-QVP) in the X-band dual-polarization radars. The algorithm uses the gradients of the polarimetric radar variables reflectivity factor at horizontal polarization (Z_h), differential reflectivity (Z_dr), and copolar correlation coefficient (ρ_hv), and their combinations to describe the ML characteristics. The melting layer heights derived from the radar were compared and validated with the heights of the 0 °C wet-bulb temperature derived from the Modern-Era retrospective analysis for research and applications (MERRA) reanalysis datasets and obtained high correlation coefficient 0.96. The R-QVP combined with this algorithm led to spatial and temporal variabilities of the melting layer thickness. The thickness of the melting layer was independent of the seasonal, spatial, and temporal variabilities of the precipitations. Intriguing polarimetric signatures have been observed inside, above, and below the ML, based on the phase of the precipitation particles. The statistics of the polarimetric variables were evaluated for ML, rain, and snow. Further, the linkage between enhanced specific differential phase shift (K_dp) and Z_dr in the dendritic growth layer (DGL) and surface precipitation was also described.

Research related to thermal cameras, which are major control measures, is increasing to overcome the limitations of closed-circuit television (CCTV) images. Thermal cameras have the advantage of easily detecting objects at night and of being able to identify initial signs of dangerous situations owing to changes in temperature. However, research on thermal cameras from a comprehensive perspective for practical urban control is insufficient. Accordingly, this study presents a thermal camera-based abnormal-situation detection service that can supplement/replace CCTV image analysis and evaluate service preferences. We suggested an integrated Fuzzy AHP/TOPSIS model, which induces a more reasonable selection to support the decision-making of the demand for introducing thermography cameras. We found that developers highly evaluated services that can identify early signs of dangerous situations by detecting temperature changes in heat, which is the core principle of thermography cameras (e.g., pre-fire phenomenon), while local governments highly evaluated control services related to citizen safety (e.g., pedestrian detection at night). Clearly, while selecting an effective service model, the opinions of experts with a high understanding of the technology itself and operators who actually manage ser-vices should be appropriately reflected. This study contributes to the literature and provides the basic foundation for the development of services utilizing thermography cameras by presenting a thermography camera-based abnormal situation detection service and selection methods and joint decision-making engagement between developers and operators.

This paper addresses the viral infectious watermarking (VIW) model using biological virus infection for a new-paradigm of video copyright protection of MPEG/H.264/AVC/HEVC. Our model aims to spread or infect the watermark to different codecs each time video contents are copied, edited, or transcoded. Thus, we regard the watermark as the infectious virus, the video content as the host, and the video codec as the contagion medium and then model pathogen, mutant, and contagion as the infectious watermark. Then, we define the techniques of viral infectious watermark generation, kernel-based VIW, and content-based VIW. Furthermore, we present a reversible VIW for fast infection in VIW model. This makes the video quality and strength be adaptively controlled in the infectious process. Experiment results verified that our VIW model can detect or recover the reversible watermark without loss in different codecs and also can maintain the quality of video content that is recovered to the same bit rate.

Particulate matter (PM) poses serious health risks, including respiratory and cardiovascular diseases, and is classified as a carcinogen by the World Health Organization and International Agency for Research on Cancer. Roadside air pollution, which is strongly affected by traffic emissions, is a major contributor to urban air quality deterioration. This study investigated the feasibility of establishing a low-cost, Internet of Things (IoT)-based, high-density monitoring network for roadside PM₁₀ and PM_2.5 to support safer and more sustainable road environments. We developed low-cost IoT sensing devices, deployed them at three urban roadside sites with different environmental conditions, and compared their performances with those of nearby public monitoring stations. One-minute resolution data were analyzed using Pearson correlation, cross-correlation, dynamic time warping, Z-score, and the roulette index. The IoT sensor data were strongly correlated with public station data, confirming its reliability as a complementary observation method. Notable site-specific patterns were sharp concentration increases with traffic at an intersection and distinct diurnal and weekly cycles at residential and rooftop sites. These findings demonstrate that low-cost IoT sensing can complement sparse public networks by providing microscale air quality information. This approach offers a practical foundation for smart city development and intelligent roadside environmental management.