This paper presents a working system to prove as a solution that makes the user aware of the conditions of the road. The system is designed based on principles of Internet of Things to detect and identify the road irregularities in real-time. Road irregularity is a broad term which includes any potholes, cracks or random deviations that exist. Existing method measuring the irregularity is by visual interpretation. The system consists of accelerometers or tilts switches to measure the irregularity and also provides a real time data collection via a two way wireless communication and reuse of the same. It also has a global positioning system interface to monitor the coordinates of the irregularity and map the index of irregularity on a digital map via a cloud based service. The cloud service performs transformations on the data received simultaneously by all the devices on the ground. The resulting information about any forthcoming irregularities/obstacles is then disseminated to the consumers through existing mapping applications. Thus providing a feedback for the user to know the condition of the road and plan the safety measures or change the route direction all in real-time. The System adds great value to the existing mapping technology but making it more location aware. While the estimated results are approximate but the study provides a transfer function relating the inertia of the vehicle to that of the road profile. The study also provides the scope to maintain and analyze the road the conditions by the local administrative body.
No abstract
Cyclone vortex localization under varying conditions of saturated spiral bands is challenging. This paper presents a unique combination of image processing techniques, viz., Sequential Cross-Correlation (SCC) and Multi-Level Thresholding (MLT) for vortex localization. SCC is used for cyclone detection in a full-disk satellite imagery, and is based on the high degree of correlation in the sequence of cyclone stages. MLT is used for vortex localization in the detected Tropical Cyclone (TC), and is based on Tsallis entropy and particle swarm optimization (PSO). These consider unimodal distribution of the pixel intensity and non-extensive nature of cyclone for image segmentation. The vortex co-ordinates thus obtained will be the authentic estimate of the TC's vortex and is further used for the TC tracking. The proposed algorithm is applied on the full-disk visible and infrared (IR) imagery of size 744 × 676 obtained from Geostationary Operational Environmental Satellites, namely GOES-12 and 13 and the experimental results indicate that proposed algorithm efficiently tracks the TC with the best average Euclidean distance error of 23 per TC.
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