The relationship between smart cities and the internet of things in low density regions

In these times of digital transformation, cities have overcome the challenges of the past and are building the future. The use of technological resources as a means of efficiently delivering various services and improving citizens’ quality of life has transformed regions and cities into smart regions and cities. There have been a remarkable amount of projects implemented by the Municipalities in the last years, taking the technologies to the cities. However, for a project to be interesting, it must have a positive impact on society, that is, citizens. This evidence gave rise to the present study whose goal was to find out if citizens living in inner cities, labeled as smart cities, actually consider them that way, and whether their city uses innovative solutions that optimize their daily lives. The results are discussed in the light of the literature and future work is identified with the aim of shedding some light on a field as emerging, promising and current as this of Intelligent Cities and the Internet of Things.info:eu-repo/semantics/publishedVersio

Segmentation of SAR images using similarity ratios for generating and clustering superpixels

The superpixels are groups of similar neighbouring pixels which are perceptually meaningful and representationally efficient segments. Among those existing superpixel generating algorithms, simple linear iterative clustering (SLIC) seems to be one of the simplest ones. Its simplicity is due to adaption of a distance measure which is a linear combination of colour and spatial proximity. It is this measure that is modified using a similarity ratio. This modified measure is used to label the pixels within the search areas for generating the superpixels. This generation phase is further augmented with a clustering phase based on the same formulated similarity metric, which clusters the superpixels into larger segments. It has been demonstrated that this modified version performs better in terms of boundary recall and undersegmentation error, and is more robust to the speckle noise than the one in SLIC. Moreover, the clustered segments formed by superpixels generated by this approach has better boundary adherence than those formed by superpixels generated by SLIC