Data driven channel characterization of human body communication

Intra-body communication (IBC) is a novel research area that will foster personalized medicine by allowing the interconnection of implanted devices through energy-efficient technologies, such as the galvanic coupling (GC) technique considered in this work. Channel characterization of the human body is here evaluated through experimental data considering in-vivo tissues in the frequency range up to 100 kHz. Pseudorandom noise (PN) sequences are transmitted in baseband and a correlative channel sounding system is implemented to evaluate the channel impulse and frequency response through real measurements

Spatio-Temporal Urban Change Mapping With Time-Series SAR Data

The strong urbanization impetus of developing countries leads to various urbanization phenomena such as building constructions, reconstructions, and demolitions. It is desirable to monitor and recognize these intraurban changes by utilizing temporal and spatial information in an automatic way. This may be useful, for example, to timely update urban information databases. The aim of this work is, therefore, to automatically extract first, and further recognize, change time series in sequences of SAR data with high-frequency acquisition. Specifically, SAR time-series segmentation and unsupervised classification are combined together to recognize areas with the same urban change pattern, by fully exploiting both the temporal and spatial dimensions. Experimental results in a fast-growing Chinese city show that the proposed approach is effective and able to characterize temporal patterns due to different kinds of intraurban changes

Urban Change Pattern Exploration of Megacities Using Multitemporal Nighttime Light and Sentinel-1 SAR Data

During the last 20 years, fast urbanization activities have been highly concentrated in just few countries (e.g., China, India, and Nigeria) and have led to the emergence of large urban aggregations, with high population density. Still, very few researches have focused on this dynamic phenomenon with a global perspective using multisource remote sensing data. In this article, combining radar and spectral sensors of different spatial resolution, a novel approach based on a novel hierarchical biclustering technique is proposed and proved to be effective in discriminating the underlying change patterns without pre-estimating the number of clusters. To this aim, experimental results focused on newly emerging megalopolis in China, India, and Nigeria, as well as on the highly urbanized and stable Lombardy region in Italy, are presented. The analysis of the results allows us to understand, in a global and comparative perspective, the spatiotemporal differentiation of urban density and how cities are changing and evolving in the building volume and, to some extent, their economic level