Monitoring of aquatic ecosystems has been historically accomplished by intensive campaigns of direct measurements (by probes and other boat instruments) and indirect extensive methods such as aero-photogrammetry and satellite detection. These measurements characterized the research in the last century, with significant but limited improvements within those technological boundaries. The newest advances in the field of smart devices and increased networking capabilities provided by emerging tools, such as the Internet of Things (IoT), offer increasing opportunities to provide accurate and precise measurements over larger areas. These perspectives also correspond to an increasing need to promptly respond to frequent catastrophic impacts produced by drilling stations and intense transportation activities of dangerous materials over ocean routes. The shape of coastal ecosystems continuously varies due to increasing anthropic activities and climatic changes, aside from touristic activities, industrial impacts, and conservation practices. Smart buoy networks (SBNs), autonomous underwater vehicles (AUVs), and multi-sensor microsystems (MSMs) such as smart cable water (SCW) are able to learn specific patterns of ecological conditions, along with electronic “noses”, permitting them to set innovative low-cost monitoring stations reacting in real time to the signals of marine environments by autonomously adapting their monitoring programs and eventually sending alarm messages to prompt human intervention. These opportunities, according to multimodal scenarios, are dramatically changing both the coastal monitoring operations and the investigations over large oceanic areas by yielding huge amounts of information and partially computing them in order to provide intelligent responses. However, the major effects of these tools on the management of marine environments are still to be realized, and they are likely to become evident in the next decade. In this review, we examined from an ecological perspective the most striking innovations applied by various research groups around the world and analyzed their advantages and limits to depict scenarios of monitoring activities made possible for the next decade.

Air quality in urban areas is a very important topic as it closely affects the health of citizens. Recent studies highlight that the exposure to polluted air can increase the incidence of diseases and deteriorate the quality of life. Hence, it is necessary to develop tools for real-time air quality monitoring, so as to allow appropriate and timely decisions. In this paper, we present uSense, a low-cost cooperative monitoring tool that allows knowing, in real-time, the concentrations of polluting gases in various areas of the city. Specifically, users monitor the areas of their interest by deploying low-cost and low-power sensor nodes. In addition, they can share the collected data following a social networking approach. uSense has been tested through an in-field experimentation performed in different areas of a city. The obtained results are in line with those provided by the local environmental control authority and show that uSense can be profitably used for air quality monitoring.

A 2-D wind sensor, based on microelectromechanical systems (MEMS) flow sensors, is presented. The device consists of a cylinder with a channel network connecting the internal flow sensors with the lateral surface. The pressure distribution developed by the wind on the cylinder's surface is thus converted into two air flows from which, due to the special channel configuration, wind speed and direction can be unequivocally determined. Since the MEMS devices are located in the channels, their fragile sensing structures are protected from direct exposure to the wind. Performance estimation based on literature data of pressure distributions indicates that the approach is applicable to a wide wind velocity range. The feasibility of the idea is supported by measurements performed on a prototype.

A program for the simulation of the nucleation and growth of thin island films on amorphous substrates is presented. The program is based on the description of the phenomenon in terms of adsorption, diffusion, and reevaporation of single atoms on the substrate surface, which is represented as a triangular lattice of adsorption sites. A method of taking into account the interaction with neighboring particles in the evaluation of the diffusion activation barrier is proposed. The simulations are devoted to investigating the role of diffusion and reevaporation in the determination of the particular microstructure of the clusters. The effects of the substrate temperature on the morphology of the simulated island films are also presented. The cluster-size distribution and percolation threshold have been estimated and compared to experimental results available in the literature.

In this work, we propose a method to increase the parasitic input resistance caused by application of chopper modulation to indirect current feedback instrumentation amplifiers. The result is obtained by applying dynamic element matching to the input and feedback ports at the same frequency as chopper modulation. The proposed approach requires effective offset ripple rejection and equalization of the input and feedback common mode voltages. An in-amp architecture that meets both requirements and embodies the proposed input resistance boosting method is described. Experimental verification is provided by means of a prototype designed and fabricated using the 0.32 μm CMOS devices of the STMicroelectronics BCD6s process. The amplifier operates with a 3.3 V supply voltage and a total current absorption of 170 μA. An input impedance in excess of 1 GQ has been measured at a chopper frequency of 20 kHz. The input referred voltage noise density is 18 nV/sqrt(Hz) with a flicker corner of 0.2 Hz and 200 Hz bandwidth.