Solid State Nuclear Magnetic Resonance spectroscopy (SSNMR) is today one of the most powerful techniques for characterizing solid and soft materials and systems. This spectroscopy allows the detailed characterization of structural and dynamic properties over large spatial (0.1-100 nm) and time (102-10-11 s) scales. Accessing these properties allows a deep knowledge of a material to be obtained and its design and optimization to be oriented.
Technologies
In this section it is possible to view, also through targeted research, the technologies inserted in the PROMO-TT Database. For further information on the technologies and to contact the CNR Research Teams who developed them, it is necessary to contact the Project Manager (see the references at the bottom of each record card).
Displaying results 1 - 4 of 4
The prototype uses soil moisture sensors which, through a measurement of dielectric permittivity, estimate the soil moisture based on which irrigation is started through relay-controlled solenoid valve. The system was developed using Open Source technologies. Specifically, for the hardware components, a small sized board computer Raspberry PI 3B + was used together with a 4G LTE Wi-Fi router and a Modbus rs485 / USB converter.
Network structures that require the use of a common database are affected by the risk of processing identification data that are necessary for sharing information and updating and processing data with equal access level between the network nodes. However, this sharing could lead risks of vulnerability when identification data are exchanged between the nodes of the network. The proposed information system involves the exchange of information by encrypting the identification data with an MD5 Hashing procedure (RFC1321).
This form describes a programmable, autonomous and stand-alone imaging system for the acquisition and processing of images containing subjects whose size is larger than 1cm (e.g. gelatinous zooplankton, fishes, litter, manufacts), form the seafloor or along the water column, in shallow or deep waters. It is capable to recognize and classify the image content through pattern recognition algorithms that combine computer vision and artificial intelligence methodologies.