Did you know …….?
Smart Cities go hand in hand with ICT and IoT.
A smart city needs Information and Communication Technologies (ICT) and an intelligent network of connected objects that transmit data in real time (IoT). This data is analyzed to make decisions that improve the quality of life and sustainability. How does this network really work…?
Smart City citizens interact daily with their city’s ecosystems through the use of smartphones and mobile devices. This interconnectedness reduces costs and improves sustainability, from efficient energy distribution to optimizing air quality.
Environmental, social and economic sustainability is vital to keep pace with the population growth of cities that are challenging available resources. Nearly 200 countries have agreed to the Sustainable Development Goals (SDGs) and smart city technology is paramount to their fulfillment.
There are four essentials for the success of Smart Cities:
1. pervasive wireless connectivity – In addition to low power wide area network (LPWAN) technologies, 5G technology will be critical to the expansion of smart cities.
2. Open data -The ecosystem will allow information to be shared and combined with data that is analyzed in real time for decision making. However, the openness of the Data vault increases the possibility of cyber-attacks.
Security – All players in the ecosystem (governments, companies, software providers, etc.) must integrate authentication solutions that guarantee data sharing.
4. Flexible monetization schemes – Subscription-based models enable sustainable commerce by providing a way to monetize the hardware and software used to build smart infrastructures and spread the costs.
In short, Smart Cities technology is expanding to improve sustainability. With pervasive connectivity, open data, security and software monetization solutions, the varying needs of smart cities are being addressed, improving the experience for all players in the ecosystem.
Cloudprint complies with the most stringent security and data protection regulations, as well as not being intrusive or leaving access ports open unnecessarily, preventing cyber-attacks. It has also adopted a combination of usage-based and subscription-based models, known as usage-based subscriptions. Customers pay a fixed monthly fee with no commitment of permanence for a minimum configuration (for an additional charge, more apps can be flexibly added ). In terms of sustainability, Nubeprint promotes the circular economy, with A.I. applied to optimizing resources and reducing waste, complying with the SDGs.
Sources: thalesgroup.com/ Nubeprint
Did you know …….?
Nubeprint has an extensive collector ecosystem.
On the seabed of the North Sea off the coast of Denmark, the first CO2 reservoir has been established that will store this unwanted gas produced from the manufacturing process. The process is simple and efficient: in the factory itself that generates it, the CO2 is converted to liquid form for transport, via heating, then compressing and finally cooling the CO2. Is it a complete process or is something missing…?
The result is CO2 in liquid form: easy to transport and store. It is transported by gas tankers from the coast to the platform of a former oil well, now exhausted. It is then injected into the subsoil to occupy the place where, until not so long ago, the black gold lay.
We said that everything is in place to make this practice commonplace in order to neutralize CO2 emissions. We correct: the sensors are missing. In a process of this scale, which involves different industries, countries and legislations, it is critical to have information on each phase (how much CO2 an industry emits, how much is captured, etc.).
In the printing industry, we were part of the revolution that involved creating the first sensor capable of capturing data on the use and status of printers installed remotely. This sensor, later baptized DCA (Data Collection Agent), gave rise to a new business: the MPS (Nubeprint has a lot to say because its engineers and founders were the ones who dreamed of this technology and made it a reality with the first patent in 2000).
But, as with CO2 capture and storage, in printing a single sensor is not enough, quite the contrary. As the sensor is the secure connection between the source of the data (which is located next to the printer user) and the place where the data is to be used (the supplier of the printing products), it is very unlikely that a single sensor can be adapted to the requirements of each and every one of the printing customers (the requirements of a bank with thousands of printers are not the same as those of a dentist’s practice with a single printer).
For all these reasons, Nubeprint has developed the concept of a data collector ecosystem. This is a whole library of DCAs that have the same purpose: to read the printer status and usage data and transport it in the most convenient way to the place where it needs to be used.
Currently, Nubeprint has DCAs for LAN, WIFI and USB connected printers; laser and inkjet printers and multifunctional equipment; Ribbon, thermal, large format and 3D printers; even DCAs for scanners; also DCAs for PCs, servers, tablets, smartphones, smart TVs and Raspberry.
We are aware that our developments position our customers competitively and that they are also their gateway and the basis for the expansion of new business. For this reason, Nubeprint never stops researching, dedicating year after year 30% of its turnover to R+D+I activities.
Sources: Nubeprint.com
Did you know …….?
AI searches for extraterrestrial signals.
The SETI Institute has been searching for intelligent life beyond Earth since the 1960s. Recently, it has implemented a series of machine learning algorithms to filter the Big Data generated from telescope observations. Is there life in “a Galaxy far, far away…”?
AI is all the rage: from Chat GPT to the recent petition by 1,000 scientists calling for a 6-month waiting period for AI, which is going too fast. Artificial Intelligence is here to stay and is capable of solving in seconds what would take a human being days, months or even years of effort and dedication.
The SETI Institute has developed a system of machine learning algorithms, based on AI, that filters out interference from terrestrial signals and is able to detect unknown space signals. This AI system also aids in the analysis of Big Data obtained from telescope searches and, perhaps, in finding extraterrestrial life.
Since 2015, SETI has been searching for signs of intelligent life in a million stars through observations from telescopes installed in Virginia (USA), Australia and South Africa. The project aims to capture radio emissions coming from the direction of a star and constantly changing frequency (just as would happen if an extraterrestrial transmitter were on a planet moving relative to Earth).
Machine learning software was built to analyze the data from observations of 820 stars. Nearly three million signals were captured, but the vast majority were discarded as terrestrial interference. Then, about 20,000 others were manually reviewed and the hope was reduced to 8 signals of unknown origin.
Ultimately, the search was unsuccessful: all 8 signals disappeared the second time the team searched for them. However, this method could be used to analyze more Big Data (such as observations from the MeerKAT array of 64 radio telescopes in South Africa), plus the algorithms could also filter archived data to look for signals that might have been missed.
Nubeprint has a managed MPS solution with dynamic algorithms and filters.
In 2013, it developed the first A.I. engine for MPS and, since 2017, it has a Machine Learning (ML) developed specifically for MPS: through this machine learning, the system develops pattern recognition and the ability to learn continuously, with predictions based on Big Data, after which it makes the necessary adjustments without having been specifically programmed to do so.
Source: abc.es/science/Nubeprint
The SETI Institute has developed a system of machine learning algorithms, based on AI, that filters out interference from terrestrial signals and is able to detect unknown space signals. This AI system also aids in the analysis of Big Data obtained from telescope searches and, perhaps, in finding extraterrestrial life.
Since 2015, SETI has been searching for signs of intelligent life in a million stars through observations from telescopes installed in Virginia (USA), Australia and South Africa. The project aims to capture radio emissions coming from the direction of a star and constantly changing frequency (just as would happen if an extraterrestrial transmitter were on a planet moving relative to Earth).
Machine learning software was built to analyze the data from observations of 820 stars. Nearly three million signals were captured, but the vast majority were discarded as terrestrial interference. Then, about 20,000 others were manually reviewed and the hope was reduced to 8 signals of unknown origin.
Ultimately, the search was unsuccessful: all 8 signals disappeared the second time the team searched for them. However, this method could be used to analyze more Big Data (such as observations from the MeerKAT array of 64 radio telescopes in South Africa), plus the algorithms could also filter archived data to look for signals that might have been missed.
Nubeprint has a managed MPS solution with dynamic algorithms and filters.
In 2013, it developed the first A.I. engine for MPS and, since 2017, it has a Machine Learning (ML) developed specifically for MPS: through this machine learning, the system develops pattern recognition and the ability to learn continuously, with predictions based on Big Data, after which it makes the necessary adjustments without having been specifically programmed to do so.
Source: abc.es/science/Nubeprint
Ultimately, the search was unsuccessful: all 8 signals disappeared the second time the team searched for them. However, this method could be used to analyze more Big Data (such as observations from the MeerKAT array of 64 radio telescopes in South Africa), plus the algorithms could also filter archived data to look for signals that might have been missed.
Nubeprint has a managed MPS solution with dynamic algorithms and filters.
In 2013, it developed the first A.I. engine for MPS and, since 2017, it has a Machine Learning (ML) developed specifically for MPS: through this machine learning, the system develops pattern recognition and the ability to learn continuously, with predictions based on Big Data, after which it makes the necessary adjustments without having been specifically programmed to do so.
Source: abc.es/science/Nubeprint
In 2013, it developed the first A.I. engine for MPS and, since 2017, it has a Machine Learning (ML) developed specifically for MPS: through this machine learning, the system develops pattern recognition and the ability to learn continuously, with predictions based on Big Data, after which it makes the necessary adjustments without having been specifically programmed to do so.