What Is GEANT?

The world is significant, and you have a lot to do. What's more, you need to get it all done quickly. You must do it fast enough to get home for dinner if you're in Europe. That's where GEANT comes in. GEANT is the Gigabit European Academic Network, and it provides data communication, infrastructure and resources for telecommunication and information technology development across the continent. It's also co-funded by education networks, European national research and the European Commission—and coordinated by DANTE. With the GEANT network, we can do things once thought impossible. A world where researchers in one country can collaborate with those in another—a world where researchers in one lab can share data with researchers in another—is a world we want to live in. The GEANT network has enabled us to accomplish previously impossible things. With its unrivaled geographical coverage and high bandwidth, it's no wonder why so many researchers and educational institutions have benefited from using this network! GEANT's high-speed research network has dramatically helped many research and innovative scientific projects and studies. As it uses routed and switched technology, GEANT is making its way not only for the next generation but also for high-performance and cost-effective communication networks. The benefits don't stop there: as it improves data sharing among researchers from different locations throughout Europe, we're getting closer to fundamentally transforming data collaboration among researchers from other areas throughout Europe—and we've only just begun! Another advantage of GEANT is providing access to remote resources, which are expensive for a single nation to develop. GEANT has a special place in the hearts of scientists and researchers. It's no secret that the number of people using GEANT is up to 30 million, including educational and research institutions. What makes GEANT so popular? Well, it's helped many people in all spheres of study, including radio astronomy observations and medical research.

By TechDogs Editorial Team

Related Terms by Communication And Collaboration Software

Sentiment Analysis

Sentiment analysis is a lot like having the ability to discern minds, except it's done with computers. Opinion mining is a data mining subfield that utilizes unstructured text analysis to gauge consumer sentiment toward a brand, individual, or concept. Sentiment analysis is a technique for gleaning emotional data from online sources using NLP, computational linguistics, and text analysis. Social media sites and other online forums where users post their thoughts and observations on various subjects are familiar places to find this data. Sentiment analysis uses complex algorithms and machine learning methods to identify a person's opinion's positive, negative, or neutral nature. As a bonus, it can determine whether the text is joyful, sad, angry, or anxious, as well as other emotions. The results of this analysis can be used to calculate the extent to which the public approves or disapproves of various brands, individuals, and concepts. Knowing the thoughts and preferences of customers can be invaluable to companies and organizations. A business may employ mood analysis to monitor customer feedback via social media and use the results to improve its offerings. The material's polarity in its context can also be revealed through sentiment analysis. It can tell you how people feel about a subject or entity and what it is about that subject or entity that people like or dislike. Sentiment analysis can show, for instance, that consumers have a generally positive attitude toward a given brand but a negative attitude toward its customer service. To sum up, sentiment analysis is a subfield of data mining that assesses consumer reaction to a brand, individual, or concept by examining written language. It's like having the ability to read thoughts, only this time, and it's accomplished through complex mathematical formulas stored in a computer. Sentiment analysis, or opinion mining, is a method for gleaning and analyzing biased data from online sources, such as social media and blogs. Data analysis can reveal the contextual polarity of information and provide quantitative estimates of the public's feelings or responses to specific goods, people, or ideas.

Self-Provisioning

If you're like most people, you're always looking for ways to get out of work. So when we heard about self-provisioning—the ability to set up services and applications by yourself without the help of a dedicated IT specialist or service provider—we were all over it. It's like having your server, except that instead of having to buy your server, pay for its maintenance, and hire an IT person to manage it when things go wrong, you sign up with a cloud provider who has already done everything for you. Moreover, they'll even let you use their servers for free! So if you have ever wanted to launch your website but didn't want to take on the burden of managing it yourself, or if you've been dreaming of starting an online business but didn't want to spend all that money on servers and software licenses well, now's your chance! Self-provisioning is excellent, but the self-de-provisioning part is even more significant. Provisioning is like getting a massage—you know what you want and are in charge of getting it. Deprovisioning is like getting a haircut—it's a little more complicated than telling someone what to do. It requires much attention to detail and technical skill to ensure you're not cutting off any substantial parts of yourself in your zeal to be smooth and sleek. We don't want you to be soft and elegant! We want you to be well-groomed! So here are some tips for taking care of yourself by taking care of your resources. Always deprovision after using a resource so that others can use it when they need it later. Only do something once you've found another that does what that other one did for you (and then de-provision the old one).

Secure Hash Algorithm (SHA)

Secure Hash Algorithm is a set of algorithms developed by the National Institutes of Standards and Technology and other government and private parties. Cryptographic hashes (or checksums) have been used for electronic signatures and file integrity for decades. However, these functions have evolved to address some of the cybersecurity challenges of the 21st century. The NIST has developed a set of secure hashing algorithms that act as a global framework for encryption and data management systems. The initial instance of the Secure hash Algorithm (SHA) was in 1993. It was a 16-bit hashing algorithm and is known as SHA-0. The successor to SHA-0, SHA-1, was released in 1995 and featured 32-bit hashing. Eventually, the next version of SHA was developed in 2002, and it is known as SHA-2. SHA-2 differs from its predecessors because it can generate hashes of different sizes. The whole family of secure hash algorithms goes by the name SHA. SHA-3, or Keccak or KECCAK, is a family of cryptographic hash functions designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. SHA-3 competition to develop a new secure hash algorithm was held by the United States National Security Agency (NSA) in 2007. To be a super safe and fast hashing algorithm, SHA3 was developed from this contest. The evolution of cybersecurity has led to the development of several "secure hash algorithms." Security is a crucial concern for businesses and individuals in today's digital world. As a result, many types of encryption have been developed to protect data in various scenarios. One of these is hash algorithms. All secure hash algorithms are part of new encryption standards to keep sensitive data safe and prevent different types of attacks. These algorithms use advanced mathematical formulas so that anyone who tries to decode them will get an error message that they aren't expected in regular operation.

Trending Definitions

High-Speed Data Acquisition (High-Speed DAQ)

Let's have a chat on #HighSpeedDAQ or High-Speed Data Acquisition. Consider this the Superman of data collection; it can amass vast volumes of data in a fraction of a second. Picture a high-speed camera that captures a racing automobile in action. High-Speed DAQ can rapidly collect a large amount of data from various sources, just like the camera can quickly capture photographs of a passing car. Now, let's say that a "regular speed" data-gathering system can process a specific number of bytes per second. But what about Fast DAQ? The best way to describe it is that it stands alone. It can process information at a rate of MSPS (millions of samples per second) or even GPS (billions of samples per second) (GSPS). Try to quench your thirst with a fire hose. If we don't need to go that fast, then there's no reason to get equipment that can manage it. There is, in fact, a wide variety of contexts in which such rapid processing is desirable. For instance, high-frequency transmissions may require data collection in research and engineering. An ordinary data-collecting system would need to be improved in these scenarios. High-Speed DAQ is very useful in signal processing, among other areas. Quick data collection is essential for analyzing a system's behavior over time. Finally, High-Speed DAQ is utilized in medical imaging, where speed is crucial to building a detailed image of inside organs; for instance, when capturing data for a picture of a beating heart, we only have milliseconds. Where exactly does the magic happen in High-Speed DAQ? To swiftly transform analog signals into digital data that a computer can process, this technology frequently employs specialized hardware such as #AnalogToDigitalConverters (ADC) and #FieldProgrammableGateArrays (FPGA). The rate at which data may be gathered is increasing due to technological development. Fast-Speed DAQ is a data acquisition system that operates at high speeds. It's a crucial tool for a wide variety of uses because we can quickly collect vast volumes of data. We'd be collecting data at a snail's pace without it. However, please keep in mind that this is just a high-level overview and that High-Speed DAQ can have a wide variety of meanings based on the application and a wide variety of concrete forms depending on the details of the system.

Remote Sensing

Remote sensing is a way of getting information about something without touching it. It's like peeking through a window, but instead of using your eyes, you're using a satellite. That's pretty cool! Remote sensing is acquiring information about an object or phenomenon without physical contact, as opposed to onsite observation or onsite sensing. It requires using aerial sensor technologies such as those used in reconnaissance airplanes and satellites to detect and analyze objects on the Earth, usually on the surface. The earliest known uses of aerial remote sensing technology were with kites in China around 400 BC. These kites would often be used to monitor weather patterns or find lost children. However, these devices needed to provide details about what they saw above. The first use of aerial remote sensing devices occurred during World War II when airplanes were sent up into the air over enemy territory to collect information about troop movements, supply caches, and other factors that could help combat operations. Since then, aerial remote sensing has become a significant part of military operations worldwide and plays an essential role in national defense today. Remote sensing is a rad technology that lets us see the world from a bird's eye view. We're not talking about a drone or something like that. We're talking about satellites, planes, and other aerial technologies using advanced sensor technologies to detect energy reflected from the Earth's surface. It makes it possible to collect data in inaccessible or dangerous areas where it would be too hazardous to bring in a team and equipment. Since the sensors are high above the Earth, they are beneficial for gathering data in an extensive area. Applications of remote sensing include monitoring the extent of deforestation or the spread of an oil spill and monitoring the movement of and changes in polar ice caps and icebergs. The same principle is applied to a ship's depth sounding of oceanic and coastal depths.

What Is GEANT?

Related Terms by Communication And Collaboration Software

Sentiment Analysis

Self-Provisioning

Secure Hash Algorithm (SHA)

Trending Definitions

High-Speed Data Acquisition (High-Speed DAQ)

DumpSec

Remote Sensing