5G Technology

Manodya Wickramasinghe | Undergraduate | University of Sri Jayewardhanepura

Communication has evolved and continues to do so, from fire to fibre optics cables, over the past decades. To make wireless communication possible, cellular networks need to be developed. This was started in Japan in 1979, with the first generation of cellular networks. It is called 1G. At its best 1G was capable of about 2.4kilobits per second transfer rate. With the introduction of a fully digital system, 2G started a new era of mobile phones. At first, 2G could achieve about 9.6kbits per second transfer rate. But with the launch of the first iPhone, the 2G era has ended. It increased the transfer rate to 384 kilobits per second. It is sometimes referred to as 2.5G. 3G was introduced by the time iPhone 2 launched. With the introduction of 3G, the communication system has changed as the fully utilized method, data packet switching was functioning. In 2005, High-Speed Packet Access (HSPA) (represented on our phones as H+) was introduced. It has a transfer rate of 14.4 Megabits per second. It is called 3.5G. In 4G a new technology called Long Term Evolution has been introduced. It has a peak download speed of 50 Megabits per second. Because of superfast broadband, high reliability and efficient energy usage, 5G is introduced with 20 Gigabits per second of peak data rate and 100+ Megabits per second average data rates.

5G is a new global wireless standard that was introduced after all the generations. 5G also uses massive MIMO (multiple input multiple outputs). 5G is also looking to use beamforming technology. It will allow the antenna to aim at phones instead of broadcasting the signal in all directions. This leads the data transfer rate of 5G to be high. 5G will allow higher download speeds and lower latency. OFDM(Orthogonal Frequency-division Multiplexing) is a method that modulates a digital signal across several different channels to reduce interference. 5G is based on that technology. Also, 5G uses wider bandwidth technologies. 5G is introduced not only for faster delivery and better mobile broadband services but also to connect the massive IoT and to expand into new service areas such as mission-critical communications. When comparing the 5G technology with 4G, it is significantly faster than 4G. It is clear as the data transfer rate of 5G is way higher than 4G. As 5G supports a 100 times increase in traffic capacity and network efficiency, 5G has more capacity than 4G. 4G has higher latency than 5G.4G latency ranges from 60 milliseconds to 98 milliseconds while the latency of 5G lies under 5 milliseconds. Also, the spectrum that is used by 5G is better than 4G. When comparing 4G with 5G, 5G is a unified platform that is more capable than 4G.

When considering the advantages of 5G technology, the main advantage is the greater speed of transmission. With the use of 5G, speed transmission can approach 15 or 20 Gigabits per second. The elapsed time between giving an order and action occurring is considered the latency. 5G has a lower latency which is ten times less than 4G. With the introduction of 5G technology, the number of devices connected to a computer network also increases. If the number of devices is increased, all the devices have an instant connection to the internet. 5G is capable of network slicing. It allows the creation of subnets, to implement virtual networks to provide connectivity more adjusted to specific needs. There are some insane theories about 5G technology. It is said that 5G technology is weakening the immune system and causing a global pandemic. Also, some people believe that 5G causes cancer and it is mind-controlled by lizard people. Anyone with little knowledge of technology will find these things to be false. However, as with any new technology, 5G also has some disadvantages to be considered. It has a high initial cost for rollout. Development of 5G infrastructure will cost a lot. Those who live in rural areas will not necessarily benefit from the 5G connection. So, limitations of rural access are another drawback of 5G technology. Batteries of cellular devices are not capable of operating for a significant time when the device is connected to 5G. So, it drains the battery. The upload speed of 5G technology is not as incredible as the download speed. Although the download speed is high, the upload speed is low. With 5G technology, infrastructure development is going to be increased and because of that, the overall look and feel of an area are going to be diminished. Even though the 5G technology has some disadvantages, it has more advantages than other generations. With high speed, high reliability and lower latency, 5G has become more important in the communication sector. Let’s take the maximum benefits of 5G technology, as it enables us to connect the entire universe in an instant.

Intelligent Automation

Thamayanthi mahendranathan| Undergraduate | University of Peradeniya

Intelligent automation is the combination of artificial intelligence (including natural language processing, machine learning, autonomics and machine vision) and automation. As a result, Intelligent Automation is defined as the fusion of these “smart” technologies with automated processes to improve business process automation.

Natural Language Processing: Natural language processing (NLP) is an Artificial Intelligence (AI) subfield (AI). This is a widely used technology for personal assistants in a variety of fields and industries. This technology analyses the user’s speech, breaks it down for proper comprehension, and processes it accordingly. This is a relatively new and effective approach, as a result of which it is in high demand in today’s market. Natural Language Processing is a new field that has already seen many advancements, such as compatibility with smart devices and interactive human conversations. AI applications in NLP focus on knowledge representation, logical reasoning, and constraint satisfaction. It was first applied to semantics and then to grammar in this case. The widespread use of statistical approaches such as machine learning and data mining on a massive scale has resulted in a significant change in NLP research over the last decade. Because of the amount of work that needs to be done these days, the need for automation is never-ending. When it comes to automated applications, NLP is a very beneficial aspect. NLP’s applications have made it one of the most in-demand methods for implementing machine learning. Natural Language Processing (NLP) is a field that studies how computers and humans communicate in natural language by combining computer science, linguistics, and machine learning. The goal of natural language processing (NLP) is for computers to be able to understand and generate human language. This isn’t just for show.

Machine learning: Machine learning is a crucial part of the rapidly expanding field of data science. Algorithms are trained to make classifications or predictions using statistical methods, revealing key insights in data mining projects. Following that, these insights drive decision-making within applications and businesses, with the goal of influencing key growth metrics. As big data expands and grows, the demand for data scientists will rise, necessitating their assistance in identifying the most relevant business questions and, as a result, the data needed to answer them.

Machine vision: Machine vision is a tool used in the industrial field to control machines autonomously. Computer vision is a type of technology that allows images to be processed and understood. Consider an industrial robot that is specifically equipped and programmed

to detect faulty products on a manufacturing line. While computer vision focuses on the algorithms that identify visual defects, a machine vision system encompasses the entire system that both detects and eliminates defects from the manufacturing process.

Automation: Electronics and computer-controlled devices are being used to replace manual operations.

Artificial intelligence and automation are not new concepts, but they have advanced significantly in recent years. Machine learning techniques, sensor improvements, and ever-increasing computing power have all contributed to the development of a new generation of hardware and software robots with practical applications in nearly every industry sector.

Intelligent automation systems detect and generate massive amounts of data, and they can automate entire processes or workflows while learning and adapting as they go. From collecting, analysing, and making decisions about textual information to guiding autonomous vehicles and advanced robots, applications range from routine to revolutionary. It is already assisting businesses in going beyond traditional performance tradeoffs to achieve unprecedented levels of efficiency and quality.

lately, most robotics applications were found in the primary sector, automating and removing the human element from the production chain. Its first foray was into replacing menial tasks, and many organizations integrated robotics into their assembly line, warehouse, and cargo bay operations. Businesses in the tertiary sector are already using new technologies and the robotic paradigm to automate processes and replace humans in low-value-added tasks.

We can gain the following advantages from using Intelligent Automation,

increase process efficiency (Increase the speed of your processes while maintaining a high level of quality).
Make the most of bots (Rather than relying on RPA (Robotic Process Automation) bots to complete tasks on their own, combine them with other technologies to achieve intelligent automation).
Release employees from repetitive tasks (With intelligent technologies assisting in making informed decisions, you can be sure that routine tasks are being completed fully, effectively, and error-free).
Interpret Big Data (Large amounts of data that would take humans a long time to manage can be handled and interpreted by intelligent automation).
Reduce costs (Automating tasks can save the equivalent of one or more full-time employees, who can then devote their time to higher-value tasks or be deployed elsewhere in the company).
Improve governance and fraud detection (You can ensure that governance is followed to the letter, and intelligent systems can detect and prevent fraud by identifying suspicious activity).

Intelligent automation can benefit businesses, particularly in processes where data needs to be managed, moved from one part of the business to another, or presented to a client, such as automating customer requests. These tasks would take much longer and be more prone to errors if they were not automated intelligently.

These technologies are becoming more widely available as open-source or low-cost products, as well as cloud-based services. However, they can only replace humans in tasks that are not central to the processing platform. If they decide to replace it, they should carefully choose a suitable solution that can be easily integrated using a service layer. They will have to adapt the processes to the new system rather than the new system to the old way of working to get the most out of the new system in terms of automation. if they choose to overhaul it, they should componentize the architecture, factorize the elements, review the process, isolate, simplify, and reduce the transactional platform to its core, and introduce a secure yet high-performance service layer to integrate it with peripheral systems and IA technologies.

IA technologies can and will be used in a variety of process areas, but there will almost certainly be domains where software cannot replace humans, such as deal structuring, where a significant amount of creativity or intelligence is required. When regulations mandate that a human is in charge of the financial review process, IA can assist but not replace humans. Obviously, it will not completely replace humans, as customers will flee if they can only interact with machines – or perhaps it is just a matter of attitudes and time.

Conclusion:

Artificial intelligence-based automation is made possible by a combination of new types of software and recent advances in computing power.
Technologies such as machine vision, speech recognition, natural language processing, machine learning, and autonomics can be combined to automate processes by interpreting facts, making decisions, and adapting to change.
These technologies are still in the early stages of development, but they are already capable of replacing humans in a variety of tasks.
To get the most out of this technology, legacy core platforms will almost certainly need to be overhauled or replaced from the ground up.

Robotic Process Automation

Jayana Gunaweera | Undergraduate | Informatics Institute of Technology

Robotic Process Automation (RPA) is an advanced technology that builds an intelligent software robot that can emulate human interactions with a business process. RPA does not use machines, but they implement so-called software bots. They do not perform physical work as physical robots in the industry, but control digital processes by imitating work steps on a user interface. RPA bots are capable of logging into various applications and systems and can then perform rule-based routine operations such as filling out forms, moving files or extracting data from documents, reading a PDF file, scanning it for data, and sending the data somewhere else which can be useful for the management of supply chains and fully automate entire business processes. For the past decade there’s been growing discussion on automation and many leading companies are enthusiastically seizing the latest devices utilizing RPA technology to automate thousands of processes and tasks.

RPA has evolved from three main technologies called screen scraping, workflow automation and artificial intelligence. Screen scraping is the act of copying information that shows on a digital display so it can be used for another purpose. Workflows can help streamline and automate repeatable business tasks, minimizing room for errors and increasing overall efficiency. Artificial intelligence involves the ability of computer systems to perform tasks that require human intervention and intelligence.

What makes RPA so appealing is its efficiency and propensity for producing positive business outcomes. There are lots of advantages in using RPA.

A well-calibrated RPA can perform any repetitive task that would otherwise be left to a human automatically.
An employee who’s performing a task that an RPA could handle isn’t doing something else that’s potentially more productive.
An RPA robot can perform repetitive tasks over and over, much faster than a human, and it won’t get tired. If your productivity is being bottlenecked by a certain task taking too long, RPA is one way to improve this problem.

Though there are advantages to RPA, there are some significant drawbacks.

It can become a serious decoy from the necessary long-term work needed to digitize and make processes and administrative work more efficient. There is a risk that you may focus on quick fixes rather than doing things the correct way from the start.
RPA bots can’t detect some obvious errors that a human would be able to immediately point out. If your data has problems with it, RPA robots will not call it out but pass it on, magnifying an error that might have otherwise been caught.
Some problems aren’t a good fit for RPA, especially when the stakes are high. For example, if you need to handle your purchase invoices, it’s likely a better idea to use software that can understand and manage the data correctly from the start.

RPA software is called RPA vendors. UiPath is leading the “automation first” era championing a robot for every person and enabling robots to learn new skills through AI and machine learning. The IBM Robotic Process Automation offering helps you automate more business and IT processes at scale with the ease and speed of traditional RPA. SS&C’s Blue Prism is a global leader in enterprise RPA and intelligent automation, transforming the way work is done.

RPA will be one of the pioneering technologies utilized in innovations. By making room for innovation, it will make an impact well beyond faster and more efficient business processes which accomplish the aim of advancing technology for humanity.

Big Data and Analytics

M.N.Nishad Ahamed & M.J. Fathima Hana | Undergraduates | University of Moratuwa

In recent years, technology has broken its barriers and advanced in tremendous ways where the evolution of technology to date is unimaginable. Big data analytics has gained a unique place in this development journey globally. The growth in popularity of Big data and its usage appears to have no end. Since its introduction, big data has contributed to the digital transformation of our world, which has helped improve various fields.

In simple terms, data is defined as unprocessed data capable of being stored and transmitted in the form of electronic signals. Unfortunately, today, we have to look at alternatives other than data due to the increased necessity to store and process more than terabytes of data daily. To address this problem, big data is adopted nowadays in Amazon, Facebook, Spotify, and many more. Let us dive deep into big data to get a better image.

Data that is impossible to process, store, or analyse using traditional methodologies is generally referred to as Big Data. The National Institute of Standards and technology report defines Big Data as follows,

“Extensive datasets primarily in the characteristics of volume, velocity, and variability that require a scalable architecture for efficient storage, manipulation, and analysis.”

Moreover, Volume, Velocity, and Variety, or in other words, 3V, forms the foundation of Big Data which gained momentum in the early 20s. With the rapid industrial evolution, Big data comes in handy as it combines all 3 data types:

structured, unstructured, and semi-structured. In addition to the 3Vs defined, we can further categorize big data as 5Vs to include Value and Veracity.

Suppose we put the term big data analytics into a simple and understandable form that conveys the idea. In that case, Big data analytics uses advanced analytic techniques against large, diverse big data sets. Data for analysis include structured, semi-structured, and unstructured data, from different sources and of various sizes, from terabytes to zettabytes, to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences, and arrive at meaningful conclusions.

In the early stages, this was only possible for huge organizations which stored large numbers of data in an organized manner. But now, with the growth of cloud computing, whoever is in need can use Big data via cloud computing technologies. Vendors, such as Amazon Web Services (AWS), Google, and Microsoft, have made it easier to access and benefit everyone.

We can take advantage of big data analytics in four significant areas. It will help in analysing and predicting future risks and avoiding uncertainties. It will help in product development and research where we can understand different patterns and will be able to predict the approximate output without spending millions on them. Moreover, while making business decisions and forming strategies, big data analytics will come in handy to arrive at the most appropriate conclusion. Last but not least, in this digital era where people prefer window shopping and online purchases, big data can help us take an extra step to ensure the customer’s eternal satisfaction and provide them with pleasant treatment. In such ways, big data analytics can significantly support industries and businesses to grow and avoid risks.

The rise of Big data has surprisingly become a critical turning point in data analysis history due to the capability to analyse massive data sets. Hopefully, Big Data and AI will be the next industrial era.