TechnoWhiz, New Delhi (2026)

04/04/2019

Program details of TECHNOWHIZ 2019

04/04/2019

“We expect 5G to become the worldwide dominating mobile communications standard of the next decade.”

VINAV AHUJA of BCA 4th Semester give his point of view on 5G MOBILE COMPUTING.
Wanna to know How 5G performance targets high data rate?

Must read this article for your answers.

5G MOBILE COMPUTING

5G, the latest generation of mobile communications after 4G will definitely change mobile computing as we know it as 5G performance targets high data rate, reduced latency, energy saving, cost reduction, higher system capacity and massive device connectivity, while also being a big push for cloud systems at the back-end. As it is, the sending of 5G remote systems will offer ascent to Mobile Edge Computing (MEC) activities that will empower conveyance of much further developed applications. For example, Intel expects data transmission rates of as much as 10 GB/s to be accessible to portable registering gadgets.
That is quick enough to help both enlarged and computer generated reality applications that are sent at the edge of a specialist co-op's system, while will eventually contribute to high speed standards as expected of 5G.
Also, the statistical surveying firm O**m predicts that there will be 24 million 5G memberships for remote administrations set up by 2021. One of the primary changes in the remote systems will be in respect to the cell base stations.

As a component of these MEC activities, administrators intend to empower applications to keep running at the edge of the system, or at the cell base station. This will diminish the system inertness which occurs when an end client gets to an application. That opportunity to get to the application and to run it is drastically decreased in light of the fact that the application will physically run nearer to the client. Applications that will make utilization of MEC stages cover everything from enlarged-reality and ongoing video to area-based administrations and Internet of Things (IoT).
MEC stages, nonetheless, will lead to a challenge for the administrators of the above-mentioned base stations. In addition to the fact that they have to distribute APIs and programming advancement packs that engineers can summon to make applications that will keep running on these stages, they have to put resources into the board and arrangement structures.

There's a race between customary transporters thus brought by the over-the-top suppliers that use systems made by Google and Amazon Web Services (AWS) to conquer this obstacle. While access to the backend IT framework in the cloud will be significant, the fight itself will be won and lost at the edge of the system. Making the system capable of using the backend foundation requires more knowledge to be put at the edge of the system while also utilizing entryways that give access to nearby capacity as close as conceivable to the point of utilization by the end-side client. Those applications thusly would then be able to use back-end cloud assets to get to information as required, something very typical for 5G.The up and coming age of portable applications are now ready to change the way clients interact with their general surroundings. In any case, none of that will be conceivable without first evaluating the advances being made at the edge of the system that will make those encounters conceivable in any case.

WRITTEN BY:
VINAV AHUJA
BCA II YEAR II SHIFT

03/04/2019

"Because, 5G and Mobile Edge Computing - together may change the world as we know it !"
Kirti Bhardwaj of BCA 4th Semester (1st Shift) gives way to a detailed discussion on 5G and it's symbiotic relation with MEC.
Her perspective towards the topic is very strongly showcased in the article given below. Read more to find out and give your opinions on the same.

A detailed discussion on 5G, MEC, speculations,
specifications, and future implications…
With the beginning of a new year come hopes, possibilities and expectations for a better world. And, this time around, we are headed to achieve what innovators and visionaries have always referred to as the future of mobile and networking technology.
Soon 4G (LTE/WiMax), 3G (UMTS) and 2G (GSM) systems will become mere names in mobile computing history, giving rise to 5G computing systems and networks leading to what can only be called the big bang of mobile computing technology.
Keeping automation, edge computing, low latency at its fore-front, 5G - the fifth generation of cellular mobile communications, will provide enhanced throughput making it the next enterprise trend. Service continuity in trains, sparse and dense areas while supporting machine to machine devices will be also made possible through 5G. But then the million dollar question is how 5G will achieve all this and more? The answer to this is closely entwined with 5G’s association with MEC or Multi-access Edge Computing (MEC) - a form of network architecture that enables cloud computing to be done at the edge of a mobile network. As a matter of fact, according to the European Telecommunications Standards Institute (ETSI) - MEC represents key technology and architectural concept to enable the evolution to 5G.
While 4G gave the foundation for 5G, this emerging technology couldn't be more different.
Where 4G required large, high-power cell towers to radiate signals over protracted distances, 5G wireless signals will be transmitted via large numbers of small cell stations using new air interface (radio frequency portion of the circuit between the mobile device and the active base station) called the New radio (NR), that is being built from the ground up in order to support the new developments encompassing 5G which will further enable higher speed and more capacity.
With current 4G network being pushed to their consummate limits, and with the number of connected devices set to reach 100 billion by 2025, 5G is going to manage online traffic far more intelligently and MEC will be the key to that.

The primary benefit of shifting and spreading the significant load of cloud computing with MEC is to reduce congestion on our mobile networks. In addition to managing the data load, MEC will play an enormous part in decreasing latency for 5G networks. Wi-Fi levels of receptivity (1ms, which is 30 to 50 times more responsive than 4G) are a major part of the 5G package.
However, the ‘full’ 5G System includes eMBB (enhanced Mobile Broadband) that provides greater data-bandwidth complemented by moderate latency improvements on both 5G NR and 4G LTE, URLLC (Ultra Reliable Low Latency Communications) which is already partly developed and mMTC (massive Machine Type Communications).

All of these specifications when finally completed and implied will prove to be a big push in promoting IoT( Internet of Things) that is already bumping up against existing networking's physical limits because of its fast growth. For example, sensors in industrial equipment are providing not only gobs of data but also a need to analyze that data in real time, which not only imposes bandwidth needs all of its own, but also requires a serious upgrade in acceptable latency.

5G will lighten the load for cloud-based applications through its vastly increased capacity, but MEC, in particular, will take them to the next level Henceforth, the IoT (Internet of Things) will massively benefit from MEC. By their very nature, the connected devices that will be littered around our homes and cities will require any computing tasks to be done in the cloud. Bringing cloud computing closer to them will be better for the reliability, speed and efficiency of their operation and efficiency.

This, in turn, will prove to be a big boon for the global market and tech-leaders like Qualcomm, Huawei, Intel, Nokia, Ericsson, and Samsung, among others, that are also leading its development.

Certainly, 5G mobile computing will cause a whirlwind the moment it will hit the market but till when can we expect it to reach us – the common folks?

Before its worldwide commercial launch expected in 2020, numerous telecom operators like Korea telecom and Telstra have already given the world an insight for what is to come by using 5G technology at the 2018 Winter Olympics and at the 2018 Commonwealth Games.
More than that since Mobile edge computing will be an essential component of 5G, the general public can expect the debut of the astounding combination in smart-phones till 2020. Prior to that, 5G-enhanced services of fixed wireless broadband will likely begin rolling out in 2019.
An exuberant test of this technology was also witnessed when InterDigital conducted the first real-world trial of the technology in Bristol back in August 2017 in the form of a three-week trial that took the form of a city-wide treasure hunt, with location-specific video riddles beamed to participants’ smart-phones.
Considering that it lacked a full 5G network environment to provide it with much needed back it up, the trial successfully recorded latency of as little as several milliseconds and video distribution that was implicitly more efficient than with standard IP technology. But, there hasn’t been a completely successful trial for the same. So far, all the experiments done on this tech has been focusing on only singular aspects.
Needless to say, it’s going to be a bumpy road ahead for this marvellous piece of technology as it still requires a lot of work and a subsequent amount of research on the same is still in progress.
However, among all its advantages 5G technology has not been without speculations and doubts that are well placed in their own right.
For one, the 5G radio system isn't compatible with 4G radios that elucidate more hardware costs that may reach up to billions. Even though network operators who have purchased wireless radios previously, may be able to upgrade to the new 5G system via software rather than going the extra mile by buying new equipment, the appropriate returns are, by such up gradation, still in question.
Also, some cities have blocked deployment of 5G systems because of health and safety concerns which were even supported by a petition opposing the high density of these towers.
But with countries like USA and China implementing this technology in their markets nation-wide, it seems like there will be no stopping 5G mobile and networking systems to slowly outreach to other countries and with time, to overthrow the existing 4G networks.
Soon, the future will be upon us and hopefully, it will lead us to a more technologically advanced world that will not only eradicate first world problems but will also give way to the dawn of a new era extraordinaire.

Written by:
KIRTI BHARDWAJ

03/04/2019

Shubhangi Gupta of BCA 4th Semester (1st Shift) explained Data Science in a simpler version. Read the article below to get in-depth knowledge on the topic.

DATA SCIENCE
Data Science is a combination of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data that is vast in nature - often called big data.
Big data may be defined as data sets whose size is beyond the ability of typical database software tools to capture, create, manage and process. The concept of big data came with the era of the outburst of the internet where the size of the data sets that need to be processed became so huge that the normal methods of information processing couldn’t deal with such amounts of data which makes big data very important as most data collected now is unstructured and requires different types of storage and processing techniques than that found in traditional relational databases. The Internet has democratized data, steadily increasing the data available while also producing more and more raw data. Data, when is in its raw form, has no value. Thus, data needs to be processed in order to be of treasured. However, here lies the integral problem of big data.
With the world being on the verge of an era where big data plays an important role in all things IT, it becomes very difficult to access so much of data through the previously used statistical measures. With a major difference between the rate at which this huge amount of data is being generated by sources such as the internet, smart phones, etc and the rate at which information is being processed by us- data science just becomes the need of the hour.
Data science enables the use of theoretical, mathematical, computational and other practical methods to study and evaluate data and help us bridge the gap between data and processed information in a much more efficient way.
The key objective of data science is to extract essential or valuable information that may be used for various purposes, such as decision making, product development, trend analysis and forecasting. The need for data science is increasing as companies need to use data to run and grow their everyday business.
The fundamental goal of data science is to help companies make quicker and better decisions, which can get them among the ranks of global tech-leaders, or at least promote long-term survival in the market.
Data science changes how decisions are made, and companies are adopting a data-driven approach on a huge scale. Data-driven decisions using advanced data analytics benefit companies greatly. The magic of data science doesn’t end here, though. It not only helps businesses analyze their growth and progress through trends or patterns but sometimes data science shows us invaluable information which would have certainly been missed by standard methods of information processing.
Lack of data is rarely an issue as a massive amount of data is collected every second and we are beginning to recognize the potential and influence it can have. Data sets with the techniques and technical personnel can help predict and shape the future.
The problem is getting data sets to mingle. It is the data scientist’s role to redirect organizations from reactive environments with static and aged data, to automated ones that continuously learn in real time. The speed at which every industry is moving forward with the help of emerging technologies, it’s not just important to understand your own business but it is just as necessary to keep up with the competition in the market- something that can be achieved with the smart use of data science only.
Data science techniques comprise of data mining, big data analysis, data extraction and data retrieval. Moreover, data science concepts and processes are obtained from data engineering, statistics, programming, social engineering, data warehousing, machine learning and natural language processing, among others.
Now let us dive deeper and learn the various data science concepts:
Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information. This information is, then, collected and assembled in common areas, such as data warehouses or data lakes. Moreover, for efficient analysis, data mining algorithms facilitate decision making in businesses that lead to cut costs and increase in revenue for the overall success, ultimately.
Data mining is also known as data finding or knowledge discovery.
The major stages involved in a data mining process are:
• Extract, renovate and load data into a data warehouse
• Store and manage data in a multidimensional database
• Provide data access to business analysts using application software
• Present analyzed data in easily understandable forms, such as graphs

Data extraction is analyzing the data and then retrieving relevant information from data sources (like a database) in a specific pattern. Further data is processed, which includes adding metadata and the integration of the other remaining data. Most of the data extraction comes from unstructured data sources and different data formats. This unordered data can be in any form, such as tables, indexes, and analytics.

Data retrieval, for instance, in databases, is the process of identifying and extracting data from a database, based on a query provided by the user or application. It permits the fetching of data from a database in order to display it on a monitor and/or use within an application. Data retrieval typically involves writing and executing data retrieval or extraction commands on a database. Based on the query provided, the database searches and retrieves the data requested.
There is no end when it comes to the study and discussion of data sciences, which makes data science as a new, innovative and emerging field in its own right.

Written by:
Shubhangi Gupta
BCA II year I shift

03/04/2019

"Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world."

Ritik Mittal of BCA 4th Semester explained about DATA SCIENCE in a very interesting manner. This article explains DATA SCIENCE and its LIFECYCLE . Read the articles given below and also give your opinions related to the article.

DATA SCIENCE
In this new era that we are living in, vast amount of data is generated in mere seconds via social media, smart phones and by any online platform, you can think of. This data is commonly referred to as big data.
While we were able to solve the problem of data storage with frameworks like Hadoop, etc, the problem of processing of data thus stored has occurred. Here, data science comes into play. Now the question arises, what is data science?
Data science is the science which deals with big data by converting it into a meaningful form.
Data science combines different fields, tools, algorithms and various machine learning principles to interpret data for decision making. Also, data scientists play an important role in all the processes associated with data science.
Data scientists are hired by companies for the purpose of analytical research on big data. They are part mathematicians, part computer scientist and part trend- spotter, while also having a great understanding of the software architecture and various computer languages to go along with it.
While data science has come into the limelight just now, its origins date back to 1962 when John W. Tukey predicted the effect of the digital world on data analysis as an empirical Science. In 1977 The International Association for Statistical Computing (IASC) linked modern computer technology with the conversion of information into a valuable one. Needless to say, in the last 50 years, there has been a significant evolution in the field of data science since dating back to 1962
Now the question arises as to why do we need data science? According to the ongoing research, 2.5 quintillion bytes of data is created every day in which most of the data comes under the category of unstructured and semi-structured.
To convert unstructured and semi-structured into the structured one we have to analyze the data and then, evaluate the data. Due to much usage of internet, unstructured data is more vast then semi-structured one. E-mail, voice-mails, ECG recordings and audio recording all come under unstructured data.
However, that’s not the only reason for the popularity of data science, today. Data collected from ships, aircrafts, radars and satellites are used to forecast weather and predict possible natural calamities. There are many other applications of data science in the field of artificial intelligence, machine learning, etc.
There are 6 main phases in a lifecycle of a typical data science process i.e. discovery, data preparation, model planning, model building, operationalise and communication of results.
Discovery- This is the first step to discover the objectives and requirements according to the business perspective. In this phase, data scientists discover all the valuable resources such as time, people, technology and data which are further used for data analysis.
Data Preparation- This phase is used to prepare the final dataset. This phase requires an analytic sandbox in which data scientists perform analysis for the project. The analytics team needs to extract, transform and load (ETL) to get data into the sandbox. In this phase, meaningful data is prepared from the whole data.

Model Planning- To prepare a model first, we have to determine the techniques, procedures and methods. Meaningful data obtained after the data preparation phase is molded to a model by forming relationships between the data.
Model Building- In this phase, we build a model and test the objectives of the same. This phase is mainly to find issues related to the model made in the previous step.
Communication of result- In this phase, the analytical team summarizes the objective of the model, collect the information related to the model and convey it to all stakeholders.
Operationalise- This is the last step of the data science lifecycle in which the analytical team delivers reports, code, and documents related to the model along with preparing a dummy-model in the production environment to implement the same.
Data Science requires a lot of skills and some of the major skills are:
Mathematical Expertise - Data science requires data scientists to be a mathematically-sound expert, while also having the knowledge of linear algebra and machine learning as much of the data are in numbers and needs to be correlated in mathematical terms.
Technical Skill - Data scientist should be aware of programming languages like Python, R and other technologies like Hadoop, ML, SQL, etc. Data scientists should be expert in data visualization as well.
Business acumen - Business Understanding is the foremost skills required that a data scientist needs to master for taking proper decision on behalf of the businesses he is working on. Data Scientist should know how the data and the results that are drawn from its analysis will impact the business.
Needless to say, in the light of the above discussion data science is a very demanding yet rewarding field and might be of significant impact in the future.

Written by:
Ritik Mittal

02/04/2019

"The Generation Of Wireless Technology "

JAI BATRA of BCA 4th Semester (2nd Shift)
explained the 5G and its Potential. This Article explained that How 5G becomes a Strong Backbone of Network Connection. Read the article given below and also give your opinions related to the article.

UNLEASHING 5G
"5G Devices are about to change our very lives!"

5G is the new generation of radio systems and network architecture. It is promising to deliver extreme broadband, ultra-robust low latency connectivity, and massive networking services to support different use cases and business models. For mobile operators, 5G is enabling a new level of network economy and a leap forward in network efficiency.

The G in 5G means it's a generation of wireless technology. While most generations have technically been defined by their data transmission speeds, each has also been marked by a break in encoding methods. There was 2G, which came along in 1991, replaced with 3G in 2001, followed by 4G in 2009. Now the world is chatting about the coming of 5G.

1G was analogue cellular whereas 2G technologies, such as CDMA and GSM were the first generation of digital cellular technologies. 3G technologies brought speeds from 200kbps to few megabytes per second. 4G technologies such as LTE were the incompatible leap forward. And 5G brings new aspects to our attention with greater speed, lower latency, and ability to connect more devices at once.

For example, apps will no longer degrade our videos or postpone downloading when we are out of Wi-Fi range. In fact, we'll probably prefer to do our downloading when we're on cellular devices because 5G will be much faster than the network or services we have got on our computers.Further, our phones will become radically more powerful. Today the processors we are using in our devices have limitations in the form of battery capacity and heating over continuous use. 5G will indeed revolutionise mobile technology, but how?

5G works, like other cellular networks, except that 5G network uses a system of cell sites that divide their territory into sectors and send encoded data through radio waves. Each cell site must be connected to a network backbone, whether through wired or wireless backhaul connection.
5G networks will use encoding called OFDM, which is quite similar to the encoding that is used in 4G LTE uses. The air interface will be designed for much lower latency and greater flexibility than LTE.

5G networks need to be much smarter than the previous systems, though, as they are smaller in cells that can change in size and shape even compared to the existing macrocells. As per Qualcomm, 5G will be able to boost capacity four times over current systems. The goal is to have far higher speeds available and far high capacity per sector, at a far lower latency than 4G. The standards involved are aiming at 20Gbps speeds and 1ms latency that just might be the change we are looking for.
Another question arises about the frequency at which 5G will operate. 5G primarily will run in two kinds of airwaves: below and above 6GHz.

Low-frequency 5G networks, which use existing cellular and Wi-Fi bands, take advantage of more flexible encoding and bigger channel sizes to achieve speeds 25-50% better than LTE. Those networks can cover the same distances as existing cellular networks and generally won't need additional cell sites. Sprint, for example, is setting up all of its new 4G cell sites as 5G-ready, and it'll just flip the switch when the rest of its network is prepared.
All in all, 5G will offer the following advantages:

-High resolution and bi-directional large bandwidth shaping.
-Technology to gather all networks on one platform.
-More effective and efficient.
-Technology to facilitate subscriber supervision tools for quick action.
-Most likely, will provide huge broadcasting data (in Gigabit), which will support more than 60,000 connections.
-Easily manageable with the previous generations.
-Technological sound to support heterogeneous services (including private network).
-Possible to provide uniform, uninterrupted, and consistent connectivity across the world.
-Parallel multiple services, such as you can know about the weather and location while simultaneously talking on the phone.
-You can control your PCs by handsets.
-Education will become easier − taking online classes for any student across the globe will become really easy,
-Medical Treatment will become easier & frugal − A doctor can treat the patient located in any remote part of the world.
-Monitoring will be easier − A governmental organization and investigating offers can monitor any part of the world and crime rate might be decreased.
-Visualizing universe, galaxies, and planets will be possible.
-Cases like missing person cases might be solved faster.
-Possible, natural disasters including tsunami, earthquake etc. can be detected faster.
Indeed, 5G devices are going to change our way of thinking, acknowledging, feeling, observing not only our lives but our outlook towards the world.

WRITTEN BY:

JAI BATRA
BCA II YEAR I SHIFT

01/04/2019

“Data Really Powers Everything That We Do.”

Ish*ta Srivastava of BCA 4th Semester (2nd Shift)
explained the Data Science and its Life-cycle in a well defined manner. This Article explained that How Data Science plays a vital role to play in IT Field.Read the article given below and also give your points related to the article.

DATA SCIENCE – AN OVERVIEW OF ITS LIFECYCLES AND APPLICATIONS

In this modern world, we are always concerned with the issue of storing big data; as this need is increasing gradually. Years ago our focus was limited to just develop the solutions for storing data and the result are frameworks like Hadoop came into existence and then, with time the ultimate focus diverted to processing of this data. Now for this purpose data science came into the picture.

So, what is data science? Data science is a field that involves the combination of academic fields into one activity. It takes the information from other fields as well including Physics, Chemistry etc. It focuses on the formation of something that is beyond limits or boundaries. It uses scientific methods, processes, algorithms to take out information from data in various forms, both structured and unstructured. It is a blend of various tools and machine learning principles with the intention to discover patterns from data.

A typical lifecycle of data science includes- discovery, data preparation, model planning, model building, perform operations & communicate results.

As we progress, the need for data science also increases as the data we had previously was small in size, structured and could have been analysed using simple business tools.
When we take a glimpse at real-time data collection today, we find it unstructured! And from here on the problem arises, the unstructured data gets generated from sources like text files, multimedia, instruments etc. Simple business tools are not capable of processing this big data. Thus, here comes the need of complex and advanced analytical tools for processing, analysing the data to give meaningful outputs. This is the reason that data science is becoming a booming IT sub-stream.

Not only that it has a vital role to play in IT as well, for example, a car having intelligent features to drive itself home, collects live data from sensors, radars, cameras to create a map of its surroundings. Based on this data it takes decisions when to speed up when to slow down when to overtake etc. it uses advanced machine learning algorithms making it into an application of prescriptive analysis.

Data science can be used in predictive analysis, too for example, in weather forecasting systems. Data from ships, aircraft, radars, satellites can be collected and analysed to build models. This uses advanced machine learning algorithms. These models will not only forecast the weather but also help in predicting the natural calamities.
If we need transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are proved to be best. This is considered under supervised learning as we already have the data based on which we can train our machines.

Data science is a more forward-looking approach with the aim of making informed decisions.

Another use for data sciences could be in the field of data mining that requires a lot of data either from primary or secondary sources.
It begins with identifying various data sources which could be web servers, social media data, data from the census or any other source. A major challenge occurs at the time of determining whether the data is up-to-date or not.
Moving onto the various processes involved, data preparation/data cleaning is one such task that data scientists complain about as being the most boring and the most time-consuming. Having acquired data, scientists have to clean and re-format the data through manual editing. Exploratory data analysis forms an important part of this stage.

After data planning comes model planning which is a core activity in the lifecycle and requires writing, running and refining programs to derive good insights from data, often written in programming languages like python, perl etc.
Following data planning is model planning that has different evaluation metrics for different performance metrics. For example, if the model aims to classify spam emails then performance metrics like average accuracy and log loss have to be considered. Machine learning model performances should be measured using validation and test sets to identify the best fitting model.
The next step is performing model operations that involve developing a plan for monitoring and maintaining data sets obtained and further results.
The final step of data science is communicating the results that involve retraining the machine learning models.
Data science has become popular due to its ability to store huge amount of data and then processing it and henceforth, data science can add value to any business which holds the knowledge and charisma to use whatever data they do have.
WRITTEN BY:
ISH*TA SRIVASTAVA
BCA II YEAR II SHIFT

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