Foreword

A successful preliminary project initiation meeting is the one that facilitates satisfactory results.

With immense pleasure and gratitude IEEE BKBIET SB is presenting the first edition of

“Technefois”, the magazine. Earlier named as “The Tech Times”.

This magazine is a combination of many creative mind sets who work for the betterment of life

through technology. Information of technological areas have been bound together. We, being a

team welcome all the comments and feedback by our readers. The contact details can be found

at the end of the magazine.

A special gratitude towards the Director of B. K. Birla Institute of Engineering & Technology,

Dr. S. M. Prasanna Kumar and the Branch Counsellor, Dr .L. Solanki.

Thank you.

Contents

~ From the desk of

i. Anti – Solar Panel

ii. Embedded Systems

iii. Deep Learning

iv. SAP

v. Robotic Process Automation

vi. Wearable Technology

vii. Smart home Technology

viii. Quantum Computer

ix. Regenerative Braking System

x. Cloud computing

xi. Machine Learning

xii. Blockchain

xiii. Development of Arduino

Mailing Address

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Dr. S.M. Prasanna Kumar

Director, BKBIET

Message

It gives me immense pleasure in noting down, as usual, every year this year as well, IEEE

chapter of BKBIET team is ready to publish magazine, which helps student fraternity to go

through the modern and emerging areas of technology and application area of the same in

reputed industries.

The content of this magazine is worth appreciable. The topics chosen by the students are

displaying their thoughts in new modern areas, this itself says their mode of learning and

knowing about application drive and industry requirement has started with a positive note.

The articles “Anti solar panel”, “Embedded Systems”, “Deep learning”, “SAP”, “Robotic

process Automation”, “Wearable Technology” , “Smart home Technology”, “Quantum

computers”, “Regenerative braking system”, “Cloud computing”, “Machine Learning”, “Block

chain”, “Development of Arduino” themselves say the interest shown by the students are all

buzz words in industry. Also, very proud to acknowledge the written articles are worth to note

by everyone.

I wish each and every member and all other students a grand success in taking up this chapter to

greater heights and acquire the industrial knowledge by participating and attending all activities.

From the desk of…

Prof. (Dr.) L. Solanki

Branch Counsellor – IEEE BKBIET SB

Message

I am very pleased to learn that newly elected IEEE-BKBIET ExeComm which was formulated for

session the 2020-21 is coming up with its first issue of Technical Magazine “Technefois” and I am

sure that this magazine will provide a platform for students to share their innovative ideas and plans

to resolve the future growth of the country.

I wish to thank all its past Chairman, Vice Chair, Secretaries and ExeComm members for giving

their valuable contributions in adding value to IEEE-BKBIET chapter, and making it most active,

popular and prominent among the IEEE chapters in the northern region.

My best wishes to present Chairperson Kumar Sachin and his team members for taking up the

initiative so early and with great enthusiasm. I wish that present ExeComm will together add an

extra feather in the cap of the IEEE-BKBIET chapter.

Finally, my sincere thanks to the students whose articles have been selected for publication, and

happy to acknowledge their achievements.

From the desk of…

Mr. Santosh Jangid

Faculty Advisor – IEEE BKBIET Pilani Chapter

HoD Electronics Department

Message

It gives me immense pleasure to learn about the forthcoming technical magazine for IEEE

BKBIET Pilani Chapter .I wish to extend my deep appreciation to the new Executive Team of

this professional body who have so generously volunteered their time to plan out the new

upcoming events for IEEE Pilani Chapter. I hope that this creative endeavour will bring out an

array of scientific and technological expressions with distinct individual signatures. I congratulate

the editorial board for bringing out this magazine as per schedule, which in itself is an achievement

considering the effort and time required. I again wish the team to achieve the future endeavours.

From the desk of…

Mr. Praveen Kr. Sharma

Faculty Advisor – IEEE BKBIET Pilani Chapter

Assistant Professor, Department of Electronics and Communication Engineering

Message

The forthcoming technical magazine “Technefois”, of the IEEE BKBIET Pilani Chapter reveals

the hard work and sincerity of work that the complete IEEE team is performing from the past

many years. I have been fortunate enough to be a part of this team. I hope that the newly appointed

Executive Committee 2020 will add another milestone to their account with this magazine. I want

to wish all the members of the editorial team who are directly or indirectly associated with this

work an enormous success. I know that all the members of the IEEE BKBIET family will maintain

their legacy and continue to shine as always.

“STRIVE NOT TO BE A SUCCESS BUT RATHER TO BE OF VALUE- ALBERT EINSTEIN”

Best of Luck!

From the desk of…

Researchers have developed a new prototype of night time solar cells that can produce

electricity at night through a radiative cooling mechanism.

Solar power has numerous benefits. Notably, it is a clean and renewable energy resource that

can help us to reduce carbon emissions from fossil fuel use and mitigate climate change.

However, solar energy production is limited to daytime hours when sunlight is abundant.

Researchers from the University of California, Davis explain in a new paper that was just

published in the journal ACS Photonics that if you want to create a solar panel that generates

electricity at night, then you just have to create one that operates the exact opposite way solar

panels work during the day. It’s being referred to as the “anti-solar panel”.

Tristan Deppe of the University of Maryland and Jeremy Munday of the University of

California, Davis are currently developing prototypes of these new night time solar cells. Jeremy

Munday explained the concept in detail in statement.

A regular solar cell generates power by absorbing sunlight, which causes a voltage to appear

across the device and for current to flow. In these new devices, light is instead emitted and the

current and voltage go in the opposite direction, but you still generate power. You have to use

different materials, but the physics is same.

You have heat energy coming from the Sun towards the Earth and that normal solar cell picks

off that energy as it’s transmitted from Sun to Earth, so basically you need these two different

temperature bodies and some way of converting that power. What this nigh time device does is

similar sort of thing – where it’s just taking a hot body and a cold body – but now the relatively

hot body is the Earth and space is the cold body. As this heat is flowing from the Earth to outer

space, it’s picking that off and converting that into power.

Munday and his team are currently working on developing prototypes to see how well they can

make this concept work.

While the prototype night time solar cells can only generate about a quarter of the energy

produced by conventional solar cells, the scientist hoping that they can improve their

performance in the future better designs. This would represent an exciting breakthrough in

renewable energy research.

SOLAR Panels don’t generate

electricity at night, so we have to

store the electricity they generate

during the day to power

applications during the evening.

That works fine, but what if we

could develop solar panels that

did generate electricity at night?

It’s possible, and the way it works

is pretty surprising.

01

ANTI – SOLAR PANEL

The devices don’t use the same technology as solar panels, although they’d probably look

similar. Solar panels rely on photovoltaic cells that absorb to create electron – hole pairs across

the semiconductor, generating a working voltage. A night time panel would use a

thermoradiative cell to emit infrared radiation from the Earth into space to create electron – hole

pairs.

The team estimates that thermoradiative cells would only be able to generate about a quarter as

much power as a solar panel of the same area. That’s mainly a consequence of the lower energy

of infrared light. Silicon is the current material of choice for solar panels as it’s good at

capturing light in the visible wavelengths. It may be possible to boost the efficiency of

thermoradiative cells by using materials that can better interact with longer wavelengths of light,

for example, mercury alloys.

The University of California study is just an initial proposal for night time energy generation.

The next step is to start building the devices to see how well they perform.

Source: Deanna Conners in HUMAN WORLD; Nighttime Photovolatic Cells:

Electrical Power Generation by optically Coupling with Deep Space (Via

University of California, Davis)

TAGS: SOLAR ENERGY | CLIMATE CRISIS

-Kumar Sachin

Other researchers are also looking into how to make solar panels, or “anti-solar panels”, that generates

electricity at night. Researchers at Stanford published a paper in the journal Jouele in November showing

how a thermoelectric generator that radiates heat to the sky can generate electricity.

02

An embedded system is a computer system—a combination of a computer processor, computer

memory, and input/output peripheral devices—that has a dedicated function within a larger

mechanical or electrical system. It is embedded as part of a complete device often including

electrical or electronic hardware and mechanical parts. Because an embedded system typically

controls physical operations of the machine that it is embedded within, it often has real-time

computing constraints. Embedded systems control many devices in common use today. Ninety-

eight percent of all microprocessors manufactured are used in embedded systems. Modern

embedded systems are often based on microcontroller (i.e. microprocessors with integrated

memory and peripheral interfaces), but ordinary microprocessors (using external chips for

memory and peripheral interface circuits) are also common, especially in more complex systems.

In either case, the processor(s) used may be types ranging from general purpose to those

specialized in a certain class of computations, or even custom designed for the application at hand.

A common standard class of dedicated processors is the digital signal processor (DSP).

Since the embedded system is dedicated to specific tasks, engineers can optimize it to reduce the

size and cost of the product and increase the reliability and performance. Complexity varies from

low, with a single microcontroller chip, to very high with multiple units, peripherals and networks

mounted inside a large equipment rack. Embedded systems are commonly found in consumer,

industrial, automotive, home appliances, medical, commercial and military applications.

Telecommunications systems employ numerous embedded systems from telephone switches for

the network to cell phones at the end user. Computer networking uses dedicated routers and

network bridges to route data.

Consumer electronics include MP3 players, television sets, mobile phones, video game consoles,

digital cameras, GPS receivers, and printers. Household appliances, such as microwave ovens,

washing machines and dishwashers, include embedded systems to provide flexibility, efficiency

and features. Advanced HVAC systems use networked thermostats to more accurately and

efficiently control temperature that can change by time of day and season. Home automation uses

wired- and wireless-networking that can be used to control lights, climate, security, audio/visual,

surveillance, etc., all of which use embedded devices for sensing and controlling.

-Cherry Vaish

03

EMBEDDED SYSTEM

04

Deep Learning is a new area of Machine Learning research, which has been introduced with the

objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

It is capable of learning unsupervised data that is unstructured or unlabeled. Also known as deep

neural learning or deep neural network.

In deep learning, a computer model learns to perform classification tasks directly from images,

text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding

human-level performance. Models are trained by using a large set of labeled data and neural

network architectures that contain many layers.

While deep learning was first theorized in the 1980s, there are two main reasons it has only

recently become useful:

1. Deep learning requires large amounts of labeled data. For example, driverless car development

requires millions of images and thousands of hours of video.

2. Deep learning requires substantial computing power. High-performance GPUs have a parallel

architecture that is efficient for deep learning. When combined with clusters or cloud computing,

this enables development teams to reduce training time for a deep learning network from weeks

to hours or less.

Deep learning is a machine learning technique that teaches computers to do what comes naturally

to humans: learn by example. Deep learning achieves recognition accuracy at higher levels than

ever before. Recent advances in deep learning have improved to the point where deep learning

outperforms humans in some tasks like classifying objects in images.

What’s the difference between deep learning and machine learning?

A machine learning workflow starts with relevant features being manually extracted from images.

The features are then used to create a model that categorizes the objects in the image. With a deep

learning workflow, relevant features are automatically extracted from images. In addition, deep

learning performs “end-to-end learning” – where a network is given raw data and a task to

perform, such as classification, and it learns how to do this automatically.

A key advantage of deep learning networks is that they often continue to improve as the size of

your data increases. In machine learning, you manually choose features and a classifier to sort

images. With deep learning, feature extraction and modeling steps are automatic.

-Sakshi Kumari

DEEP LEARNING

SAP is one of the world’s leading producers of software for the

management of business processes, developing solutions that enable effective data processing

and information flow across organizations.

Founded in 1972, the company was initially called System Analysis Program Development

(Systemanalyse Programmentwicklung), later abbreviated to SAP. Since then, it has grown from

a small five-person startup to a multinational enterprise with more than 100,000 employees and

over 440,000 customers in 180 countries. Its global headquarters is in Walldorf, Germany.

With its original SAP R/2 and SAP R/3 software, SAP established the standard for enterprise

resource planning (ERP) software. SAP S/4HANA takes ERP to the next level by using the

power of in-memory computing to process vast amounts of data and support advanced

technologies such as artificial intelligence (AI) and machine learning.

The company’s integrated applications connect all parts of a business into an intelligent suite on

a digital platform. Today, SAP has more than 215 million cloud users, more than 100 solutions

covering all business functions, and the largest cloud portfolio of any provider.

The name is an acronym of the company’s original German name: Systemanalyse

Programmentwicklung, which translates to System Analysis Program Development. Today the

company’s legal corporate name is SAP SE — SE stands for societas Europaea, a public

company registered in accordance with the European Union corporate law.

What is SAP software used for?

Traditional business models often involve decentralized data management, with each business

function storing data about its operations in a separate database. This means that employees from

other business functions cannot access the information, and duplication of data across multiple

departments increases IT storage costs and the risk of data errors.

By centralizing data management, SAP software provides a single view of the truth that is used

by multiple business functions. This helps companies better manage complex business processes

involving different departments by giving employees access to real-time insights across the

enterprise. As a result, businesses can accelerate workflows, improve operational efficiency, raise

productivity, enhance customer experiences – and ultimately increase profits.

-Tushar Budhiraja

05

SAP

Robotic Process Automation (RPA) is enjoying tremendous growth as organizations worldwide

seek to accelerate productivity and boost efficiency by automating mundane and repetitive tasks.

Yet despite its benefits, RPA continues to stir debates about replacement of the human workforce.

When you bring up automation, it is not uncommon for employees to think “job loss.”

A significant number of organizations remain hesitant to adopt RPA within business or IT

functions because employees worry that it will change their roles and responsibilities.

When implemented thoughtfully, however, RPA can reduce the costs associated with Human

Training, services, and support will become more important over time.

RPA does not interfere with existing systems of records or business applications and therefore

does not require a lot of integration work. However, to leverage RPA software, individuals and

teams must be trained so that deployment and implementation meet expectations.

People didn’t believe much on creation of robots in older decades. Aparently, humans again

proved that nothing is impossible. Robots do much of the human work, satisfactorily when given

proper command. Digital workers and robots are essential to eliminate existing skill gaps while

also improving the productivity and quality of work. One way to overcome resistance is to identify

an initial path and start by automating small, highly manual processes. This will let you prove

quick wins that build momentum with key stakeholders and employees.

Most of the vendors offer free version that allow for proofs-of-concept, and current, per-bot

licensing models make it possible to invest into RPA one bot at the time.

-Suyash Sharma

06

ROBOTIC PROCESS

AUTOMATION

Wearable technology, wearables, fashion technology, tech togs, or fashion electronics are smart

electronic devices (electronic device with micro-controllers) that are worn close to and/or on the

surface of the skin, where they detect, analyze, and transmit information concerning e.g. body

signals such as vital signs, and/or ambient data and which allow in some cases immediate

biofeedback to the wearer. Wearable technology has a variety of applications which grows as

the field itself expands. It appears prominently in consumer electronics with the popularization

of the smartwatch and activity tracker. Apart from commercial uses, wearable technology is

being incorporated into navigation systems, advanced textiles, and healthcare.

Google Glass

The day marked with the official launch of Google Glass, a device intended to deliver rich text

and notifications via a heads-up display worn as eyeglasses. The device also had a 5 MP camera

and recorded video at 720p. Its various functions were activated via voice command, such as

"OK Glass". The company also launched the Google Glass companion app, MyGlass. The first

third-party Google Glass App came from the New York Times, which was able to read out

articles and news summaries.

Monitoring Systems for assisted living

Another field of application of wearable technology is monitoring systems for assisted

living and eldercare. For this reason, researchers are moving their focus from data collection to

the development of intelligent algorithms able to glean valuable information from the collected

data, using data mining techniques such as statistical classification and neural networks.

Virtual Reality

Another increasingly popular wearable technology involves virtual reality. VR headsets have

been made by a range of manufacturers for computers, consoles, and mobile devices. Recently

Google released their headset, the Google Daydream. In July 2014 a smart technology footwear

was introduced in Hyderabad, India. The shoe insoles are connected to a smartphone application

that uses Google Maps, and vibrate to tell users when and where to turn to reach their

destination.

07

WEARABLE TECHNOLOGY

Bluetooth Sunglasses

This bluetooth headset headphone sunglasses is the perfect combination of durable and fashion.

You may wear it and enjoy music anytime anywhere, getting hands-free phone call, control the

phone to take pictures of yourself. It also can filter ultraviolet and protect your eyesight at the

same time. The Bose Frames are the answer to the question: what if your sunglasses were also a

set of smart, hidden headphones with no earbuds or no bone-conduction system, just a set of

personal speakers? There’s no screen, camera or any visible signs of “smart” from the front.

Instead they have built-in sensors and a pair of hidden speakers, which pipe music to your ears.

Smartwatches and Activity Trackers

While optical head-mounted display technology remains a niche, two popular types of wearable

devices have taken off: smartwatches and activity trackers.

The FDA drafted a guidance for low risk devices advises that personal health wearables are

general wellness products if they only collect data on weight management, physical fitness,

relaxation or stress management, mental acuity, self-esteem, sleep management, or sexual

function.This was due to the privacy risks that were surrounding the devices. Although they help

track health and promote independence there is still an invasion of privacy that ensues to gain

information. This is due to the huge amounts of data that has to be transferred which could raise

issues for both the user and the companies if a third partied gets access to this data. The issue is

consent as well when it comes to wearable technology because it gives the ability to record and

that is an issue when permission is not asked when a person is being recorded.

-Komal Sharma

08

Smart Home technology often referred to as home automation or domotics (from the Latin

"domus" meaning home), provides homeowners security, comfort, convenience and energy

efficiency by allowing them to control smart devices, often by a smart home app on their

smartphone or other networked device. A part of the internet of things (IoT), smart home systems

and devices often operate together, sharing consumer usage data among themselves and

automating actions based on the homeowner’s preferences.

The origin of the smart home

With the 1975 release of X10, a communication protocol for home automation, the smart home,

once a pipe dream a la The Jetsons, came to life. X10 sends 120 kHz radio frequency (RF) bursts

of digital information into a home's existing electric wiring to programmable outlets or switches.

These signals convey commands to corresponding devices, controlling how and when the devices

operate. A transmitter could, for example, send a signal along the house's electric wiring, telling

a device to turn on at a specific time.

Nest Labs was founded in 2010 and released its first smart product, the Nest Learning Thermostat,

in 2011. The company also created smart smoke/carbon monoxide detectors and security

cameras. After being acquired by Google in 2015, it became a subsidiary of Alphabet Inc. in the

same year.

In 2012, Smart Things Inc. launched a Kick starter campaign, raising $1.2 million to fund its

smart home system. Following additional funding, the company came in the market in August

2013 and was acquired by Samsung in 2014.

Recently, companies including Amazon, Apple and Google have released their own smart home

products and demotics platforms, including Amazon Echo, Apple Home Kit and Google Home.

Examples of smart home technology

~ Smart TVs connect to the internet to access content through applications, such as on-demand

video and music. Some smart TVs also include voice or gesture recognition.

~ In addition to being able to be controlled remotely and customized, smart lighting systems,

such as Hue from Philips Lighting Holding B.V., can detect when occupants are in the room and

adjust lighting as needed. Smart light bulbs can also regulate themselves based on daylight

availability.

09

SMART HOME TECHNOLOGY

• Using smart locks and garage-door openers, users can grant or deny access to visitors. Smart

locks can also detect when residents are near and unlock the doors for them.

• With smart security cameras, residents can monitor their homes when they are away or on

vacation. Smart motion sensors are also able to identify the difference between residents,

visitors, pets and burglars, and can notify authorities if suspicious behavior is detected.

Kitchen appliances of all sorts are available, including smart coffee makers that can brew you

a fresh cup as soon as your alarm goes off.

• Household system monitors may, for example, sense an electric surge and turn off appliances

or sense water failures or freezing pipes and turn off the water so there isn't a flood in your

basement

• Smart thermostats, such as Nest from Nest Labs Inc., come with integrated Wi-Fi, allowing

users to schedule, monitor and remotely control home temperatures. These devices also learn

homeowners' behaviors and automatically modify settings to provide residents with

maximum comfort and efficiency. Smart thermostats can also report energy use and remind

users to change filters, among other things.

• Using smart locks and garage-door openers, users can grant or deny access to visitors. Smart

locks can also detect when residents are near and unlock the doors for them

-Kanchan Tanwar

10

Quantum computers are machines that use the properties of quantum physics to store data and

perform computations. This can be extremely advantageous for certain tasks where they could

vastly outperform even our best supercomputers.

Classical computers, which include Smartphone’s and laptops, encode information in binary

“bits” that can either be 0s or 1s. In a quantum computer, the basic unit of memory is a quantum

bit or qubit.

Quantum computers, on the other hand, use qubits, which are typically subatomic particles such

as electrons or photons. Generating and managing qubits is a scientific and engineering

challenge. Some companies, such as IBM, Google, and Rigetti Computing, use superconducting

circuits cooled to temperatures colder than deep space. Others, like IonQ, trap individual atoms

in electromagnetic fields on a silicon chip in ultra-high-vacuum chambers. In both cases, the

goal is to isolate the qubits in a controlled quantum state.

Qubits have some quirky quantum properties that mean a connected group of them can provide

way more processing power than the same number of binary bits. One of those properties is

known as superposition and another is called entanglement.

What can quantum computers do?

Quantum computers will find a use anywhere where there’s a large, uncertain complicated

system that needs to be simulated. That could be anything from predicting the financial markets,

to improving weather forecasts, to modelling the behaviour of individual electrons: using

quantum computing to understand quantum physics.

Quantum computers aren’t just about doing things faster or more efficiently. They’ll let us do

things that we couldn’t even have dreamed of without them. Things that even the best

supercomputer just isn’t capable of.

11

QUANTUM COMPUTERS

WHAT IS QUANTUM SUPERPOSITION ?

Superposition helps do away from binary constraints. The working of a quantum computer is

based on using the particles in superposition. Rather than representing bits, such particles

represent qubits, which can take on the value 0, 1, or both simultaneously.

Quantum computer can hold the information using a system that can exist in two states at the

same time. This is possible due to the superposition principle of quantum mechanics. This

“qubit” can simultaneously store a “0” and “1.” Similarly, two qubits can simultaneously hold

four values: 00, 01, 10, and 11.

Conclusion — TRANFORMATION IN TECHNOLOGY

We can never imagine what such a new technology can achieve in the next 10 years. Researchers

are working on creating full-fledged quantum computers to answer problems which are just too

hard for classical computers today. Many fields such a cryptography, machine learning,

information security may soon undergo a drastic change due to the advent of the Quantum

computing capabilities. World of science and technology is getting weirder and harder to explain

day by day. We cannot even imagine how the entire concept of computing might change in the

coming years, and that’s what excites me the most!

“Without change there is no innovation, creativity, or incentive

for improvement. Those who initiate change will have a better

opportunity to manage the change that is inevitable.”

-Abhishek Pandey

12

Moving vehicles have a lot of kinetic energy, and when brakes are applied to slow a vehicle, all

of that kinetic energy has to go somewhere. Back in the Neanderthal days of internal combustion

engine cars, brakes were solely friction based and converted the kinetic energy of the vehicle into

wasted heat in order to decelerate a car. All of that energy was simply lost to the environment.

Fortunately, we have evolved as a species and developed a better way. Regenerative braking uses

an electric vehicle’s motor as a generator to convert much of the kinetic energy lost when

decelerating back into stored energy in the vehicle’s battery. Then, the next time the car

accelerates, it uses much of the energy previously stored from regenerative braking instead of

tapping in further to its own energy reserves.

It is important to realize that on its own, regenerative braking isn’t a magical range booster for

electric vehicles. It doesn’t make electric vehicles more efficient per se, it just makes them less

inefficient. Basically, the most efficient way to drive any vehicle would be to accelerate to a

constant speed and then never touch the brake pedal. Since braking is going to remove energy and

require you to input extra energy to get back up to speed, you’d get your best range by simply

never slowing down in the first place.

But that obviously isn’t practical. Since we need to brake often, regenerative braking is the next

best thing. It takes the inefficiency of braking and simply makes the process less wasteful.

Effectiveness

This is where things get really interesting. The effectiveness of regenerative braking is a measure

of how much it can increase your range. Does it make your theoretical range 5% further? 50%

further? Even more?

As you’ve probably already guessed, the effectiveness of regenerative braking varies

significantly based on factors including driving conditions, terrain and vehicle size.

Driving conditions have a large impact. You’ll see much better effectiveness for regenerative

braking in stop-and-go city traffic than in highway commuting. This should make sense, as if

you’re repeatedly braking, you’ll recapture a lot more energy than if you simply drive for hours

without touching the brake pedal. Terrain also plays a large role here too, as uphill driving

doesn’t give you much chance for braking, but downhill driving will regenerate a much larger

amount of energy due to the long braking periods. On long downhills, regenerative braking can

be used nearly constantly to regulate speed while continuously charging the battery.

13

REGENERATIVE BRAKING

SYSTEM

Vehicle size may be the largest factor in the effectiveness of regenerative braking for the simple

reason that heavier vehicles have much more momentum and kinetic energy. Just like a big

flywheel is more effective than a small flywheel, a four-wheel electric car has a lot more kinetic

energy when in motion than an electric bicycle or scooter.

Data for comparison can be somewhat hard to come by. Tesla vehicles show you the

regenerative braking power, such as 60 kW during hard braking, but that doesn’t answer the

more interesting question. We want to know how much energy we are recapturing over a trip,

not how strong our brakes are each time we mash the pedal.

Fortunately, a number of Tesla drivers have reported back energy contribution data using

different data tracking apps. Model S drivers have reported recapturing as much as 32% of their

total energy use while driving up and then back downhill. This would effectively increase a 100

mile car’s range to 132 miles, for example. A Model S P85D owner reported approximately

28% energy recapture (forum in Danish) and still others have reported recapturing between 15-

20% of their total kWh usage on average during normal trips.

For smaller EVs such as personal electric vehicles, the numbers aren’t quite as optimistic. On

multiple electric bicycles with regenerative braking options, I’ve generally averaged around 4-

5% regeneration, with a maximum of around 8% in hilly areas. Other personal electric vehicles

including electric scooters and skateboards have similar results, usually in the lower single

digits. Again, keep in mind this isn’t the raw efficiency of the system (as in how much braking

energy is lost in the energy transfer), it’s the effectiveness (as in how much further your range

increases due to the use of regenerative braking).

They do not have much momentum and thus have less kinetic energy to convert back into the

battery.

-Tarun Sharma

14

What is Cognitive Computing? Cognitive Computing refers to machine systems that can mimic human understanding of the

environment, bringing an immense level of contextualization and intelligence to business

processes. Cognitive Computing is closely related to artificial intelligence and its multiple

subsumed technologies (image recognition, pattern recognition, machine learning, natural

language processing, and the like).

It differs from traditional data analytics, owing to its agile, interactive and contextual properties.

A cognitive algorithm, for example, can intuitively change in response to real-time data and help

you make more accurate decisions. And, the interfaces used for Cognitive are very high on

intractability, letting users deep-dive into the insights and multiple predictive scenarios available.

But the biggest USP of Cognitive is possibly its power to contextualize information. Equipped

with in-the-moment data on customers/users/machines, algorithms can make decisions that are

relevant and effective.

Expectedly, Cognitive is highly resource-intensive, requiring powerful servers, deep technical

skillsets, and often leading to a high degree of technical debt. For example, developing machine

learning models not only consumes significant computing power but also adds technical debt

with every learning cycle. That’s why, for a long time, Cognitive was limited to large enterprises

such as the Fortune 500s.

This has been completely overturned by the cloud. The Cloud allows developers to build

Cognitive models, test solutions, and integrate with existing systems without needing physical

infrastructure. While there are still resource costs involved, enterprises can flexibly subscribe to

Cloud resources for Cognitive development and downscale as and when necessary.

-Abhishek Sharma

15 15

CLOUD COMPUTING

Let’s Demystify Machine Learning!!

Machine Learning: - Now that’s a word that packs a punch! Machine learning is hot stuff these

days! And why won’t it be? Almost every “enticing” new development in the field of Computer

Science and Software Development in general has something related to machine learning behind

the veils. Microsoft’s Cortana - Machine Learning, Object and Face Recognition – Machine

Learning and Computer Vision. Advanced UX improvement programs – Machine Learning (yes!

The Amazon product recommendation you just got was the number crunching effort of some

Machine Learning Algorithm).

And not even just that. Machine Learning and Data Science in general is EVERYWHERE. It is

as omnipotent as God himself, had he been into Computers! Why? Because Data is everywhere!

So it is natural, that anyone who has above average brains and can differentiate between

Programming Paradigms by taking a sneak-peek at Code, is intrigued by Machine Learning.

But what is Machine Learning? And how big is Machine Learning? Let’s demystify Machine

Learning, once and for all. And to do that, rather than presenting technical specifications, we’ll

follow a “Understand by Example” approach.

Well, Machine Learning is a subfield of Artificial Intelligence which evolved from Pattern

Recognition and Computational Learning theory. Arthur Lee Samuel defines Machine Learning

as: Field of study that gives computers the ability to learn without being explicitly programmed.

So, basically, the field of Computer Science and Artificial intelligence that “learns” from data

without human intervention.

But this view has a flaw. As a result of this perception, whenever the word Machine Learning is

thrown around, people usually think of “A.I.” and “Neural Networks that can mimic Human brains

(as of now, that is not possible)”, Self-Driving Cars and what not. But Machine Learning is far

beyond that. Below we uncover some expected and some generally not expected facets of Modern

Computing where Machine Learning is in action.

So as you might have seen now. Machine Learning actually is everywhere. From Research and

Development to improving business of Small Companies. It is everywhere. And hence it makes

up for quite a career option, as the industry is on the rise and is the boon is not stopping any time

soon.

So, this is it for now. This wraps up our Machine Learning 101. We’ll hopefully meet again, and

when we do, we’ll dive into some technical details of Machine Learning, what tools are used in

the industry, and how to start your journey to Machine Learning prowess. Till then, Code Away!

-Yashaswini .S. M.

16

MACHINE LEARNING

Although most people think of blockchain technology in relation to cryptocurrencies such as Bitcoin,

blockchain offers security that is useful in many other ways. In the simplest of terms, blockchain can

be described as data you can only add to, not take away from or change. Hence the term “chain”

because you’re making a chain of data. Not being able to change the previous blocks is what makes it

so secure. In addition, blockchains are consensus-driven, so no one entity can take control of the data.

Key elements of Blockchain

Distributed ledger technology

All network participants have access to the distributed ledger and its immutable record of transactions.

With this shared ledger, transactions are recorded only once, eliminating the duplication of effort that’s

typical of traditional business networks

Records are immutable

No participant can change or tamper with a transaction after it’s been recorded to the shared ledger. If

a transaction record includes an error, a new transaction must be added to reverse the error, and both

transactions are then visible.

Smart contracts

To speed transactions, a set of rules – called a smart contract – is stored on the blockchain and executed

automatically.

Types of blockchain networks:

There are several ways to build it, viz:-

Public blockchain networks

A public blockchain is one that anyone can join and participate in, such as Bitcoin. Drawbacks might include substantial computational power required, little or no privacy for transactions, and weak security. These are important considerations for enterprise use cases of blockchain. Private blockchain networks

A private blockchain network, similar to a public blockchain network, is a decentralized peer-to-peer network, with the significant difference that one organization governs the network. Permissioned blockchain networks Businesses who set up a private blockchain, will generally set up a permissioned blockchain

network. It is important to note that public blockchain networks can also be a permissioned.

The Bitcoin is the first successful implementation of blockchain. Today, the world has found

applications of blockchain technology in several industries, where the trust without the involvement of a

centralized authority is desired. So welcome to the world of Blockchain.

-Sandeep Shah

-

17

BLOCKCHAIN

Arduino provides open-source electronics prototyping platforms based on flexible, easy-to-use

hardware and software. Arduino prototyping platforms are intended for artists, designers,

hobbyists, and anyone interested in creating interactive objects or environments. Arduino's

prototyping platforms can sense the environment by receiving input from a variety of sensors and

can affect their surroundings by controlling lights, motors, and other actuators. Arduino projects

can be stand-alone or they can communicate with software running on a computer.

In 2005, building upon the work of Hernando Barragán (creator of Wiring), Massimo Banzi and

David Cuartielles created Arduino, an easy-to-use programmable device for interactive art design

projects, at the Interaction Design Institute Ivrea in Ivrea, Italy. David Mellis developed the

Arduino software, which was based on Wiring. Before long, Gianluca Martino and Tom Igoe

joined the project, and the five are known as the original founders of Arduino. They wanted a

device that was simple, easy to connect to various things (such as relays, motors, and sensors),

and easy to program. It also needed to be inexpensive, as students and artists aren’t known for

having lots of spare cash. They selected the AVR family of 8-bit microcontroller (MCU or µC)

devices from Atmel and designed a self-contained circuit board with easy-to-use connections,

wrote bootloader firmware for the microcontroller, and packaged it all into a simple integrated

development environment (IDE) that used programs called “sketches.” The result was the

Arduino.

Since then the Arduino has grown in several different directions, with some versions getting

smaller than the original, and some getting larger. Each has a specific intended niche to fill. The

common element among all of them is the Arduino runtime AVR-GCC library that is supplied

with the Arduino development environment, and the on-board bootloader firmware that comes

preloaded on the microcontroller of every Arduino board.

-Manash Khetan

DEVELOPMENT OF ARDUINO

18

02

IEEE Executive Committee 2020-2021

Designation Member Name

Chairperson Kumar Sachin

Vice-Chairperson Cherry Vaish

WIE Chairperson Sakshi Kumari

General Secretary Tushar Budhiraja

Joint Secretary Suyash Sharma

WIE Vice-Chairperson Komal Sharma

Technical Coordinator (Comp. Soc.) Abhishek Pandey

Technical Coordinator (Robex) Kanchan Tanwar

Graphic Designer Tarun Sharma

PR Head Abhishek Sharma

Editorial & Publication Head Yashaswini .S.M.

Program Coordinator Sandeep Shah

Program Coordinator Manash Khetan

Mailing Address:

B. K. BIRLA INSTITUTE OF ENGINEERING & TECHNOLOGY

IEEE BKBIET Student Chapter

CSIR-CEERI Road, Pilani – 333 031

Rajasthan (INDIA)

Ph.: +91-99586 96950 | e-mail: ieeevibes@gmail.com | www.ieeebkbiet.in