Fourth paradigm

MAHIRSHAM ANEKA DANTAM
BHAKTANAM

Do We need to rejuvenate our
self in Statistics to herald the 21st
Century research?
Dr. N.B. Venkateswarlu
Visiting Fellow, School of Computer Studies,
Univ. of Leeds, UK,1992-1995
ISTE Visiting Fellow, 2010-11
Former Faculty Member of BITS, Pilani
Currently at: AITAM, Tekkali
venkat_ritch@yahoo.com
www.ritchcenter.com/nbv

Fourth Paradigm
Dr. N.B. Venkateswarlu
Visiting Fellow, School of Computer Studies,
Univ. of Leeds, UK,1992-1995
ISTE Visiting Fellow, 2010-11
Former Faculty Member of BITS, Pilani
Currently at: AITAM, Tekkali
venkat_ritch@yahoo.com
www.ritchcenter.com/nbv

My itinerary:
• Some of my observations on Indian Research.
• Simple recap of USA identified Grand
Challenges of 21st Century.
• Predictions for 21st Century.
• 16 Massive Scientific Facilities at the Cutting
Edge of Research.
• IOT (Internet of Things), a new dimension for
scientific research.
• Dawn of Data Science research.
• Essential Statistics to prepare us for 21st
century.

May be, my talk is both critique
and progressive.
• విమర్శ కులు (Critique)
• వికటకవి(తెనాలి రామలి0గము వలెనే)
In politics, even if you
loose, yet you can get
cabinet post!! May be,
Sonia example.

Let me first have a simple recap of my
29 years of frustration (of course
enjoyed it ) as an Engineering
Teacher/researcher.
•సి0హావలోకన0

Success stories in 20th Century
In the century just ended, engineering
recorded its grandest
accomplishments. The widespread
development and distribution of
electricity and clean water, automobiles
and airplanes, radio and television,
spacecraft and lasers, antibiotics and
medical imaging, and computers and
the Internet are just some of the
highlights from a century in which
engineering revolutionized and
improved virtually every aspect of
human life.

My observations on Indian
research in 20th Century. I am
lucky as I belong to both 20th and
21st Century academics.
May be 20 years back, Indian
research was experimental.
Because of the availability of
Computers, majority of current
research works are around
computer simulation, modeling.

Indian Research with my eyes-
observations are independent of
engineering branch.
• Optimization
• Fuzzy
• Neural
• Expert Systems
• Data Mining
• Evolutionary algorithms
• Machine learning

I feel, again, research will be
shifting towards experimental
oriented because of the
developments of Sensors, IOT.

My observations.
• During my time, statistics is in Inter. The
same is moved to high school. Some how, it
is not taught properly or left in choice.
• Now, it is covered under the course
“Probability and Statistics” during
Bachelors. Unfortunately, emphasis is not
given to it. Practical flavor is not delivered.

My observations: Research
Methodologies course
• Research methodologies course is
supposed to be completed by every Ph.D
student in majority of Universities, where
we are exposed to statistics, analysis,
experimentation, etc. Unfortunately, in
India, it became a course on paper only.

My Thanks
• Late. Dr. M. N. Reddy garu, my friend at IIT,
Kanpur. He has inculcated my interest in
Statistics.

My observations: Passing
knowledge downwards
(knowledge infiltration] is not
taking place in India. I mean,
knowledge and research
outcomes are introduced at
higher degree, some contents to
be pushed downwards.

My observations: Knowledge
infiltration
• See, in US, Engineering is started now at
School level itself.
• I remember, in some news article, that first
duty of a commission on Nano-technology
formed by Taiwan government is to identify
6 experiments to be taught at school level.

My observations: Knowledge
infiltration

Leadership in innovation is
essential for any country which
depends on a wide array of
factors, one of which is
leadership in engineering
research, education, and
practice.

Open Innovation – recent mantra
• Companies are no longer look just within
themselves for innovation, nor do they just
purchase it by acquiring small companies.
Today they obtain innovation wherever it is
found—in other companies, in other
countries, or even through arrangements
with competitors. Working in this evolving
context requires a nimble new kind of
engineer and engineering organization.

Today, Word smart is too
ubiquous!!
• Smart devices
• Smart phones
• Smart cars
• Smart houses
• Smart offices
• Smart cities
• Smart countries
• Smart world
Are you the ultimate Smart Person?

In the recent past, Scientific
research is becoming more
data-driven. Developments in
Sensors (MEMS, Nano-sensors),
IOT are adding add-on flavor for
it. In fact, I shall be pointing more
about this in the coming slides.
This is the objective of my talk.

USA has Identified Fourteen
Grand challenges for next
century. They are:
● Make Solar Energy Economical
● Provide Energy from Fusion
● Develop Carbon Sequestration Methods
● Manage the Nitrogen Cycle
● Provide Access to Clean Water
● Secure Cyberspace
● Engineer Better Medicines
● Advance Health Informatics
● Prevent Nuclear Terror
● Restore and Improve Urban Infrastructure
● Reverse Engineer the Brain
● Enhance Virtual Reality
● Advance Personalized Learning
● Engineer the Tools of Scientific Discovery

Solar Energy: Storing is great
challenge
• Better battery technology
• One intelligent attempt in USA, during day
time using solar energy pump the water to
a reservoir at height and when needed run
turbines and generate power!!!
• To mimic the biological capture of
sunshine by photosynthesis. Sunlight to
electrolysis of water, resulting H2 to power
fuel cells, electricity generating units.

Provide Energy from fusion: To
mimic Sun
• Main problem is controlling fusion

Artificial Sun in China. Sun’s temperature
is 15million degrees. China achieved 50 million degrees and
aspiring for 100 million degrees.

Solar Energy – Artificial Sun
through fusion!!!

A reactor that is used in the
creation of an artificial: ITER-US, EU,
Japan, Russia, China, south Korea, and India.

I don’t deny our achievement as
a whole.

This one can go into active
volcanoes and even into Sun!!

Problems
• CO2 sequestration (storing)
• How do you capture CO2?
• How do you store?-Old oil fields?
• Inside earth by closing fissures, faults and
monitoring them continuously?
• In Ocean?

Managing Nitrogen cycle.
• Artificial pesticides are increasing the
availability of Nitrogen in atmosphere.
• Also planting legumes, including soya
beans, alfalfa. In addition, burning of fuel.
• Greenhouse effect, damaging ozone layer,
increasing earth temperature. Also,
respiratory illness, cancer, cardiac
disease.

Managing nitrogen cycle
• Denitrification
• Recycling food waste
• Monitoring regularly farm areas
• Monitoring industrial leaks continuously.

Personalized Medication
One goal of biomedical engineering today is fulfilling
the promise of personalized medicine. Doctors have
long recognized that individuals differ in their
susceptibility to disease and their response to
treatments, but medical technologies have generally
been offered as “one size fits all.” Recent cataloging
of the human genetic endowment, and deeper
understanding of the body’s complement of proteins
and their biochemical interactions, offer the
prospect of identifying the specific factors that
determine sickness and wellness in any individual.
An important way of exploiting such information
would be the development of methods that allow
doctors to forecast the benefits and side effects of
potential treatments or cures.

Health Informatics
The acquisition, management, and use of
information in health — can greatly enhance the
quality and efficiency of medical care and the
response to widespread public health
emergencies. Health and biomedical informatics
encompass issues from the personal to global,
ranging from thorough medical records for
individual patients to sharing data about disease
outbreaks among governments and international
health organizations. Maintaining a healthy
population in the 21st century will require systems
engineering approaches to redesign care
practices and integrate local, regional, national,
and global health informatics networks.

WIMS
Such devices are emerging from advances in
microelectronic mechanical systems for health care
delivery as wireless integrated micro systems, or
WIMS. Tiny sensors containing wireless transmitter-
receivers could provide constant monitoring of
patients in hospitals or even at home. If
standardized to be interoperable with electronic
health records, WIMS could alert health
professionals when a patient needs attention, or
even trigger automatic release of drugs into the
body when necessary. In effect, every hospital room
could be turned into an ICU. Seamlessly integrating
the input from such devices into a health informatics
system raises the networking challenge to a new
level.

Electronic Information Carried
(EIC)

Health bands
Sugar Levels
EEG
ECG
BP

EMG
EMG is a sensor system concerned with measuring the
electrical activity of your body about your skeletal muscles,
i.e.. the ones you need for locomotion. Your motor neurons
electrically stimulate muscle clusters - the more intense the
signal, the more of these clusters are involved in the activity
and, so, the harder you're getting your body to work.
While endurance sport is more cardiovascular-based,
anyone looking to build up their bodies in certain ways or
get the most our of their time at the gym really needs to
know that they're exercising the correct muscle groups as
they do so. EMG heat maps and readings can offer that.
Companies like Athos and Myontech have already created
clothing with EMG sensors embedded to keep you training
in the zone that's right for you. For Athos, it's all about the
gym to give you live feedback on your muscle effort and
your building/toning targets.

How to ready against biological,
chemical attacks? – Artificial
Nose!!!
Providing data to feed an informatics system in
preparation for bio and chemical terror involves
engineering challenges in three main categories.
One is surveillance and detection — monitoring the
air, water, soil, and food for early signs of an attack.
Next is rapid diagnosis, requiring a system that can
analyze and identify the agent of harm as well as
track its location and spread within the population.
Finally come countermeasures, powered by nimble
operations that can quickly develop and mass-
produce antidotes, vaccines, or other treatments to
keep the effects of an attack as small as possible
and track how effective the countermeasures are.

Ready against pandemic?
A major goal of pandemic
preparedness is a good early
warning system, relying on
worldwide surveillance to detect the
onset of a spreading infectious
disease. Some such systems are
now in place, monitoring data on
hospital visits and orders for drugs
or lab tests. Sudden increases in
these events can signal the initial
stages of an outbreak.

Ready against Pandemic.
But certain events can mask trends in these
statistics, requiring more sophisticated monitoring
strategies. These can include tracking the volume of
public Web site hits to explain acute symptoms and
link them to geo-codes, such as zip codes. Having
an integrated national information technology
infrastructure would help greatly. Closures of
schools or businesses and quarantines may actually
reduce hospital use in some cases, and people may
even deliberately stay away from hospitals for fear
of getting infected. On the other hand, rumors of
disease may send many healthy people to hospitals
for preventive treatments. In either case the
numbers being analyzed for pandemic trends could
be skewed.

Ready against Pandemic.
New approaches to analyzing the math can help —
especially when the math describes the network of
relationships among measures of health care use.
In other words, monitoring not just individual
streams of data, but relationships such as the
ratio of one measurement to another, can provide
a more sensitive measure of what’s going on.
Those kinds of analyses can help make sure that a
surge in health care use in a given city because of
a temporary population influx (say, for the
Olympics) is not mistaken for the beginning of an
epidemic.

Ready against pandemic
Understanding the
mathematics of networks to
estimate the spread.

Reverse Engineer the Brain!!
• Artificial brains

Reverse Engineer the brain
• To understand brain disorders
• To understand how drugs works
• To understand neural implants works
• To understand more about how brain works
• To understand how learning takes place

Cyber Security
• Psychology of computer users can be
monitored

Mobiles that uses our Iris as
login.

Virtual Reality
• Correct certain phobias
• Correcting social phobias such as public
speaking,
• Treating post-traumatic stress disorders
• Research, education, training
• Surgeons virtual operations

Virtual Reality: current
challenges
• Display technologies
• Reproducing sensations of sound, touch,
and motion

Windowless War Vehicles Will
Show the Outside World Via
Virtual Reality

DARPA's Ground X-Vehicle
• DARPA's Ground X-Vehicle Technologies
(GXV-T) program is an effort to combine
new technologies to improve survivability,
agility, and mobility for the next generation
of military ground vehicles. GXV-T was first
announced in 2014, but now Honeywell has
signed on and is proposing a virtual reality
instrument panel concept, which the
company says could provide drivers with
an enhanced 360-degree view outside the
vehicle.

Challenge
• As the operator moves his head around, he sees the high
resolution inset where his eyes would focus as they
scanned around the cockpit,
• Even so, a camera is not a human eye, which raises a few
interesting challenges. A man can naturally move his
head but stay focused on same object using what's called
the vestibular ocular reflex (the fastest human reflex). But
replicating this virtually, using the near-to-eye inset, can
cause nausea or motion sickness. Honeywell thinks it can
compensate, but it will also have to reduce latency in the
display, which must have very high refresh rate.

మాయాజ0గాలు (Robot Army]

Robots as colleagues instead of work tools
Ergonomic relief for the older staff member. Highly incriminating and
physical tough jobs reducing new tasks and the need for qualifications
(programming?)
Robots as training partner? Or as gateway to inferior jobs

Transformation of the automotive industry

How fast Changes in the century
coming may take place?. Are we
ready?
When the automobile was introduced into the
market, it took 55 years, essentially a lifetime,
until a fourth of U.S. households owned one. It
took about 22 years until 25 percent of U.S.
households owned a radio. The World Wide Web
achieved this penetration in about eight years.
Such acceleration drives an inexhaustible thirst
for innovation and produces competitive
pressures. The spread of education and
technology around the world magnifies these
competitive pressures many fold. However, next
century inventions are going to take very less
time to reach household.

Do you remember weather
forecast of any day in ETV news?
Accurate or vague? Why?
Small Joke on our self: Do take it
in light manner.

UK Experience/Predictions
• Since 1838
• One rain gauge for one mile
• So total data:
• 180x242495x365x24x24
• Higher order Bernoulli Equation solver
• Micro Climate monitoring – an outcome of
Sensor networks

Precision, resolution
• Increasing the grid size
• India – Famous for cooked up data.
• No re-producability of experiments
• How to write a paper? Some one has to
reproduce with the given information by
you.

Quality Control: Usually after
manufacturing the product.
• A bolt example:
• Is it suits to space craft?
• Is it suits to aero plane?
• Is it suits in BMW?
• Is it suits to you a local car?
• Does it fits to a motor bike?
• Does it suits to a cycle?
• If not, recycle it.

Let me share with you the
predications of technological
innovations in the coming
years.

What we have achieved as of
now?
• We are able to transmit messages
• We are able to exchange voices
• We are able to exchange photos, videos
• We are able to transmit smell
• We are able to sense smell around us
• Of course, we do need to achieve
teleportation
• I understand some Israel Scientist
developed means to transfer our kiss!!!

369TB Memory – 5D Technology-
Then no Virtual Memory
concept?

http://www.bbc.com/future/story/2013
0102-tomorrows-world
• predictions.html

Cognitive Sciences
• In 1990 Congress and President George H. W.
Bush proclaimed the beginning of the “Decade of
the Brain,” intended “to enhance public
awareness of the benefits to be derived from
brain research.”
• Last year the Obama administration announced
the Brain Research through Advancing Innovative
Neuro technologies (BRAIN) Initiative, with a
funding level of more than $100 million in 2014. It
joins the Human Brain Project, a $1.6-billion, 10-
year effort funded by the European Union.

https://www.washington.edu/alumni/co
lumns/june98/technology.html

• In future, perhaps many of our appliances
may be powered by the metabolism of our
own bodies.
• It reminds me some telugu cinema, where
Bakta vama deva makes his body as baking
owen(stove) to prepare rotis.

• As a result of a new understandings of how
our bodies work, the better nutrition and a
complete mapping of the human genome,
those that are born near the 22nd century
can expect lifetimes of perhaps several
hundred years.

• Preventive medicine will begin in the womb
with gene therapy. We can expect organ
replacement and repairing of fractured DNA
to be commonplace.

• Sensors and computers will be implanted
within our bodies and embedded within the
very fabric of what we wear, in the walls of
our home and in our places of business.

Money will not be needed
• ... just our physical characteristics act as a
"fingerprint" to signal our identity with
electronic processing of transactions that
automatically adjusts our instantaneous
net worth.

No need of physical prisons!!
• Since we will be able to track the identity of
everybody with sensors within our
environment, the nature of crime will
change ... indeed, prisons as we know them
will become obsolete as we will use new
therapies to rehabilitate.

Do we need to move in future at all?
• Synthesized 3-D spaces.
• Our transportation systems will become
more efficient, and less polluting.

Transportation
• 2075-2100: Faster-than-light travel is developed.
Scientists have selected fusion power and zero-point
energy as the most probable technologies that could
enable spaceships to break the light-speed barrier.
• For example, a 2070s hyper-drive vessel or 2080s warp-
speed ship might reach Alpha Centauri (four light-years
away) in just 30 days, or make the six-month trip to Mars
in three hours. Officials at NASA’s Glenn Research Center
have explored other options to travel faster than light-
speeds and believe that, in a distant future, humans may
even harness wormholes, enabling instant access to vast
distances in space.

• At present, millions of medical devices are implanted in
humans each year. These include pacemakers, blood
vessel replacements, hip joints, eye lens implants,
drainage tubes, heart valves and cochlear implants. The
devices save lives and improve the quality of life. But they
never work as well as the original part being replaced.
Basically, the body views most of the materials we now
use as "foreign objects" and simply walls them off. Thus,
we get aberrant healing and poor mechanical and
electrical communication between the implant and the
body. The path to the future of medical implants demands
that the body recognize these devices as "natural" and
heal them in a facile manner.

• Envision prosthetic limbs that heal into the skin
for a bacterial seal, the bone for mechanical
support and the nerves for control. An artificial
heart that functions about as well as a healthy
natural heart would--extending hundreds of
thousands of lives. A robust artificial pancreas
could improve the quality of life for millions, as
could an electronics-electrode array artificial
eye for the vision impaired. Finally, can "dip-stick"
diagnostic devices be built that offer early home
detection of cancers and other life-threatening
conditions? The potential now exists to engineer
synthetic surfaces so that they control biological
reactions with precision. Thus, we can imagine
creating a new generation of biomaterials that
might revolutionize health care and diagnostics.--
UW Engineered Biomaterials Director Buddy D.
Ratner

• By 2050, bold pioneers begin replacing
their biology with non-biological muscles,
bones, organs, and brains. Non-bio bodies
automatically self-repair when damaged.
In fatal accidents (or acts of violence),
consciousness and memories can be
transferred into a new body, and victims
simply continue life in their new body.
Death is now considered no more
disruptive than a brief mental lapse. Most
patients are not even aware they had died.
Built labor-free with nanofactories, non-bio
body parts are easily affordable.

Sorry Einstein: Biology Replaces Physics
as Science's Top Dog
• Physics, long the dominant determinant of thought and
ideas in science, has been displaced by the biological
sciences which display the extraordinary complexity that
defies or belies many of the ideas promoted by physicists
and chemists through which much of our ideas in the
present century have been promoted. Hence I predict new
modalities of thought in which systems analysis or concepts
involving organized networks of cellular processes will
come to the forefront of the biological sciences. Of course,
early in the next century, much of the so-called Human
Genome Project will have been completed with the
promised "encyclopedia of genetic information". However,
along with that will be the evidence that knowledge of the
genome and its constituent genes does not give knowledge
of how the living cell or organism is constructed and the
multiple types of physiological processes are regulated.
Hopefully the next century will see a more appropriate and
detailed construction of the probabilistic schemes or
networks of the living process rather than the simplistic and

http://www.popularmechanics.com/tec
hnology/a3120/110-predictions-for-the-
next-110-years/

http://www.popularmechanics.com/tec
hnology/a3120/110-predictions-for-the-
next-110-years/
• Digital "ants" will protect the U.S. power grid from
cyber attacks. Programmed to wander networks
in search of threats, the high-tech sleuths in this
software, developed by Wake Forest University
security expert Errin Fulp, leave behind a digital
trail modeled after the scent streams of their
real-life cousins. When a digital ant designed to
perform a task spots a problem, others rush to the
location to do their own analysis. If operators see
a swarm, they know there's trouble.

Your genome will be sequenced before
you are born
• Researchers led by Jay Shendure of the University
of Washington recently reconstructed the
genome of a fetus using saliva from the father
and a blood sample from the mother (which
yielded free-floating DNA from the child). Blood
from the umbilical cord later confirmed that the
sequencing was 98 percent accurate. Once the
price declines, this procedure will allow us to do
noninvasive prenatal testing.

Drugs will be tested on "organ chips"
that mimic the human body
• Now undergoing trials in 15 research
institutions, the new silicon chips feature
channels that house living kidney or lung
cells, above. Simulated blood and oxygen
flow allows them to mirror the actions of
real organs, reducing the need for animal
testing and speeding up drug
development—in the midst of a pandemic,
that would be crucial.

Supercomputers will be the size of
sugar cubes.
• The trick is to redesign the computer chip.
Instead of the standard side-by-side model
in use today, IBM researchers believe they
can stack and link tomorrow's chips via
droplets of nano-particle infused liquid.
This would eliminate wires and draw away
heat. What it won't do is help you remember
where you left your tiny computer before
you went to bed.

Tall Buildings – Sensors are the
ultimate security means.

Jeddah Tower- 1KM originally
planned for 1.6KM height. Saudi
Arabia, ready by 2019.

Floating Cities in the oceans.

Femtoengineering is going to
lead.

http://www.futuretimeline.net/2
2ndcentury/2100-
2149.htm#femtoengineering
• Technology on the scale of quadrillionths of a metre (10-
15) has recently emerged.* This is three orders of
magnitude smaller than pico-technology and six orders of
magnitude smaller than nanotechnology.
• Engineering at this scale involves working directly with
the finest known structures of matter – such as quarks
and strings – to manipulate the properties of atoms. This
development is a further step towards macro-scale
teleportation, i.e. transportation of objects visible to the
naked eye. Significant breakthroughs in anti-gravity and
force field generation will also result from this.

http://www.futuretimeline.net/2
2ndcentury/2100-
2149.htm#femtoengineering
• Another area that will see major progress is in materials technology. For
example, metals will be produced which are capable of withstanding
truly enormous pressures and tensile forces. The applications for this
will be endless, but perhaps one of the most exciting areas will be in the
exploration of hostile environments – such as probes capable of
travelling within the Sun itself, and tunnelling machines that can
penetrate the Earth's crust into the layers of magma beneath. Longer
term, this development will pave the way for interstellar ships and the
massive forces involved in light speed travel.
• Other more exotic materials are becoming possible – including wholly
transparent metals, highly luminous metals, frictionless surfaces, and
ultra dense but extremely lightweight structures. As with many areas of
science, femtoengineering is being guided by advanced AI, which is
now trillions of times more powerful than unaided human intelligence.

Earthquakes and Tsunamis will
be made in human hand!

• By now, geophysicists have mapped the entirety of the Earth's
crust and its faults, extending some 50 km (30 mi) below the
surface. Computer simulations can forecast exactly when and
where an earthquake will occur and its precise magnitude. With
a "scheduling" system now in place, comprehensive
preventative measures can be taken against these disasters.
• For instance, people know when to stay out of the weakest
buildings, away from the bridges most likely to collapse and
otherwise away from anything that might harm them. Rescue
and repair workers can be on duty, with vacations cancelled and
extra workers brought in from other areas. Workers can be
geared up with extra equipment ordered in advance to fix key
structures that may fail in an earthquake. Freeways can be
emptied. Dangerous chemical freight can be prevented from
passing through populated areas during the quake. Aircraft can
be stopped from approaching a potentially damaged runway.
Weak water reservoirs can have their water levels lowered in
advance. Tourists can be made to stay away. All of these

• However, some nations are going one step further and creating
additional systems, in the form of gigantic engineering projects. To
protect the most earthquake-prone regions, a network of "lubrication
wells" is being established. These man-made channels penetrate deep
underground, to the very edge of the mantle. They work by injecting
nanotechnology-based fluid or gel into fault lines, making it easier for
rock layers to slide past each other. Explosive charges can also be
dropped at strategic points, in zones where the lubrication might be less
effective. Instead of sudden, huge earthquakes, the network induces a
series of much smaller earthquakes. Using this method, an earthquake of
magnitude 8.0 can be buffered down to magnitude 4.0 or lower, causing
little or no damage to structures on the surface. In coastal locations,
tsunamis can also be prevented.
• This is a carefully controlled process – requiring heavy use of AI – and is
by no means perfect. There are complex legal and liability issues in the
event of accidents. For instance, damage from human-induced
earthquakes cannot be excused as an "act of God."

Super Computing- Tianhe-2 (33.86Peta
Flops)
• Trinity and Hazel-Hen of Cray

http://www.hpcwire.com/2015/10/05/th
e-revolution-in-the-lab-is-
overwhelming-it/

• An excellent, though admittedly high-
end, example of the growing
complexity of computational tools
being contemplated and developed in
life science research is presented by
the European Union Human Brain
Project[ii] (HBP). Among its lofty goals
are creation of six information and
communications technology (ICT)
platforms intended to enable “large-
scale collaboration and data sharing,
reconstruction of the brain at different
biological scales, federated analysis
of clinical data to map diseases of the
brain, and development of brain-

• The elements of the planned HPC
platform include[iii]:
• Neuroinformatics: a data repository,
including brain atlases.
• Brain Simulation: building ICT models and
simulations of brains and brain
components.
• Medical Informatics: bringing together
information on brain diseases.
• Neuromorphic Computing: ICT that mimics
the functioning of the brain.
• Neurorobotics: testing brain models and
simulations in virtual environments.
• HPC Infrastructure: hardware and software
to support the other Platforms.

16 Massive Scientific Facilities at
the Cutting Edge of Research
http://www.popularmechanics.com/science/g2475/16-
massive-scientific-facilities-at-the-cutting-edge-of-
research/?mag=pop&list=nl_pnl_news&src=nl&date=0223
16

Super-Kamiokande-Neutrons no mass

Super-Kamiokande
• The Super-Kamiokande is a giant neutrino
detector, where thousands of cylinders of water
wait for an incredibly rare event: the annihilation
of a weakly interacting neutrino when it strongly
interacts with regular matter and creates proton
decay. The facility won a Nobel in 2015 for the
discovery that neutrinos had mass, one more step
in understanding how these hard-to-detect
particles affect the universe on larger scales.
• Kamioka Observatory, ICRR, University of Tokyo

Very Large Array- frozen water on Mercury

Very Large Array
• Since 1980, the National Radio Astronomy
Observatory's Very Large Array has tuned in
to distant galaxies, hunted for alien radio
signals, and even discovered things in our
solar system, like frozen water on Mercury.
Each of the 27 radio telescope dishes are
on a track such that they can be moved.
That means they can be grouped together
tightly into a 2000-square foot area or
spread as far apart as 13 miles across.

Large Hadron Collider
• CERN's Large Hadron Collider discovered
the missing particle that gives matter its
mass. And that was just the beginning. The
17 miles of tunnel are operating at higher
power than ever, hunting for particles
never before even theorized, attempting to
solve supersymmetry and maybe, just
maybe, finding evidence of parallel
universes.

LIGO
• In case you missed it, physicists discovered
gravitational waves, finally solving Einstein's
theories and paving the way for brand new
understandings of physics. To do that, two near-
identical observatories in Washington and
Louisiana have two 2.5 mile vacuum tubes, which
fire five laser interferometers each. If those
lasers are disturbed by gravitational waves, LIGO
detects a positive match. And that's exactly how it
caught the whispers of a black hole merger from
1.5 billion years ago.

Tevatron- Large Hadron Collider

Tevatron
• The Large Hadron Collider is the most
powerful particle accelerator in the world.
Fermilab's Tevatron, located in suburban
Chicago, is the the second most powerful.
Operating from 1971 to 2011, the lab was
able to verify CERN's results regarding the
Higgs-Boson, and made countless particle
physics discoveries in its decades of
operation

Arecibo Observatory –Hunt for Aliens

Arecibo Observatory
• Arecibo is the largest single aperture radio
telescope in the world at about 1000 feet
wide, located in the forests of Puerto Rico.
The facility tunes in to pulsars, galaxies,
and other cosmic phenomena, while
occasionally hunting for aliens. Pictured
here is the steering mechanism and
antenna assembly at the top of the dish.

Aperture Spherical Radio
Telescope – for glimpses of Heavens

Aperture Spherical Radio
Telescope
• China is building a 1,650 foot telescope in
the hills of Guizho, a remote
province. Around 10,000 people are being
relocated to give the radio dish a "quiet
zone." The $184 million program is meant to
dwarf Arecibo in size, and provide the
country incredible glimpses of the
heavens—and maybe help them hunt for
technologically advanced aliens.

https://youtu.be/ob5IYlPX89w
High Voltage Marx and Tesla
Generators Research Facility

High Voltage Marx and Tesla
Generators Research Facility
• Russia's premier weapons testing facility
has been in use since the 1970s. This drone
video from last year shows the tall, tall
Tesla towers in all their monstrous glory.
The towers produce intense amounts of
energy to ensure the durability of
insulative materials on aircraft, vehicles,
and weapons.

HAARP - ionosphere observations. Some claim artificial aircraft accidents..

HAARP
• In 2014, the Air Force, Navy, and DARPA pulled out
of the High Frequency Active Auroral Research
Program, transferring it over to the University of
Alaska Fairbanks. For 21 years, it had been making
ionospheric observations in the Alaskan
wilderness. At least, that was the official
government line. A cursory Google search will
yield mostly conspiracy theories ranging from
weather to mind control.
• The facility itself is huge: 180 antennas spread
across 33 acres. All that to either monitor the
ionosphere and test communications capability,
or to enslave us all and cause aircraft accidents

IceCube
• In Antarctica, the IceCube Neutrino Observatory
waits for the passage of neutrinos. Already, it's
found dozens, some from outside our solar
system. 86 holes just like this one were dug, each
about 1.5 miles deep. Neutrino detectors were
placed at the bottom of each hole—the detectors
need to be buried that deep to prevent
interference from other particles passing
through. Operating since 2010 after five years of
construction that could only happen during the
Antarctic summer, the facility has already
expanded our understanding of the ghostly
neutrino particles.

Atacama Large Millimeter Array

Atacama Large Millimeter Array
• A total of 66 radio telescope dishes sit high
up in the mountainous deserts of Chile, far
away from most civilization, allowing it to
be one of the most precise radio astronomy
observatories in the world. Operating since
2013, the observatory has provided
stunning glimpses into our universe's past,
studied comets, and made amazing
observations of planetary formation.

National Ignition Facility-Fusion

National Ignition Facility
• The Lawrence Livermore National
Laboratory is California is home to this 10
story chamber where 192 different lasers
focus in on particles of hydrogen,
attempting to compress them until a fusion
reaction occurs. 500 trillion watts of
energy are aimed toward the small target
in the midst of it all, with the hope being we
could someday get more energy back out
then we put in—the holy grail of fusion.

Facility for Advanced Accelerator
Experimental Tests (FACET) and
Test Beam Facilities

Facility for Advanced Accelerator
Experimental Tests (FACET) and
Test Beam Facilities
• At the SLAC National Accelerator Lab, FACET
explores the cutting edge of plasma
research and provides ultra-hot particle
beams for particle accelerator research.
It's got a lot of punch packed into a facility
the size of a large living room. At peak
power, it can produce 10 trillion watts of
power, or 2.5 billion 9 volt batteries firing
off all at once.

Tianhe-2
• Tianhe-2 is the most powerful
supercomputer in the world. There are a
total of 16,000 nodes in the supercomputer,
which are used to crunch numbers for the
Chinese government and aid in national
security.

Bruce Nuclear Generating
Station

Bruce Nuclear Generating
Station
• Ontario is home to the second largest
nuclear reactor in the world and the largest
currently online, the Bruce Nuclear
Generating Station. This is the vault, the
part of the nuclear generating station
where fission occurs. The plant produces
30 percent of Ontario's energy output.

Aquarius Reef Base
• NASA doesn't just send astronauts high
above the ocean. It also sends them to this
base at the bottom of the coral reef off the
coast of the Florida Keys, where they can
learn to work in tight spaces and extreme
environments. Though NASA utilizes it,
Florida International University currently
owns the base.

Let us have a glance of
developments in MEMS,
Nanotechnology, IOT.

Microelectromechanical Systems (MEMS)
What is MEMS ?
• Imagine a machine so small that it is imperceptible to the human eye.
• Imagine working machines with gears no bigger than a grain of pollen.
• Imagine these machines being batch fabricated tens of thousands at a
time, at a cost of only a few pennies each.
• Imagine a realm where the world of design is turned upside down, and
the seemingly impossible suddenly becomes easy – a place where
gravity and inertia are no longer important, but the effects of atomic
forces and surface science dominate.
Source: Sandia National Laboratories, Intelligent Micromachine Initiative (www.mdl.sandia.gov/mcormachine)
154

MEMS THE ENGINE OF INNOVATION AND NEW
ECONOMIES
• “These micromachines have the potential to revolutionize the world
the way integrated circuits did”.
Linton Salmon, National Science Foundation
• “Micromachining technology has the potential to change the world in
some very important ways, many of which are not possible to foresee
at this time, in the same way that standard IC technology has so
revolutionized our lives and economies”.
Ray Stata, Chairman and CEO, Analog Devices, Inc.
155

MEMS TECHNOLOGY
• Creates Integrated Electromechanical Systems that merge computin
with sensing and actuation.
• Mechanical components have dimensions in microns and numbers i
millions.
• Uses materials and processes of semiconductor electronics.
• Wide applications in commercial, industrial and medical systems :
Automobiles
Wearable Sensors to Monitor Vital Biological Functions
Cell Phones
Printers
GPS/Navigation Systems etc.,
Key Characteristics: Miniaturization (small size and weight), Multiplicit
(batch processing), Microelectronics, Small Cost, High Reliability.
156

APPLICATIONS OF MEMS
Inertial Measurement:
Automotive Safety
Aircraft Navigation
Platform Stabilization
Personal/Vehicle Navigation
Distributed Sensing and Control:
Condition-Based Maintenance
Situational Awareness
Miniature Analytic Instruments
Environmental Monitoring
Biomedical Devices
Active Structures
Information Technology:
Mass Data Storage & Displays
157

APPLICATIONS OF MEMS
Automotive: Industrial:
Yaw Sensors Factory Automation
Gyroscopes Office Automation
Accelerometers Process Control
Airbag Sensors
Telecommunications : Medical:
Antenna Stabilization Blood Analysis
GPS/Navigation DNA Analysis
Wireless Communication Virtual Reality
158

NANOTECHNOLOGY
The NNI defines Nanotechnology as consisting of all of the
following:
• Research & technology development at the 1-to-100nm range.
• Creating & using structures that have novel properties because of their
small size.
• Ability to control/manipulate at atomic scale.
Reference: Nanotechnologyfor Dummies by Richard Booker and Earl Boysen, Wiley Publishing, Inc.
159

NANOTECHNOLOGY (Continued)
KEY Elements of Nanotechnology:
• Buckyball- A soccer-ball shaped molecule made of 60 carbon atoms
Applications: Composite reinforcement, drug delivery.
• Carbon Nanotube: A sheet of graphite rolled into a tube. Applications
Composite reinforcement, conductive wire, fuel cells, high-resolutio
displays.
• Quantum Dot: A semiconductor nanocrystal whose electrons show
discrete energy levels, much like an atom. Applications: Medical imaging
energy-efficient light bulbs.
• Nanoshell: A nanoparticle composed of a silica core surrounded by a gol
coating. Applications: Medical imaging, cancer therapy.
160

Typical Applications of Nanotechnology:
• Single-electron transistor (SET): Uses a single electron to indicate whether it represents a 1
or a 0, thereby greatly reducing the energy required to run a processor and limiting the heat
levels generated during operation.
• Magnetic random-access memory (MRAM): Non-volatile electronic memory that is faster &
uses less energy than conventional Dynamic RAM.
• Spintronics: “Spin-based electronics,” uses electron’s spin & its charge to represent binary
1s & 0s.
• Quantum Computing: Unlike a conventional computer it uses quantum mechanical
properties of superposition & entanglement to perform operations on data & will rely on
probability (in effect, “it is highly likely that the answer is….”). The QC will run in parallel,
performing many operations at once.
162

Typical Applications of Nanotechnology (contd)
• Quantum cryptography: Based on traditional key-based crypt., using
unique properties of quantum mechanics to provide a secure key
exchange.
• Photonic crystals: Nano crystals that guide photons according to
structural properties (optical router for Internet info. exchange).
• Other: Cell phones with longer battery life, smaller & more accurate GPS,
faster & smaller computers, smaller & more efficient memory, smart
materials, fast & accurate DNA fingerprinting, medical diagnostics &
drug delivery, etc.
Reference: Nanotechnology for Dummies by Richard Booker and Earl Boysen, Wiley Publishing, Inc.
163

• Improved (Nano-engineered
cementitious?) Materials with increased
strength, energy efficiency,
environmentally friendly…

Modern Mobile with number of
Sensors.

167
Study: Intelligent Cars Could Boost Highway Capacity by
273%
Tue, September 04, 2012 IEEE Spectrum Inside Technology
Highway Capacity Benefits from Using Vehicle-to-Vehicle Communication and Sensors for Collision
Avoidance, by Patcharinee Tientrakool, Ya-Chi Ho, and Nicholas F. Maxemchuk from Columbia
University, was presented last year at the IEEE Vehicular Technology Conference.

Automation steps of the vehicle
168

Combining vehicle networking with global infrastructure
169

A Self-Driving, Hybrid Flying Car
TF-X Is (Supposedly) Almost
Ready To Take Flight

Experimental verification of novel formulations, with 21st century
laboratory facilities, modern sensor technology.
Design wireless sensor networks for in-situ structural health
monitoring and warning systems (Minneapolis Bridge Collapse)

Improve understanding of damage/deterioration of structures
based on novel structural mechanics formulations for large
deformation and nonlinear behavior With modern high-
performance computational hardware, can a 3D solid mechanics
based framework provide more insights into failure dynamics
than a structural element based framework?

https://www.youtube.com/embe
d/KeTizNY0zDA
https://youtu.be/gSdQyVNUvTc
Car crash testing
Sensor Developments helps further.

Soldiers of 2025 and beyond may wear sensors to help detect and prevent
threats such as dehydration, elevated blood pressure and cognitive delays
from lack of sleep. There are sensors in imaging, motion detection, radar,
chemical-biological detection and more. At the end of the day, sensors are
all about collecting data."

DVE(Degraded Visual
Environments)
One critical area of research is enhancing air
operations in degraded visual environments,
known as DVE. At the Aviation and Missile
Research, Development and Engineering Center at
Redstone Arsenal, Alabama, Army engineers are
advancing and implementing new technologies.
One research program fuses images of multiple
sensor technologies such as radar, infrared, and
laser detection and ranging, also known as lidar.
Each of these sensor technologies provide unique
advantages for operating in various types of DVE
conditions.

3-D Capture in Mobile using
Stereo Vision?

Wisonsin Introduction to
Engineering Course on Society’s
Engineering Grand Challenges
Focus on the following themes, ordered by scale:
1) Engineering challenges that impact our lives on
a personal scale,
2) Engineering for the developing world,
3)Engineering the megacity,
4) Global engineering challenges, and
5) Engineering challenges beyond Planet Earth.

MIT Online Course On
Computational Thinking and Data
Science
• Topics covered include:
• -Random walks
• -Probability, Distributions
• -Monte Carlo simulations
• -Curve fitting
• -Knapsack problem, Graphs and graph optimization
• -Machine learning basics, Clustering algorithms
• -Statistical fallacies

IOT
The “Internet of Things” (or “Internet of
Everything”), which is expected to connect a
trillion devices in our homes, buildings, cars, and
even bodies to monitor our health, our
environment, and our resources, presents major
challenges: Current devices and systems
consume too much power; a trillion devices
cannot all be battery-powered; how do we design
and manufacture millions of different things; a
trillion devices form a large “attack surface.”

Data science aims to use the
different data sources described
above to answer questions
grouped into the following four
categories:
• Reporting: What happened?
• Diagnosis: Why did it happen?
• Prediction: What will happen?
• Recommendation: What is the best that
can happen?

Wikipedia definition of Data
Science
Data science incorporates varying
elements and builds on techniques and
theories from many fields, including
mathematics, statistics, data
engineering, pattern recognition and
learning, advanced computing,
visualization, uncertainty modeling, data
warehousing, and high performance
computing with the goal of extracting
meaning from data and creating data
products.

The Dawn of Data Science
discipline.
Just like computer science emerged
as a new discipline from mathematics
when computers became abundantly
available, we now see the birth of
data science as a new discipline
driven by the torrents of data
available today. We believe that the
data scientist will be the engineer of
the future.

Data Growth
• Stone age to 2003- 5 Exa-bytes
• In 2011, every two days 5 Exa-bytes
• In 2013, every ten minutes 5 Exa-bytes

Data Science is to give value to
data.
“If you're not paying for the product, you are the
product!" is used to make internet users aware of
the value of information. Organizations like
Google, Facebook, and Twitter are spending
enormous amounts of money on maintaining an
infrastructure. Yet, end-users are not directly
paying for it. Instead they are providing content
and are subjected to advertisements. This means
that other organizations are paying for the costs
of maintaining the infrastructure in exchange for
end-user data. The internet is enabling new
business models relying on data science.

Select Your Favorite Heroine.
Send SMS to 56556/57!!!
• Kajal Agarwal
• Samantha
• Sruthi Hasan
• Rasi Khanna
• Milky beauty Tamanna

Data Scientists
Data scientists are the
people who understand how
to fish out answers to
important business
questions from today's
tsunami of unstructured
information

Data mining is defined as the
analysis of (often large) data sets to
find unsuspected relationships and
to summarize the data in novel ways
that are both understandable and
useful to the data owner. The input
data is typically given as a table and
the output may be rules, clusters,
tree structures, graphs, equations,
patterns, etc.

Data Scientist-The sexiest job of
21st Century.

Birds view of Statistics – I am
afraid, I am trying to wake up a beast
(statistics) with a small stick.
Do not take it like:
కొ0డను త్రవ్వి ఎలుకను
పట్టినట్లు అనుకోవద్ుు

Statistical Research Methods
• Distributions
• Preparing graphs
• Hypothesis testing
• Regression- simple, multiple
• Multivariate Statistics
• Exploratory Data Analysis
• Sampling
• Stochastic analysis
• Time series analysis
• Spatial Statistics

How IOT, Sensor Networks, New
Sensors are opening doors for
research?
• Use of sensors increases in Engineering
Research which necessitates more or
extensive (detailed) data analysis.
• For example, we need to compare
sensitivity of two or more sensors from two
different companies. We carry
measurements with both and statistically
analyze whether they are same or different.

Feature Extraction/Selection
• We often required to analyze dependence
of measurements which may allow us to
reduce redundancy in number of sensors,
type of sensors.
• We often encounter need to compare
measurements of a set of sensors with
another set of sensors (which may be
spatially located else where or
chronologically].

Soft Sensors
• Soft sensor is a common name for software where several
measurements are processed together. There may be
dozens or even hundreds of measurements. The
interaction of the signals can be used for calculating new
quantities that can not be measured.
• Soft sensors or inferential calculators are operators’
virtual eyes. Soft sensors create windows to a process
where physical equivalents are unrealistic or even
impossible.
• Sensor output can be a control signal, advisory
information for operators, predictions of product quality,
information on process faults or outliers in data.

E-Nose and E-Tongue
• The e-tongue uses a range of sensors that
respond to salts, acids, sugars, bitter compounds,
etc. and sends signals to a computer for
interpretation. The interpretation of the complex
data sets from e-nose and e-tongue signals is
accomplished by use of multivariate statistics
including principal component analyses such as
(PCA), linear discriminant analysis (LDA),
discriminant function analysis (DFA), hierarchical
cluster analysis (HCA), soft independent
modeling of class analogy (SIMCA) and partial
least squares (PLS).

Comparing measurements of a
sensory networks in time
(Chronologically)

Comparing measurements of a
sensory system with other one
elsewhere.

Identifying patterns in the
behavior of collection of
sensors.

Studying behavior of sensors
under extreme conditions.

• Non-Destructive testing
• Condition monitoring

Intelligent buildings – Sensors
for prediction of earth quakes in
advance using nano-sensors.

If your experiment needs
statistics, you ought to
have done a better
experiment.
Ernest Rutherford (1871-1937)

“To call in the statistician
after the experiment is done
may be no more than asking
him to perform
a postmortem examination:
he may be able to say what
the experiment died of.”
Ronald Aylmer Fisher (1890 - 1962)

“He uses statistics as a
drunken man uses lamp
posts -- for support rather
than illumination.”
Andrew Lang (1844-1912)

Frequent problems which people
encounter while analyzing their
observations?
• Scaling
• Graphing
• Interpretation

Histogram of Music Experiment Data
5 10 15 20
Performance Score
0
2
4
6
Count
Control Training
5 10 15 20
Performance Score
The data from our
experiment are
represented here in
histograms
Notice here that the
bins are simply a
proportion of the total
range – in this case
1/11
This proportion can
be varied when
compiling a histogram
and can make a big
difference to the
appearance of the
data
Because the data represented on the x axis
are continuous, the actual number of and size
of the bins can be varied infinitely, though not
all combinations produce sensible graphs

Histogram of Music Experiment Data
5 10 15 20
Performance Score
0
2
4
6
Count
Control Training
5 10 15 20
Performance Score
Remember how, in
the basic distribution
plots, the best
participant was in the
control group, and
the worst was in the
training group
These values seem
atypical of their
groups
They can also be
seen when the data
is graphed as a
histogram.

Stem & Leaf Plots
Performance Score Stem-and-Leaf Plot for
Group= Control
Frequency Stem & Leaf
1.00 5 . 0
2.00 6 . 00
2.00 7 . 00
4.00 8 . 0000
3.00 9 . 000
3.00 10 . 000
3.00 11 . 000
1.00 12 . 0
1.00 Extremes (>=20.0)
Stem width: 1
Each leaf: 1 case(s)
These are the ‘stems’
The stem width indicates the size of
each category, in this case, 1
Here ‘extremes’ refer to outliers: in
this example there is 1
These are the ‘leaves’. Each leaf is
composed of a single number for
every value that falls in the range of
that ‘stem’. The number used is taken
from the next figure in the actual
value: e.g. for value 8.0, the stem is
8, the leaf is 0.
A useful type of plot for small data sets
Example (left) as generated by SPSS

223
Histograms
An important variation of the histogram is the Pareto
chart. This chart is widely used in quality and process
improvement studies where the data usually represent
different types of defects, failure modes, or other categories
of interest to the analyst. The categories are ordered so that
the category with the largest number of frequencies is on
the left, followed by the category with the second largest
number of frequencies, and so forth.

225
Box Plots
• The box plot is a graphical display that simultaneously
describes several important features of a data set, such as
center, spread, departure from symmetry, and
identification of observations that lie unusually far from
the bulk of the data.
•Interquartile Range (IQR=Q3-Q1)
• Whisker
• Outlier
• Extreme outlier

226
Box Plots—five point summary

The mean as the mathematical
‘balance point’
•
• • •
• • • • •
• • • • • • •
0 1 2 3 4 5 6
X = 3

The mean is affected by outliers
•
• • •
• • • • •
• • • • • • •
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
X =
(0+1+1+2+2+2+3+3+3+3+4+4+4
+5+5+16)/16 = 3.625

Variance
 2
  XX
 
1
2
2




n
XX
S
Squared sum of
deviations
Squared sum of
deviations divided by
number of observations
(minus 1)
= 40
= 40/15 = 2.67
The variance is calculated by calculating an average from the squared sum of
deviations
Variance = 2.67

Variance
 2
  XX
 
1
2
2




n
XX
S
Squared sum of
deviations
Squared sum of
deviations divided by
number of observations
(minus 1)
= 40
= 40/15 = 2.67
The variance is calculated by calculating an average from the squared sum of
deviations
Variance = 2.67
Why number minus 1 (n-1) and not n?
n for populations
But n-1 for samples, when using samples to make estimates about
populations
In a sample we assume that the mean of the sample is equivalent to
the population mean that we’re interested in
Imposing this constraint means that one parameter is fixed and
cannot vary, and hence n-1 produces a better estimate of the
population variance
A really good explanation can be found on p129 (Chapter 4) of Field
and Hole
This is quite complex theoretical stuff, it’s OK for now just to accept it

Calculating the variance
• Sample Data: from ‘control condition’
– First, calculate the mean
n
X
X
 = 20
2012111111101010999888877665 
= 9.25

– Then, calculate the deviations from the mean for
each value
Value Mean Calculation Deviation
5 9.25 5-9.25 = -4.25
6 9.25 6-9.25 = -3.25
6 9.25 6-9.25 = -3.25
7 9.25 7-9.25 = -2.25
7 9.25 7-9.25 = -2.25
8 9.25 8-9.25 = -1.25
.. .. .. ..
.. .. .. ..
12 9.25 12-9.25 = 2.75
20 9.25 20-9.25 = 10.75

– Then, calculate the squared deviations
Value Mean Calculation Deviation Squared
Deviation
5 9.25 5-9.25 = -4.25 18.06
6 9.25 6-9.25 = -3.25 10.56
6 9.25 6-9.25 = -3.25 10.56
7 9.25 7-9.25 = -2.25 5.06
7 9.25 7-9.25 = -2.25 5.06
8 9.25 8-9.25 = -1.25 1.56
.. .. .. .. ..
.. .. .. .. ..
12 9.25 12-9.25 = 2.75 7.56
20 9.25 20-9.25 = 10.75 115.56

– Then, sum the squared deviations
Value Mean Calculation Deviation Squared
Deviation
5 9.25 5-9.25 = -4.25 18.06
6 9.25 6-9.25 = -3.25 10.56
6 9.25 6-9.25 = -3.25 10.56
7 9.25 7-9.25 = -2.25 5.06
7 9.25 7-9.25 = -2.25 5.06
8 9.25 8-9.25 = -1.25 1.56
.. .. .. .. ..
.. .. .. .. ..
12 9.25 12-9.25 = 2.75 7.56
20 9.25 20-9.25 = 10.75 115.56
Sum = 0 189.75

– Finally, divide the sum of the squared deviations
by n-1 (i.e. the number of observations -1)
9.99
19
189.752
S
 
1
2
2




n
XX
S
Sum of squared deviations

238
Standard Deviation
• The simple range statistic has the merit of
being in the same units as the raw data.
• The variance, since it is based on the
squares of the deviations, is in squared
units and is therefore difficult to interpret,
it doesn’t make much intuitive sense.
• If you take the (positive) square root of the
variance, you have the standard deviation,
which is in the original units of

239
Standard Deviation
• The simple range statistic has the merit of
being in the same units as the raw data.
• The variance, since it is based on the
squares of the deviations, is in squared
units and is therefore difficult to interpret.
• If you take the (positive) square root of the
variance, you have the standard deviation,
which is in the original units of
measurement.
Remember that the deviations were squared to remove the problem
of them summing to 0

240
Standard Deviation
 
1
2




n
XX
S
 
1
2
2




n
XX
S
Variance Standard Deviation
9.99
19
189.752
S 3.16
19
189.75
S

Standard Deviation
• The square root operation translates the
spread described by the variance back to
the original units of measurement.
• It may be helpful to think of the standard
deviation as an ‘average of the deviations
from the average’
– for the reasons described previously this is not
entirely accurate mathematically – it is not the
mean of mean deviations

Standard Deviation
Going back to these examples:
control group: s.d. = 2.534
And for training: s.d. = 0.795
The s.d. for the control group is much
greater than that for the training
group, indicating much more spread6 8 10 12 14
Performance Score
0
4
8
12
Count
Control Training
6 8 10 12 14
Performance Score

Standard DeviationS.D. is based on all the values in a
data set, and hence a much more
accurate measure.
It is still influenced by outliers, but it is
far less influenced by extreme
maxima or minima than the range.
As in the case of the original music
study data
Control s.d. = 3.16
Training s.d. = 3.28
Without outliers:
Control s.d. =1.95
Training s.d. =2.36
5 10 15 20
Performance Score
0
2
4
6
Count
Control Training
5 10 15 20
Performance Score

What is the physical
interpretation of standard
deviation?

Important features of the Student’s t
distribution
• Use of the t statistic assumes that the
parent distribution is Gaussian
• The degree to which the t distribution
approximates a Gaussian distribution
depends on N (the degrees of freedom)
• As N gets larger (above 30 or so), the
differences between t and z become
negligible

Application of Student’s t
distribution to a sample mean
• The Student’s t statistic can also be used to
analyze differences between the sample
mean and the population mean:








N
s
x
t
)( 

Comparison of Student’s t and
Gaussian distributions
• Note that, for a sufficiently large N (>30), t
can be replaced with z, and a Gaussian
distribution can be assumed

Exercise
• The mean age of the 20 participants in one
workshop is 27 years, with a standard
deviation of 4 years. Next door, another
workshop has 16 participants with a mean
age of 29 years and standard deviation of 6
years.
• Is the second workshop attracting older
technologists?

Preliminary analysis
• Is the population Gaussian?
• Can we use a Gaussian distribution for our
sample?
• What statistic should we calculate?

Solution
First, calculate the t statistic for the two
means:
19.1
16
4
20
6
)2729(
)()(
22
2
2
2
1
2
1
21
2
2
1
1
21


























N
s
N
s
xx
N
s
N
s
xx
t

Solution, cont.
Next, determine the degrees of freedom:
N N Ndf   
  

1 2 2
16 20 2
34

Statistical Tables
df t0.050 t0.025 t0.010
- - - -
34 1.645 1.960 2.326
- - - -

Conclusion
Since 1.16 is less than 1.64 (the t value
corresponding to 90% confidence limit),
the difference between the mean ages for
the participants in the two workshops is
not significant

The Paired t Test
Suppose we are comparing two sets of data
in which each value in one set has a
corresponding value in the other. Instead
of calculating the difference between the
means of the two sets, we can calculate the
mean difference between data pairs.

Instead of:
we use:
to calculate t:
( )x x1 2


N
i
ii xx
N
xx
1
2121 )(
1
)(
t
x x
s
N
d

( )1 2
2

Advantage of the Paired t
• If the type of data permit paired analysis,
the paired t test is much more sensitive
than the unpaired t.
• Why?

Applications of the Paired t
• Method correlation
• Comparison of therapies

The 2 (Chi-square) Distribution
There is a general formula that relates actual
measurements to their predicted values


2
2
2
1




[ ( )]y f xi i
ii
N

The 2 (Chi-square) Distribution
A special (and very useful) application of the
2 distribution is to frequency data
2
2
1




( )n f
f
i i
ii
N

Exercise
• In your hospital, you have had 83 cases of
iatrogenic strep infection in your last 725
patients. St. Elsewhere, across town,
reports 35 cases of strep in their last 416
patients.
• Do you need to review your infection
control policies?

Analysis
If your infection control policy is roughly as
effective as St. Elsewhere’s, we would
expect that the rates of strep infection for
the two hospitals would be similar. The
expected frequency, then would be the
average
83 35
725 416
118
1141
01034


  .

Calculating 2
First, calculate the expected frequencies at
your hospital (f1) and St. Elsewhere (f2)
f cases
f cases
1
2
725 01034 75
416 01034 43
  
  
.
.

Calculating 2
Next, we sum the squared differences
between actual and expected frequencies
 2
2
2 2
83 75
75
35 43
43
2 34








( )
( ) ( )
.
n f
f
i i
ii

Degrees of freedom
In general, when comparing k sample
proportions, the degrees of freedom for 2
analysis are k - 1. Hence, for our problem,
there is 1 degree of freedom.

Conclusion
• A table of 2 values lists 3.841 as the 2
corresponding to a probability of 0.05.
• So the variation (2=2.34)between strep
infection rates at the two hospitals is
within statistically-predicted limits, and
therefore is not significant.

The F distribution
• The F distribution predicts the expected
differences between the variances of two
samples
• This distribution has also been called
Snedecor’s F distribution, Fisher
distribution, and variance ratio distribution

The F distribution
The F statistic is simply the ratio of two
variances
(by convention, the larger V is the numerator)
F
V
V
 1
2

Applications of the F distribution
There are several ways the F distribution
can be used. Applications of the F statistic
are part of a more general type of
statistical analysis called analysis of
variance (ANOVA). We’ll see more about
ANOVA later.

Example
• You’re asked to do a “quick and dirty”
correlation between three whole blood
glucose analyzers. You prick your finger
and measure your blood glucose four times
on each of the analyzers.
• Are the results equivalent?

Data
Analyzer 1 Analyzer 2 Analyzer 3
71 90 72
75 80 77
65 86 76
69 84 79

Analysis
The mean glucose concentrations for the
three analyzers are 70, 85, and 76.
If the three analyzers are equivalent, then we
can assume that all of the results are drawn
from a overall population with mean  and
variance 2.

Analysis, cont.
Approximate  by calculating the mean of the
means:
70 85 76
3
77
 


Analysis, cont.
Calculate the variance of the means:
Vx 
    

( ) ( ) ( )70 77 85 77 76 77
3
38
2 2 2

Analysis, cont.
But what we really want is the variance of
the population. Recall that:


x
N


Analysis, cont.
Since we just calculated
we can solve for 
Vx x 2
38
V
N N
N
x x
x
  

 

 
    

 
 
2
2 2
2 2
4 38 152

Analysis, cont.
• So we now have an estimate of the
population variance, which we’d like to
compare to the real variance to see
whether they differ. But what is the real
variance?
• We don’t know, but we can calculate the
variance based on our individual
measurements.

Analysis, cont.
If all the data were drawn from a larger
population, we can assume that the variances
are the same, and we can simply average the
variances for the three data sets.
V V V1 2 3
3
144
 
 .

Analysis, cont.
Now calculate the F statistic:
F  
152
14 4
10 6
.
.

Conclusion
A table of F values indicates that 4.26 is the
limit for the F statistic at a 95% confidence
level (when the appropriate degrees of
freedom are selected). Our value of 10.6
exceeds that, so we conclude that there is
significant variation between the analyzers.

Analysis of paired data
• For certain types of laboratory studies, the
data we gather is paired
• We typically want to know how closely the
paired data agree
• We need quantitative measures of the
extent to which the data agree or disagree?

Examples of paired data
• Method of correlation

Correlation
0 5 10 15 20 25 30 35 40 45 50
0
5
10
15
20
25
30
35
40
45
50

Linear regression (least squares)
Linear regression analysis generates an
equation for a straight line
y = mx + b
where m is the slope of the line and b is the
value of y when x = 0 (the y-intercept).
The calculated equation minimizes the
differences between actual y values and the
linear regression line.

Correlation
0 5 10 15 20 25 30 35 40 45 50
0
5
10
15
20
25
30
35
40
45
50
y = 1.031x - 0.024

Covariance
Do x and y values vary in concert, or
randomly?
cov( , ) ( )( )x y
N
y y x xi i
i
  
1

• What if y increases when x increases?
• What if y decreases when x increases?
• What if y and x vary independently?
cov( , ) ( )( )x y
N
y y x xi i
i
  
1

Covariance
It is clear that the greater the covariance, the
stronger the relationship between x and y.
But . . . what about units?
e.g., if you measure glucose in mg/dL, and I
measure it in mmol/L, who’s likely to have
the highest covariance?

The Correlation Coefficient

   

 
 
   
cov( , )
( )( )
x y N
y y x x
x y
i i
i
y x
1
1 1

The Correlation Coefficient
• The correlation coefficient is a unit less
quantity that roughly indicates the degree
to which x and y vary in the same direction.
•  is useful for detecting relationships
between parameters, but it is not a very
sensitive measure of the spread.

Correlation
0 5 10 15 20 25 30 35 40 45 50
0
5
10
15
20
25
30
35
40
45
50
y = 1.031x - 0.024
 = 0.9986

Correlation
0 5 10 15 20 25 30 35 40 45 50
0
5
10
15
20
25
30
35
40
45
50
y = 1.031x - 0.024
 = 0.9894

Standard Error of the Estimate
The linear regression equation gives us a
way to calculate an “estimated” y for any
given x value, given the symbol ŷ (y-hat):
y mx b 

Now what we are interested in is the average
difference between the measured y and its
estimate, ŷ :
s
N
y yy x i i
i
/ ( ) 
1 2

Correlation
0 5 10 15 20 25 30 35 40 45 50
0
5
10
15
20
25
30
35
40
45
50
y = 1.031x - 0.024
 = 0.9986
sy/x=1.83

Correlation
0 5 10 15 20 25 30 35 40 45 50
0
5
10
15
20
25
30
35
40
45
50
y = 1.031x - 0.024
 = 0.9894
sy/x = 5.32

If we assume that the errors in the y
measurements are Gaussian (is that a safe
assumption?), then the standard error of
the estimate gives us the boundaries
within which 67% of the y values will fall.
2sy/x defines the 95% boundaries..

Limitations of linear regression
• Assumes no error in x measurement
• Assumes that variance in y is constant
throughout concentration range

Alternative approaches
• Weighted linear regression analysis can
compensate for non-constant variance
among y measurements
• Deming regression analysis takes into
account variance in the x measurements
• Weighted Deming regression analysis
allows for both

Evaluating method performance
• Precision
• Sensitivity
• Linearity

Limitation of linear regression
method
If the analytical method has a high
variance (CV), it is likely that small
deviations from linearity will not be
detected due to the high standard error of
the estimate

Ways to evaluate linearity
• Visual/linear regression
• Quadratic regression

Quadratic regression
Recall that, for linear data, the relationship
between x and y can be expressed as
y = f(x) = a + bx

A curve is described by the quadratic
equation:
y = f(x) = a + bx + cx2
which is identical to the linear equation
except for the addition of the cx2 term.

It should be clear that the smaller the x2
coefficient, c, the closer the data are to
linear (since the equation reduces to the
linear form when c approaches 0).
What is the drawback to this approach?

Ways to evaluate linearity
• Visual/linear regression
• Quadratic regression
• Lack-of-fit analysis

Lack-of-fit analysis
• There are two components of the variation
from the regression line
– Intrinsic variability of the method
– Variability due to deviations from linearity
• The problem is to distinguish between these
two sources of variability
• What statistical test do you think is
appropriate?

The ANOVA technique requires that method
variance is constant at all concentrations.
Cochran’s test is used to test whether this is
the case.
V
V
pL
i
i

 05981 0 05. ( . )

Lack-of-fit method calculations
• Total sum of the squares: the variance
calculated from all of the y values
• Linear regression sum of the squares: the
variance of y values from the regression
line
• Residual sum of the squares: difference
between TSS and LSS
• Lack of fit sum of the squares: the RSS
minus the pure error (sum of variances)

• The LOF is compared to the pure error to give
the “G” statistic (which is actually F)
• If the LOF is small compared to the pure error,
G is small and the method is linear
• If the LOF is large compared to the pure error,
G will be large, indicating significant
deviation from linearity

Significance limits for G
• 90% confidence = 2.49

Evaluating Clinical Performance of
laboratory tests
• The clinical performance of a laboratory
test defines how well it predicts disease
• The sensitivity of a test indicates the
likelihood that it will be positive when
disease is present

Clinical Sensitivity
If TP as the number of “true positives”, and FN
is the number of “false negatives”, the
sensitivity is defined as:
Sensitivity
TP
TP FN


100

Example
Of 25 admitted cocaine abusers, 23 tested
positive for urinary benzoylecgonine and 2
tested negative. What is the sensitivity of
the urine screen?
23
23 2
100 92%

 

laboratory tests
• The clinical performance of a laboratory test
defines how well it predicts disease
• The sensitivity of a test indicates the
likelihood that it will be positive when
disease is present
• The specificity of a test indicates the
likelihood that it will be negative when
disease is absent

Clinical Specificity
If TN is the number of “true negative”
results, and FP is the number of falsely
positive results, the specificity is defined
as:
Specificity
TN
TN FP


100

Example
What would you guess is the specificity of
any particular clinical laboratory test?
(Choose any one you want)

Answer
Since reference ranges are customarily set
to include the central 95% of values in
healthy subjects, we expect 5% of values
from healthy people to be “abnormal”--this is
the false positive rate.
Hence, the specificity of most clinical tests is
no better than 95%.

Sensitivity vs. Specificity
• Sensitivity and specificity are inversely
related.

Sensitivity vs. Specificity
• Sensitivity and specificity are inversely
related.
• How do we determine the best compromise
between sensitivity and specificity?

laboratory tests
• The sensitivity of a test indicates the likelihood that
it will be positive when disease is present
• The specificity of a test indicates the likelihood that
it will be negative when disease is absent
• The predictive value of a test indicates the
probability that the test result correctly classifies a
patient

Predictive Value
The predictive value of a clinical laboratory
test takes into account the prevalence of a
certain disease, to quantify the probability
that a positive test is associated with the
disease in a randomly-selected individual, or
alternatively, that a negative test is associated
with health.

Illustration
• Suppose you have invented a new screening
test for Addison disease.
• The test correctly identified 98 of 100 patients
with confirmed Addison disease (What is the
sensitivity?)
• The test was positive in only 2 of 1000 patients
with no evidence of Addison disease (What is
the specificity?)

Test performance
• The sensitivity is 98.0%
• The specificity is 99.8%
• But Addison disease is a rare disorder--
incidence = 1:10,000
• What happens if we screen 1 million
people?

Analysis
• In 1 million people, there will be 100 cases of
Addison disease.
• Our test will identify 98 of these cases (TP)
• Of the 999,900 non-Addison subjects, the test
will be positive in 0.2%, or about 2,000 (FP).

Predictive value of the positive test
The predictive value is the % of all positives
that are true positives:
PV
TP
TP FP
 






100
98
98 2000
100
4 7%.

What about the negative predictive
value?
• TN = 999,900 - 2000 = 997,900
• FN = 100 * 0.002 = 0 (or 1)
PV
TN
TN FN
 






100
997 900
997 900 1
100
100%
,
,

Summary of predictive value
Predictive value describes the usefulness
of a clinical laboratory test in the real
world.
Or does it?

Lessons about predictive value
• Even when you have a very good test, it is
generally not cost effective to screen for
diseases which have low incidence in the
general population. Exception?
• The higher the clinical suspicion, the better
the predictive value of the test. Why?

Efficiency
We can combine the PV+ and PV- to give a
quantity called the efficiency:
The efficiency is the percentage of all
patients that are classified correctly by the
test result.
Efficiency
TP TN
TP FP TN FN


  
100

Efficiency of our Addison screen
98 997 900
98 2000 997 900 2
100 998%

  
 
,
,
.

332
Normal Distribution &
Multivariate Normal Distribution
• For a single variable, the normal density
function is:
• For variables in higher dimensions, this
generalizes to:
where the mean  is now a d-dimensional vector,
 is a d x d covariance matrix and || is the determinant of :

Principal Component Analysis
• Given N data vectors from k-dimensions, find c <= k orthogonal
vectors that can be best used to represent data
– The original data set is reduced to one consisting of N data
vectors on c principal components (reduced dimensions)
• Each data vector is a linear combination of the c principal component
vectors
• Works for numeric data only
• Used when the number of dimensions is large

X1
X2
Y1
Y2

Aimed at finding new co-ordinate system
which has some characteristics.
M=[4.5 4.25 ]
Cov Matrix [ 2.57 1.86 ]
[ 1.86 6.21]
Eigen Values = 6.99, 1.79
Eigen Vectors = [ 0.387 0.922 ]
[ -0.922 0.387 ]

However in some cases it is not
possible to have PCA working.

Unlike PCA which takes global mean and
covariance, this takes between the group
and within the group covariance matrix and
the calculates canonical axes.

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