NANOTECHNOLOGY

Topic:Characteristics of NANOTECHNOLOGY
Nanotechnology (NT) is the production and
use of materials with purposely engineered
features close to the atomic or molecular scale.
NT deals with putting things together atomby-
atom and with structures so small they are
invisible to the naked eye. It provides the ability
to create materials, devices and systems with
fundamentally new functions and properties.
The promise of NT is enormous. It has
implications for almost every type of manufacturing
process and product. Potential NT
applications in the next few decades could produce
huge increases in computer speed and
storage capacity, therapies for several different
types of cancer, much more efficient lighting
and battery storage, a major reduction in the
cost of desalinating water, clothes that never
stain and glass that never needs cleaning.While
the benefits are almost limitless, they will be
realized only if the potential adverse effects of
NT are examined and managed.
NT is new, but the effort to understand and
manage its effects will be long-term. As the
world community tries to reduce the adverse
effects of the technology, our understanding of
these effects will steadily increase. At the same
time, as the technology advances and commercial
applications multiply, new challenges and
problems will arise.The topics covered in this
paper will be with us for decades.
Three aspects of the technology are relevant
to questions of how to manage it.The first is its
definition. NT covers a wide variety of
processes and materials. One must consider
whether it makes any more sense to talk about
regulating or managing NT than it does to talk
about regulating or managing things that are
blue or things that are very large.The second is
the rapid development of the technology. It has
quickly found new applications and it will
continue to expand into new materials and
new uses. The third is NT’s possible adverse
effects. Right now, we know very little about
these effects.
1. Defining NT
The definition of NT is subject to some confusion
and controversy, and is complicated by
the fact that there are naturally occurring
nano-size materials and other nano-size particles
that occur as byproducts of combustion or
industrial processes. Size is critical in any definition
of NT, but there are a variety of definitions
in circulation. Some of the differences
over definition are of only academic interest,
but the way NT is defined in a regulatory context
can make a significant difference in what
is regulated, how it is regulated, and how well
a regulatory program works.
The U.S. National Nanotechnology
Initiative (NNI) defines NT as “the understanding
and control of matter at dimensions of
roughly 1 to 100 nanometers … nanotechnology
involves imaging, measuring, modeling,
and manipulating matter at this length scale”
(www.nano.gov accessed 10/6/05). The
Europeans tend to define it more simply as the
technology dealing with applications and
products with engineered structures smaller
than 100 nanometers (Swiss RE 2004 p.11;
The Royal Society 2004,p.5).For comparison,
a single human hair is approximately 80,000
nanometers wide, and a red blood cell is
approximately 7,000 nanometers wide (Royal
Society 2004, p. 5).
In the context of this paper, the question of
definition raises at least two important further
questions: 1) Does it make sense to regulate or
manage a collection of processes or materials
on size alone? 2) Can a definition be formulated
that allows both manufacturers and regulators
to know what is included and what is not?

CArbon Footprints Everywhere...Can we Kill it!

Increasing global environmental awareness, along with increased regulatory and governmental pressures in many countries, as well as carbon limits or carbon trading markets, have provided large incentives to companies to reduce their carbon footprints. However, few solutions have been available to help companies evaluate environmental impact, along with cost, when designing their supply chain networks.

"The level of a supply chain’s carbon footprint reflects not only potential current and future liabilities in taxes and offset costs, but may reflect inherent inefficiencies in their operations," said David Simchi-Levi, professor at MIT and product strategy consultant to ILOG. “Moreover, the ability to quantify and reduce carbon dioxide may allow companies to earn credits that can be traded with less-efficient companies, as is evident from the 40 billion Euro world-wide market for carbon emission permits in 2007.”

For over a decade, LogicNet Plus has been offering advanced optimization technology to help users manage complex supply chains by allowing supply chain managers to do an analysis of the trade-offs between production, warehousing, transportation costs and service requirements, as well as the calculation of the optimal network configuration for different cost and service objectives.

Each resident of the largest 100 largest metropolitan areas is responsible on average for 2.47 tons of carbon dioxide in energy consumption each year, 14 percent below the 2.87 ton U.S. average, researchers at the Brookings Institution say in a report released [recently].

Some highlights:

Cities with the largest carbon footprints are mostly in the eastern half of the country from Indiana to western Pennsylvania—areas that rely heavily on coal for electricity production and natural gas for heating.

Lexington, Kentucky, had the biggest per capita carbon footprint: Each resident on average accounted for 3.81 tons of carbon dioxide in their energy usage. At the other end of the scale was Honolulu, at 1.5 tons per person.

It’s tempting to conclude that any carbon reduction policy should target the highest emitters. But that would be faulty logic—or at least bad economics. Efficient policy design requires policies target the least cost reductions. That may or may not be the biggest emitters. The easiest way to guarantee a policy is efficient? Establish a price for carbon and let it be traded—I’ll bet you could see that coming a mile away.


While it is a given that electronic communication is better for the planet than older ways of communicating, it is useful to try to determine the actual carbon emissions associated with the IT systems involved in producing it. What quantity of energy is used to fuel IT equipment? Are some systems more efficient than others? How does choice of software and hardware impact the carbon footprint of electronic communication.
Sun hopes to determine best practices that lower carbon emissions and enable companies to benchmark their practices to the best in the industry

Link to "10 Ways how to keep Footprints away from your Office":--http://ecopreneurist.com/2008/04/11/10-business-practices-that-reduce-your-footprint/

Upcoming to Rule!

Biosensors-- are powerful tools aimed at providing selective identification of toxic chemical compounds at ultratrace levels in industrial products, chemical substances, environmental samples (e.g., air, soil, and water) or biological systems (e.g., bacteria, virus, or tissue components) for biomedical diagnosis. Combining the exquisite specificity of biological recognition probes and the excellent sensitivity of laser-based optical detection, biosensors are capable of detecting and differentiating big/chemical constituents of complex systems in order to provide unambiguous identification and accurate quantification. A new generation of biosensors discussed in this presentation uses antibody and DNA probes.

NANOSENSORS: Exploring the Sanctuary of Individual Living Cell: The combination of nanotechnology, biology, advanced materials and photonics opens the possibility of detecting and manipulating atoms and molecules using nano-devices, which have the potential for a wide variety of medical uses at the cellular level. We have recently reported the development of nano-biosensors and in situ intracellular measurements of single cells using antibody-based nanoprobes. The nano-scale size of this new class of sensors also allows for measurements in the smallest of environments. One such environment that has evoked a great deal of interest is that of individual cells. Using these nanosensors, it is possible to probe individual chemical species and molecular signalling processes in specific locations within a cell. We have shown that insertion of a nano-biosensor into a mammalian somatic cell not only appears to have no effect on the cell membrane, but also does not effect the cell's normal function. The possibilities to monitor in vivo processes within living cells could dramatically improve our understanding of cellular function, thereby revolutionizing cell biology.

WHat the Technology is all about?

Topic:-2

An ATM with an eye

The rise of technology has brought into force many types of equipment that aim at more customer satisfaction. ATM is one such machine which made money transactions easy for customers to bank. The other side of this improvement is the enhancement of the culprit's probability to get his 'unauthentic' share. Traditionally, security is handled by requiring the combination of a physical access card and a PIN or other password in order to access a customer's account. This model invites fraudulent attempts through stolen cards, badly-chosen or automatically assigned PINs, cards with little or no encryption schemes, employees with access to non-encrypted customer account information and other points of failure.
Our paper proposes an automatic teller machine security model that would combine a physical access card, a PIN, and electronic facial recognition. By forcing the ATM to match a live image of a customer's face with an image stored in a bank database that is associated with the account number, the damage to be caused by stolen cards and PINs is effectively neutralized. Only when the PIN matches the account and the live image and stored image match would a user be considered fully verified.
The main issues faced in developing such a model are keeping the time elapsed in the verification process to a negligible amount, allowing for an appropriate level of variation in a customer's face when compared to the database image, and that credit cards which can be used at ATMs to withdraw funds are generally issued by institutions that do not have in-person contact with the customer, and hence no opportunity to acquire a photo.
Because the system would only attempt to match two (and later, a few) discrete images, searching through a large database of possible matching candidates would be unnecessary. The process would effectively become an exercise in pattern matching, which would not require a great deal of time. With appropriate lighting and robust learning software, slight variations could be accounted for in most cases. Further, a positive visual match would cause the live image to be stored in the database so that future transactions would have a broader base from which to compare if the original account image fails to provide a match - thereby decreasing false negatives.
When a match is made with the PIN but not the images, the bank could limit transactions in a manner agreed upon by the customer when the account was opened, and could store the image of the user for later examination by bank officials. In regards to bank employees gaining access to customer PINs for use in fraudulent transactions, this system would likewise reduce that threat to exposure to the low limit imposed by the bank and agreed to by the customer on visually unverifiable transactions.
In the case of credit card use at ATMs, such a verification system would not currently be feasible without creating an overhaul for the entire credit card issuing industry, but it is possible that positive results (read: significant fraud reduction) achieved by this system might motivate such an overhaul.
The last consideration is that consumers may be wary of the privacy concerns raised by maintaining images of customers in a bank database, encrypted or otherwise, due to possible hacking attempts or employee misuse. However, one could argue that having the image compromised by a third party would have far less dire consequences than the account information itself. Furthermore, since nearly all ATMs videotape customers engaging in transactions, it is no broad leap to realize that banks already build an archive of their customer images, even if they are not necessarily grouped with account information.
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Topic:-1

INFRARED COMMUNICATION

What is IR?
Infrared (IR) is a type of light that is not visible to the human eye. The following are both good introductions to IR:


How can you use IR to communicate?
Put very simply, a transmitter produces rapid pulses of IR light in specific patterns, which a receiver can interpret. You most likely use IR for communication on a daily basis: that's how television remotes work!

How does the RCX use IR?
The IR port on the RCX is the shiny black bit above the numbered input ports. Primarily, you use this port to download new programs to the RCX (through the IR tower that plugs into your computer). However, the port can also send messages out.

The RCX sends IR messages in "packets." Each packet consists of a specific "header" followed by the "payload" or actual data. The packet ends with a checksum, which is a method of verifying that the data was read correctly. The header is there so that the RCX knows where the packet starts. When you send a message from one RCX to another, the payload is two bytes long: the first just indicates that the second byte is a message (as opposed to code to download, for example -- the same packet form is used by the IR tower!). So when you want to send messages between two RCX bricks, you can only send one byte at a time. (What is a byte?) If you are interested in specifics pertaining to the RCX, check out Stef Mientki's page on Mindstorms IR-communication.

What are some problems with IR communication?
IR is a fairly cheap and easy way for two things to communicate. However, it does have a number of problems, including:

The sun! The sun gives off a lot of infrared light. In direct sunlight, the IR receiver can be "flooded" and won't be able to see any incoming messages. To work around this, always use your Mindstorms indoors and out of direct sunlight.
Line-of-sight. You may know from experience that you need to point a TV remote control directly at the TV for it to work. If you point the remote in some other direction, or if you put your hand between the remote and the TV, chances are the remote will stop working. Similarly, if you want to have two RCX bricks talk to each other, they need to be pointed roughly at each other. In my experience, I've found that, as long as both robots are in the same room and nothing is in between them, they are still able to communicate, so this isn't as big a problem as it might seem.
Timing problems.
One-byte payloads. As mentioned above, the RCX firmware (the underlying program that interprets the message packets and runs the code you write) only allows for messages that are one byte long. As a consequence, sending complex messages becomes challenging (and time-consuming).

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Seminar Topic...continued

Topic:-6

FUZZY LOGIC

Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (Klir 1997).

Degrees of truth are often confused with probabilities. However, they are conceptually distinct; fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. To illustrate the difference, consider this scenario: Bob is in a house with two adjacent rooms: the kitchen and the dining room. In many cases, Bob's status within the set of things "in the kitchen" is completely plain: he's either "in the kitchen" or "not in the kitchen". What about when Bob stands in the doorway? He may be considered "partially in the kitchen". Quantifying this partial state yields a fuzzy set membership. With only his big toe in the dining room, we might say Bob is 99% "in the kitchen" and 1% "in the dining room", for instance. No event (like a coin toss) will resolve Bob to being completely "in the kitchen" or "not in the kitchen", as long as he's standing in that doorway. Fuzzy sets are based on vague definitions of sets, not randomness.

Fuzzy logic allows for set membership values between and including 0 and 1, and in its linguistic form, imprecise concepts like "slightly", "quite" and "very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and possibility theory. It was introduced in 1965 by Lotfi Zadeh at the University of California, Berkeley.

Fuzzy logic is controversial in some circles, despite wide acceptance and a broad track record of successful applications. It is rejected by some control engineers for validation and other reasons, and by some statisticians who hold that probability is the only rigorous mathematical description of uncertainty. Critics also argue that it cannot be a superset of ordinary set theory since membership functions are defined in terms of conventional sets.

Fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. Classical logic or Boolean logic has two values or states. Eg. (true or false). It requires a deep understanding of a system, exact equations, and precise numeric values. Fuzzy logic is a continuous form of logic. eg (bad, very bad, poor, average). It allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience. Fuzzy logic is a powerful problem solving methodology introduced by Lotfi Zadeh in 1960 s. It provides tools for dealing with imprecision due to uncertainty and vagueness, which is intrinsic to many engineering problems. It is a superset of Boolean or Crisp logic.It emerged into mainstream of information technology in late 1980 s and early 1990

Applications
Fuzzy logic can be used to control household appliances such as washing machines (which sense load size and detergent concentration and adjust their wash cycles accordingly) and refrigerators.

A basic application might characterize subranges of a continuous variable. For instance, a temperature measurement for anti-lock brakes might have several separate membership functions defining particular temperature ranges needed to control the brakes properly. Each function maps the same temperature value to a truth value in the 0 to 1 range. These truth values can then be used to determine how the brakes should be controlled.


In this image, cold, warm, and hot are functions mapping a temperature scale. A point on that scale has three "truth values" — one for each of the three functions. For the particular temperature shown, the three truth values could be interpreted as describing the temperature as, say, "fairly cold", "slightly warm", and "not hot".

A more sophisticated practical example is the use of fuzzy logic in high-performance error correction to improve information reception over a limited-bandwidth communication link affected by data-corrupting noise using turbo codes. The front-end of a decoder produces a likelihood measure for the value intended by the sender (0 or 1) for each bit in the data stream. The likelihood measures might use a scale of 256 values between extremes of "certainly 0" and "certainly 1". Two decoders may analyse the data in parallel, arriving at different likelihood results for the values intended by the sender. Each can then use as additional data the other's likelihood results, and repeats the process to improve the results until consensus is reached as to the most likely values.


Misconceptions and controversies
Fuzzy logic is the same as "imprecise logic".
Fuzzy logic is not any less precise than any other form of logic: it is an organized and mathematical method of handling inherently imprecise concepts. The concept of "coldness" cannot be expressed in an equation, because although temperature is a quantity, "coldness" is not. However, people have an idea of what "cold" is, and agree that there is no sharp cutoff between "cold" and "not cold", where something is "cold" at N degrees but "not cold" at N+1 degrees — a concept classical logic cannot easily handle due to the principle of bivalence.
Fuzzy logic is a new way of expressing probability.
Fuzzy logic and probability refer to different kinds of uncertainty. Fuzzy logic is specifically designed to deal with imprecision of facts (fuzzy logic statements), while probability deals with chances of that happening (but still considering the result to be precise). However, this is a point of controversy. Many statisticians are persuaded by the work of Bruno de Finetti that only one kind of mathematical uncertainty is needed and thus fuzzy logic is unnecessary. On the other hand, Bart Kosko argues that probability is a subtheory of fuzzy logic, as probability only handles one kind of uncertainty. He also claims to have proven a derivation of Bayes' theorem from the concept of fuzzy subsethood. Lotfi Zadeh, the creator of fuzzy logic, argues that fuzzy logic is different in character from probability, and is not a replacement for it. He has created a fuzzy alternative to probability, which he calls possibility theory. Other controversial approaches to uncertainty include Dempster-Shafer theory and rough sets.
Fuzzy logic will be difficult to scale to larger problems.
In a widely circulated and highly controversial paper, Charles Elkan in 1993 commented that "...there are few, if any, published reports of expert systems in real-world use that reason about uncertainty using fuzzy logic. It appears that the limitations of fuzzy logic have not been detrimental in control applications because current fuzzy controllers are far simpler than other knowledge-based systems. In future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems". Reactions to Elkan's paper are many and varied, from claims that he is simply mistaken, to others who accept that he has identified important limitations of fuzzy logic that need to be addressed by system designers. In fact, fuzzy logic wasn't largely used at that time, and today it is used to solve very complex problems in the AI area.

Examples where fuzzy logic is used
Automobile and other vehicle subsystems, such as ABS and cruise control (e.g. Tokyo monorail)
Air conditioners
The MASSIVE engine used in the Lord of the Rings films, which helped show huge scale armies create random, yet orderly movements
Cameras
Digital image processing, such as edge detection
Rice cookers
Dishwashers
Elevators
Washing machines and other home appliances
Video game artificial intelligence
Language filters on message boards and chat rooms for filtering out offensive text
Pattern recognition in Remote Sensing
Gambit System in Final Fantasy XII
Fuzzy logic has also been incorporated into some microcontrollers and microprocessors, for instance, the Freescale 68HC12.


How fuzzy logic is applied
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Fuzzy Set Theory defines Fuzzy Operators on Fuzzy Sets. The problem in applying this is that the appropriate Fuzzy Operator may not be known! For this reason, Fuzzy logic usually uses IF/THEN rules, or constructs that are equivalent, such as fuzzy associative matrices.

Rules are usually expressed in the form IF variable IS set THEN action

For example, an extremely simple temperature regulator that uses a fan might look like this IF temperature IS very cold THEN stop fan IF temperature IS cold THEN turn down fan IF temperature IS normal THEN maintain level IF temperature IS hot THEN speed up fan

Notice there is no "ELSE". All of the rules are evaluated, because the temperature might be "cold" and "normal" at the same time to differing degrees.

The AND, OR, and NOT operators of boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement; when they are defined this way, they are called the Zadeh operators, because they were first defined as such in Zadeh's original papers. So for the fuzzy variables x and y:

NOT x = (1 - truth(x))

x AND y = minimum(truth(x), truth(y))

x OR y = maximum(truth(x), truth(y))

There are also other operators, more linguistic in nature, called hedges that can be applied. These are generally adverbs such as "very", or "somewhat", which modify the meaning of a set using a mathematical formula.

In application, the programming language Prolog is well geared to implementing fuzzy logic with its facilities to set up a database of "rules" which are queried to deduct logic. This sort of programming is known as logic programming.

Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational data base, FRDB, appeared in Maria Zemankova's dissertation.


Other examples
If a man is 1.8 meters, consider him as tall:
IF male IS true AND height >= 1.8 THEN is_tall IS true; is_short IS false

The fuzzy rules do not make the sharp distinction between tall and short, that is not so realistic:
IF height <= medium male THEN is_short IS agree somewhat IF height >= medium male THEN is_tall IS agree somewhat

In the fuzzy case, there are no such heights like 1.83 meters, but there are fuzzy values, like the following assignments:

dwarf male = [0, 1.3] m small male = (1.3, 1.5] medium male = (1.5, 1.8] tall male = (1.8, 2.0] giant male > 2.0 m

For the consequent, there are also not only two values, but five, say: agree not = 0 agree little = 1 agree somewhat = 2 agree a lot = 3 agree fully = 4

In the binary, or "crisp", case, a person of 1.79 meters of height is considered short. If another person is 1.8 meters or 2.25 meters, these persons are considered tall.

The crisp example differs deliberately from the fuzzy one. We did not put in the antecedent

IF male >= agree somewhat AND ...

as gender is often considered as a binary information. So, it is not so complex as being tall.


Formal fuzzy logic
In mathematical logic, there are several formal systems that model the above notions of "fuzzy logic". Note that they use a different set of operations than above mentioned Zadeh operators.


Propositional fuzzy logics
Basic propositional fuzzy logic is an axiomatization of logic where conjunction is defined by a continuous t-norm, and implication is defined as the residuum of the t-norm. Its models correspond to BL-algebras.
Łukasiewicz fuzzy logic is a special case of basic fuzzy logic where conjunction is Łukasiewicz t-norm. It has the axioms of basic logic plus an axiom of double negation (so it is not intuitionistic logic), and its models correspond to MV-algebras.
Gödel fuzzy logic is a special case of basic fuzzy logic where conjunction is Gödel t-norm. It has the axioms of basic logic plus an axiom of idempotence of conjunction, and its models are called G-algebras.
Product fuzzy logic is a special case of basic fuzzy logic where conjunction is product t-norm. It has the axioms of basic logic plus another axiom, and its models are called product algebras.
Rational Pavelka logic is a generalization of multi-valued logic. It is an extension of Łukasziewicz fuzzy logic with additional constants.
All these logics encompass the traditional propositional logic (whose models correspond to Boolean algebras).


Predicate fuzzy logics
These extend the above-mentioned fuzzy logics by adding universal and existential quantifiers in a manner similar to the way that predicate logic is created from propositional logic.


Effectiveness for fuzzy logics
The notions of a "decidable subset" and "recursively enumerable subset" are basic ones for classical mathematics and classical logic. Then, the question of a suitable extension of such concepts to fuzzy set theory arises. A first proposal in such a direction was made by E. S. Santos by the notions of fuzzy Turing machine, Markov normal fuzzy algorithm and fuzzy program. Successively, L. Biacino and G. Gerla proposed the following definition where Ü denotes the set of rational numbers in [0,1]. A fuzzy subset μ : S [0,1] of a set S is recursively enumerable if a recursive map h : S×N Ü exists such that, for every x in S, the function h(x,n) is increasing with respect to n and μ(x) = lim h(x,n). We say that μ is decidable if both μ and its complement –μ are recursively enumerable. An extension of such a theory to the general case of the L-subsets is proposed in a paper by G. Gerla. The proposed definitions are well related with fuzzy logic. Indeed, the following theorem holds true (provided that the deduction apparatus of the fuzzy logic satisfies some obvious effectiveness property).

Theorem. Any axiomatizable fuzzy theory is recursively enumerable. In particular, the fuzzy set of logically true formulas is recursively enumerable in spite of the fact that the crisp set of valid formulas is not recursively enumerable, in general. Moreover, any axiomatizable and complete theory is decidable.

It an open question to give a support for a Church thesis for fuzzy computability and to give Goedel’s theorems for fuzzy logic using the notion of recursively enumerable fuzzy subset. To this aim, it is very important to refer to adequate definitions of fuzzy grammar and of fuzzy Turing machine (see for example Wiedermann's paper).

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