3. Nanorobots

Нанороботи - це мініатюрні роботи розміром від 0,5 до З нанометів, які штучно вводяться в організм людини з метою лікування від багатьох хвороб. Завдяки їх ефективності, їх застосоування мас багато перспектив у майбутньому.

The above statement raises the interesting possibility that machines constructed at the molecular level (nanomachines) may be used to cure the human body of its various ills. This application of nanotcchnology to the field of medicine is commonly called as nanomedicine.

NANOROBOTS: WHAT ARE THEY?

Nanorobots are nanodevices that will be used for the рифове of maintaining and protecting the human body against pathogens. They will have a diameter of about 0.5 to 3 microns and will be constructed out of parts with dimensions in the range of 1 to 100 nanometers. The main element used will be carbon in the form of diamond / fullerene nanocomposites because of the strength and chemical inertness of these forms. Many other light elements such as oxygen and nitrogen can be used for special purposes. To avoid being attacked by the host's immune system, the best choice for the exterior coating is

a passive diamond coating. The smoother and more flawless the coating, the less the reaction from the body's immune system. Such devices have been designed in recent years but no working model has been built so far. The powering of the nanorobots can be done by metabolising local glucose and oxygen for energy. In a clinical environment, another option would be externally supplied acoustic energy. Other sources of energy within the body can also be used to supply the necessary energy for the devices. They will have simple onboard computers capable of performing around 1000 or fewer computations per second. This is because their computing needs are simple, Communication with the device can be achieved by broadcast-type acoustic

signalling.

A navigational network may be installed in the body, with stationkeeping navigational elements providing high positional accuracy to all passing nanorobots that interrogate them, wanting to know their location. This will enable the physician to keep track of the various devices in the body. These nanorobots will be able to distinguish between different cell types by checking their surface antigens (they are different for each type of cell). This is accomplished by the use of chemotactic sensors keyed to the specific antigens on the target cells.

When the task of the nanorobots is completed, they can be retrieved by allowing them to exfuse themselves via the usual human excretory channels. They can also be removed by active scavenger systems. Tins feature is design-dependent.

4. Types of Networks. Neural Networks.

A network is simply two on more computers linked together, It allows users to share not only data files and software applications, but also hardware, like printers and other computer resources such as fax.

Most networks link computers within a limited area -within a department, an office, or a building.These are called Local Area Networks, or LANs. But networks can link computers across the world, so yon can share information with someone on the other side of the world as easily as sharing with a person at the next desk. When networks arc linked together in this way, they are called Wide Area Networks, or WANs.

Networks increase productivity by allowing workers to share information easily without printing, copying, telephoning, or posting. They also save money by sharing peripherals such as printers.

Neural Networks.

A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. This tool can come in different forms, specifically it can be hardware based or emulated through special software. The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional

linear models are simply inadequate when it comes to modeling data that contains nonlinear characteristics.

Different problems involve different functions. Some of them produce functions that are linear; others require using non-linear functions in order to get the solution. II really helps when one has a formula which describes certain function of interest. Unfortunately this is not always the case when solving more involved problems. That's when approximation comes in handy. The idea is that if we can't have exact function formula at hand then we can use its approximation to calculate necessary values. Neural network is one of the mechanisms of approximating functions with some notable features,

Brain analogy

Our brains are composed of billions of neurons, each one connected to thousands of other neurons to form a complex network of extraordinary processing power. Artificial neural network attempts to mimic our brain's processing capability, albeit on a far smaller scale.

Information is transmitted from one neuron to another via the axon and dendrites. The axon carries the voltage potential, or action potential, from an activated neuron to other connected neurons. The action potential is picked up from receptors in the dendrites. The synaptic gap is where chemical reactions take place, either to excite or inhibit the action potential input to the given neuron.

Artificial networks are quite simple by comparison. For many applications artificial neural networks are composed of only a handful, a dozen or so, neurons. This is far simpler tlian our brains. Some specific applications use networks composed of perhaps thousands of neurons, yet even these are simple in comparison to our brains. At this time we can't hope to approach the processing power of the human brain.using our artificial networks; however, for specific problems simple networks can be quite powerful.

The problem of simulating human brain has also another aspect. Lack of knowledge concerning processes in our brains prevents us from attempts to simulate it completely as il seems impossible to simulate something that we do not quite understand.

Apparently simulating the whole brain with a single even immensely vast network appears to be Sisyphean toil as it will most likely fail. One possible solution is using a whole cascade of neural networks with each one having special functions.

This is the biological metaphor for neural networks. It is not completely reflected in the construction of artificial networks but (he main feature is indeed inspired by the brain structure. The idea of combining a number of simple by staicture elements in a network allows achieving incredible results.

Network Training. There is one particularly interesting tiling about neural networks. Right after its "birth" net cannot really do anything. In order to make it capable of solving required problem you must teach it by giving it examples of how you want things done. So how are examples presented? They

are nothing but pairs of input-output data. You feed network with input data, then get its output and compare it with the output you expect. If expected results are different from what net outputs then you fix some things inside it and continue your experiments.

Depending on the complexity of the function you are approximating it will take you different number of "tries" to teach your network. After network is trained you can use it by feeding it data and accepting its output as result that this time doesn't require any corrections. Spheres of Application / Process modeling and control - Creating a neural network model for a physical plant then using that model to determine the best control settings for the plant.

V Machine diagnostics - Detect when a machine has failed so that the system can automatically shut down the machine when this occurs.

V Portfolio management - Allocate the assets in a portfolio in a way / that maximizes return and minimizes risk.

S Target recognition - Military application which uses video and/or

infrared image data to determine if an enemy target is present. / Medical diagnosis - Assisting doctors with their diagnosis by

analyzing the reported symptoms and/or image data such as MRls or

X-rays. / Credit rating - Automatically assigning a company's or individuals'

credit rating based on their financial condition. ^ Targeted marketing - Finding the set of demographics which have

the highest response rate for a particular marketing campaign. S Voice recognition - Transcribing spoken words into ASCII text. ^ Financial forecasting - Using the historical data of a security to

predict the future movement of that security. ^ Quality control - Attaching a camera or sensor to the end of a

production process to automatically inspect for defects. ^ Intelligent searching - An internet search engine that provides the

most relevant content and banner ads based on tire users' past behavior.

Neural networks no doubt can take and are actually taking our understanding of algorithms and problems solving techniques to a new level. They will even change the type of computers people use as they spurred the idea of neuro-computers - computers made of artificial neurons. Artificial neural networks have already shown their power and will do even more in the next few years.

We should be very careful with our expectations (hough. At the very dawn of neural networks a lot of harm was done to them because of unrealistic expectations. We cannot expect that we will soon build intelligent creatures using neural networks because, our knowledge about the thinking processes in our brains is very modest at present. The better we understand ourselves, the better are the chances of a breakthrough in the brain building industry,