Future health care systems

Future health care systems

Jean-Charles Bazin

CC500-GroupA – “Seeing inside the body from the outside”: this is exactly the main goal of medical imaging. X-ray pictures, MRI, endoscopy and so on are some equipment examples that permit to perform such an unbelievable task. This paper deals with the development of medical imaging systems in diverse points of view. We will first introduce some future imaging sensors and then show some image processing examples. Finally some vision-based medical robotic systems will be presented.

Imaging sensors

Nowadays, there exist three main medical imaging systems: X-ray, MRI and endoscopy. This section presents their capabilities, limitations and future versions.

X-ray

The most common imaging tool is based on x-ray technology and permits to obtain some images similar to figure 1. For example, a chest x-ray makes images of the heart, lungs, blood vessels and the bones of the chest. Therefore it is very useful to diagnose pneumonia, heart problems or other related medical conditions. However, it suffers from some limitations such as radiation exposure and image resolution. First, radiography involves exposing a part of the body to a small dose of radiation to produce pictures of the inside of the body. Thus there exists a small probability that a patient might develop a cancer from the radiation received during the procedure. Obviously, future X-ray systems should be effective with a minimum radiation dose, for example equivalent to the average dose received in daily life. The second limitation is due to low resolution: the radiologist might not detect some important deformations or even very small cancers. Current X-ray technology does not permit to increase image quality a lot. Thus two approaches are possible: either enhance the X-ray picture with image processing algorithms (cf chapter 2) or switch to another imaging system such as MRI.

Figure 1 – typical X-ray images of a skull (left) and a hand (right)

Endoscopy

Endoscopy does not correspond to a see-through system like X-ray but rather a minimally invasive equipment. Concretely, an endoscope is composed of a flexible tube in which a light and a camera are inserted. Recent researches have led to the development of both miniature and high quality cameras so that images obtained by endoscopy provide a very good resolution. Moreover, it permits to obtain color images, contrary to other imaging sensors such as X-ray or MRI. The drawback is that the procedure is far from being comfortable for patients. That is why researchers are trying to manufacture micro cameras whose size does not exceed 2 centimeters and equipped with wireless communication transmitter so that the patient can simply eat the camera.

Figure 2 – Left: an endoscope. Right: image acquired during an endoscopy

MRI

The acronym MRI stands for Magnetic Resonance Imaging and refers to a very powerful medical imaging system. Its key technology is based on magnetic field and thanks to advanced signal analysis, can retrieve the object consistency that the magnetic field goes through. The system is depicted by figure 3 and some MRI examples are sown in figure 4. As you can notice, MRI images consist of a series of slices. Then it is possible to “stick” these images so that the body can be reconstructed in three dimensions. MRI provides a much better accuracy than X-ray pictures but also suffer from some limitations. First, such a system is very expensive, about US$ 1 million. The second limitation does not deal with MRI system itself but the image processing algorithms that stick the images. Finally, the procedure requires that the patient remains immobile during the whole acquisition, typically 10-20 minutes. Future MRI systems will have to face these three important difficulties.

Figure 3 – Procedure of the MRI sensor

Figure 4 – Sequence of images obtained by MRI

Image processing

Analyzing medical images is not an easy task, even for a specialist. That is why some research works have focused on automatically analyzing these images. For examples, some methods permit to detect bone fractures or even breast cancers. Some other researchers have concentrated efforts to obtain accurate 3D reconstruction of some body parts such as chest or brain. The more accurate the 3D reconstruction is, the better diagnosis can be made. Figures 5 and 6 present some results of such algorithms.

Figure 5 – Automatic 3D reconstruction of a brain (right) from a set of X-ray pictures (left) and MRI images (middle)

Figure 6 – Advanced signal analysis methods permit to precisely retrieve the consistency of some body parts. Thus it is possible to precisely display the tissue, bones and blood vessels for example.

Vision-based robotic systems

Recently, the field of machine vision has reached such a high robustness level that it can be applied to medical applications. One of the most impressive results is based on the so-called “augmented reality” technology which adds virtual objects and information in real videos. Figure 7 shows an example where an instrument can be precisely pointed towards a particular area of the brain, without even having to cut the patient’s hair. Figure 8 depicts a critical step during a surgery for Parkinson disease: an electrode has to be accurately positioned in the brain and emit a high current to irradiate some neurons. For such a difficult case, a complex system combining augmented reality, laser and robotic has been developed: the augmented reality part permits to visualize the brain areas and blood vessels, the laser to precisely map the surface of the brain and finally the robot to carefully insert the electrode in the brain without any shaking usually inherent to any human surgeons.

Figure 7 – An augmented reality system to visualize the brain “seeing through” hair and head skin.

Figure 8 – A complex system combining augmented reality, laser and robotic for accurately inserting an electrode into the patient’s brain

Conclusion

This paper has dealt with some aspects of future health systems with respect to medical imaging. First we have presented the three most common imaging systems and explain their capabilities, limitations and future versions. Then some examples of image processing algorithms have been introduced. Finally, some vision-based robotic systems have been shown. It is worthwhile to note that research is very active in medical field nowadays, so we can expect several impressive equipments in very near future.

References

- J.-F. Mangin, O. Coulon, and V. Frouin, “Robust brain segmentation using histogram scale-space analysis and mathematical morphology”, in 1st MICCAI, MIT, Boston, USA pages 1230--1241, LNCS-1496, Springer Verlag, 1998

- Andreas H. König and Eduard Gröller, “3D Medical Visualization: Breaking the Limits of Diagnostics and Treatment” , ERCIM News No.44 - January 2001

- http://en.wikipedia.org/wiki/MRI

Galileo project - hot news

Galileo project

Jean-Charles Bazin

CC500-GroupA - If you ask anyone the name of a positioning system by satellite, they will probably answer nothing but GPS. But how many people know that the USA have a complete control on the GPS signal? Concretely U.S. authorities have the right to limit the localization accuracy, decrease the signal strength and even shut down the access. To overcome this important limitation, European Union has decided to develop its own positioning system by satellite which is referred as Galileo. In this paper, the concept of localization by satellite is introduced first. Then the reasons leading to the development of a European GPS are explained and finally, the economic and political issues are presented, which will permit to explain how much important the coming 7^th of June is.

Constellation of GPS satellites used for positioning

Positioning by satellite

Being able to know his position at anytime has been a dream since the humanity exists. Thanks to personal GPS receiver, this dream has become true for only 100 dollars. The term GPS refers to an amazing system composed of about 30 satellites orbiting at 20 000km and complex monitoring stations. The basic idea of positioning by satellite is actually very simple: a GPS receiver can calculate its position by measuring its distance with respect to three or more satellites. Indeed by measuring the time delay between the transmission and reception, and as the signal speed is known, the device can compute its distance to the satellite. Doing so for at least three satellites, the GPS receiver can estimate its position by simple trilateration. Combining this basic method with advanced technology, an accuracy of 5 meters can be obtained.

A personal GPS receiver from Magellan society

Galileo - the European GPS

Currently, two localization systems by satellite exist: the American GPS and the Russian GLONASS. Due to the collapse of the Soviet Union, GLONASS has not been correctly maintained so that only eight satellites were in operation in 2002 and therefore GPS is the most widely used system. However, less than two weeks ago, Vladimir Putin has decided to open full signal access to civilians and restore the entire satellite constellation within 5 years. Simultaneously, European Union is developing its own positioning system by satellite. Indeed, as explained in the introduction, the US ministry of defense can limit the GPS localization accuracy, decrease the signal strength and even shut down the access, at any time without informing users in advance. Thus in 2002, EU has officially signed Galileo project funding and planned a civilian use as soon as in 2010.

Political and economical issues of the Galileo project

The main reason why EU decided to develop its own positioning system was to be independent from the American ministry of Defense. Indeed how any military equipment from any European countries could be under the indirect control of the US government? However, the huge amount of money involved in Galileo project has made some interest conflict emerged between countries. For examples, large countries wanted its national organization be in charge of manufacturing the satellites: Thales or Alcatel for France, Aliena for Italia, SSTL for UK, Deutsche Telekom for Germany, etc... Concerning smaller countries like Poland, Austria or Greece, they are arguing to obtain the tracking stations inside their national territory. Moreover, an important part of the project budget came from private funds. However, regarding the difficulty of the actual situation, some institutions have cancelled or decreased their financial propositions so that the project is now under-funded. Therefore this complex situation has led to important tensions and as a consequence, Galileo project is two years late with respect to the initial development plane. In order to solve these political and economical problems, a crucial meeting will be held on the 7^th of June. For example, an important point is that Galileo became a project fully supported by public, i.e. private funds are not allowed. However such a decision can be taken only if the 27 members of the European Union vote it at unanimity. If concrete solutions are not found during this conference, it will have strong consequences for the future development of the European GPS, not only for European countries but also the nations that participate to the project, such as China, India and South Korea.

References:

- http://en.wikipedia.org/wiki/Galileo_positioning_system

- http://en.wikipedia.org/wiki/Global_Positioning_System

- http://en.wikipedia.org/wiki/Satellite_constellation

What is DNA?

What is DNA?

DNA is a long fiber, like a hair, only thinner and longer(Fig. 1)(1). It is made from two strands that stick together with a slight twist. Proteins attach to the DNA and help the strands coil up into a chromosome when the cell gets ready to divide. The DNA is organized into stretches of genes, stretches where proteins attach to coil the DNA into chromosomes, stretches that "turn a gene on" and "turn a gene off".

Figure 1 DNA strand

The genes carry the instructions for making all the thousands of proteins that are found in a cell. The proteins in a cell determine what that cell will look like and what jobs that cell will do. The genes also determine how the many different cells of a body will be arranged.

In these ways, DNA controls how many fingers you have, where your legs are placed on your body, and the color of your eyes.

What's the difference between DNA and a chromosome?

A chromosome is made up of DNA and the proteins attached to it. There are 23 pairs of chromosomes in a human cell(Fig 2). One of each pair was inherited from your mother and the other from your father. DNA is a particular bio-molecule. All of the DNA in a cell is found in individual pieces, called chromosomes.





Figure 2 human chromosome

Why do you want to learn about DNA?

If you have gotten this far, you already have some curiosity about DNA. That curiosity may have come from hearing about it in the news or in the movies. A revolution has occurred in the last few decades that explains how DNA makes us look like our parents and how a faulty gene can cause disease. This revolution opens the door to curing illness, both hereditary and contracted. The door has also been opened to an ethical debate over the full use of our new knowledge. In the end, curiosity is the reason to learn about DNA. Fittingly, curiosity is the driving force behind science itself.

What’s Genes?

I’ll tell you a bit about genes. Genes are stretches of DNA and all have assigned places on the cell's chromosomes. There's all sorts of other fragments that have assigned places, but they're not genes. Think about there's the on switches that tell a gene when its time to go to work. For example, right after you eat, the insulin-on-switch tells me to get busy!

And then there's the off switches that give a gene time off. For instance, when enough insulin has been made, the insulin-off-switch works.

Not to mention the docking sites for special proteins to bind when its time to wind up into a chromosome before cell division. And what about all those stretches of DNA that do who knows what?

How are genes all fit on the chromosome? Let's take a peek at the DNA Ladder. Can you find gene? For example, I suppose A is a pretty small gene as far as that goes. It is only about 230 nucleotides long. When you consider that all the DNA in one nucleus of one human cell adds up to over two and a half billion (2,500,000,000) nucleotides long, well, I'm kind of trivial. However the size has nothing to do with importance. And the existence of gene is important.

Think about how to make hemoglobin and myosin transcribing gene.

The B gene , who knows how to make hemoglobin, is about twice A size. And then, there is the C gene, who knows how to make myosin, the protein that makes muscles work, which is 20 times bigger than A gene product! It’s amazing.

How do genes do it? Keep the blueprints for making proteins? After all, we are made from only 4 different nucleotides! Fact is, genes are so simple, scientists used to call them the 'stupid substance'. Genes had a good laugh over that announcement! However eventually those scientists figured out our secret.

You know proteins are made from building blocks, too. Only their building blocks are different from gene’s and are called 'amino acids'. There are 20 different amino acids that can be used to make enzyme like polymerase. That's what threw those scientists off a clue for a while. They were thinking, since protein is made from 20 kinds of building blocks and genes are only made from 4 kinds. Is it possible?

DNA STRUCTURE

In chromosome, there are a lot of genes here, so it can get pretty confusing. Genes are the brains behind the whole operation - not just the nucleus, but the entire cell and even the entire body. Each of genes have only one job to do. That's to remember exactly how to construct a single protein. Gene A, for instance, keep the blueprint for making insulin. Insulin tells your body that the glucose (sugar) levels are too high and that your cells should begin using it to make fat. Gene B lives on another chromosome, keeps the blueprint for making hemoglobin. Hemoglobin is the protein that carries oxygen around in your bloodstream. Your body needs to make hemoglobin all the time. However gene A doesn’t have to work every minute. Your body doesn't need insulin except right after you eat. So, I get some time off between meals.

That's why I can show you around chromosome.

Let me introduce you to polymerase. Polymerase is not a gene. It's not even DNA. Polymerase is a protein: a special protein called an enzyme. Gene may be the brains around here. Like Gene said, polymerase’s full name is DNA Polymerase. It’s job is to construct an exact copy of all the chromosomes just before the cell divides. Actually, it takes a whole team to do that job, and it’s part is to super-glue the nucleotides together.

Gene and polymerase are made out of small pieces hooked together to make a long strand; like train cars are the small pieces that are hooked together to make a long train. Polymerase is made out of small pieces called 'amino acids'. Gene and the rest of the chromosomes are made from small pieces called 'nucleotides'.

Here, let me show you what a nucleotide is.

Here you can see nucleotides being made from a base, a sugar, and a P (phosphate)(2). There are four factories like this, each making one type of nucleotide for gene to use in making a new chromosome. One factory makes a nucleotide with the name 'adenine' that we just call 'A'. There are also factories for making 'thymine' ('T'), 'cytosine' ('C'), and 'guanine' ('G').

That's what all the Genes and all the rest of the chromosomes are made of. See the left hand end of the nucleotides? Those are sticky spots that cause the two DNA strands of a chromosome to stick together. Scientists call them 'hydrogen bonds'. And see how A and T have two sticky spots and C and G have three? That makes A and T pair up and C and G pair up(3).

1. Alberts, Bruce; Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, and Peter Walters (2002). Molecular biology of the cell:New York and London: Garland Science.

2. Ghosh A, Bansal M (2003). "A glossary of DNA structures from A to Z". Acta Crystallogr D Biol Crystallogr: 620 – 6.

3. James D. Watson(2005). Molecular biology of the gene: fifth edition. Cold spring harbor laboratory press: 100 – 2.

TGV

TGV

Jean-Charles Bazin

CC500-GroupA – What does TGV stand for? Whereas this acronym is completely unknown for most of people, TGV is simply trying to... conquer the world! TGV derives from the French expression “Train a Grande Vitesse” which means high-speed train. Last month, this “bullet train” has largely overcome the speed record on railroads which is a good opportunity to look back on this amazing scientific project. First, historical information will be introduced. Then, current position of TGV will be analyzed. Finally, the future of TGV in the world will be presented.

Historical information

In sixties, less and less people used to take train in France. SNCF, the national enterprise that owns almost all of France's railway system, has taken a risky gamble: if trains go faster, more people will choose train for traveling. At this time, a single high speed train existed in the world: the Japanese Shinkansen. Therefore, French government has put a lot of effort in research to develop a national high speed train: the “train a grande vitesse” TGV was born. The first TGV prototype was powered by gas turbine and reached a top speed of 318 km/h in 1975. However due to the oil crisis of 1973, gas turbines were not economically viable and politics drastically decided to make TGV a full electric wheeled train by overhead electrified lines thanks to the many French nuclear power stations, which preserves the energetic independence of the country.

The French TGV is the fastest wheeled train in the world

Current Position

TGV has gathered an impressive list of world records. The most satisfying award for French government is that one month ago, TGV has established a new speed record on rails: 574.8 km/h. Therefore TGV is still the fastest wheeled train, far ahead from the wheeled Shinkansen that has simply reached 443 km/h. However one may notice that the magnetic levitation version of the Shinkansen had reached 581 km/h in 2003.

Another recent record has been established in May 2006 when TGV (in his Eurostar version) has run the longest non-stop journey in the world. The 1421 km separating Cannes from London have been traveled in only 7 hours 25 minutes. Note that this performance broke the previous record set by also a TGV travelling from two geographically opposite French cities: Calais at the top North near England and Marseille in South lying in the Mediterranean Sea (1067 km in 3hours 29 minutes).

Finally, TGV is the only train in the world that has recorded no single fatality for more than 30 years in operation.

Future plan

In 2006, French president Jacques Chirac has pledged that no SNCF train would be powered by fossil fuels by 20 years. Obviously, most of trains do not use fuels directly, so it means that electricity used by trains must be produced by other energy, especially nuclear power. Actually, current nuclear power stations already generate most of electricity used by SNCF trains.

TGV has an important role to play abroad to enhance its development. After 12 years of partnership with ALSTOM (the manufacturer of TGV), South Korea has operated its first high-speed journey in 2004. This step was very important for both nations: Korea for a more uniform development of the country (capital at the north, heavy industries at the south, etc...) and France for a living advertisement of is know-how and its expertise. Clearly, the next step is to commercialize TGV to China, and also to USA for their first high speed train.

The longest non-stop train journey in the world performed in May 2006 by TGV in Eurostar version transporting crew of Da Vinci Code from London to Cannes for the Cannes film festival

To conclude, the risky gamble taken by SNCF in early sixties has been a success: TGV has carried near two billion passengers. The economic policy of SNCF and ALSTOM is clear crystal: develop a perfect railroad system in France and present it as the best train in the world. That is why we can expect that SNCF will try its best to develop new systems to beat the Japanese Shinkansen. The remaining question is: how many years will be needed to reach the 7 km/h that separate TGV and Shinkansen?

DARPA urban challenge

Jean-Charles Bazin

CC500-GroupA - Who has never dreamt of having a car that could drive fully automatically? Making this dream come true is actually the goal of the amazing DARPA urban challenge. The international participants are asked for building an intelligent robot that can drive autonomously in a completely unknown environment by using cutting-edges technologies, such as robot vision, laser, GPS, artificial intelligence, etc... No later than at the end of this week, they have to submit a qualifying video proposal that clearly presents the navigating abilities of their robot. This crucial step is a fabulous opportunity to introduce the DARPA urban challenge in this hot news.

This presentation is divided into three main parts. First, I will explain what the DARPA challenge is and introduce the goal and results of the previous editions of DARPA competitions. Then the brand new ’07 urban challenge will be presented, and finally, the current status of one team (KAIST-Upenn) will be analyzed to show some key technologies involved in this worldwide competition.

First of all, the acronym DARPA stands for US Defense Advanced Research Projects Agency whose aim is to develop future technology for military applications. In order to accelerate research and strengthen relationships with universities, DARPA has created the DARPA challenge. The first edition of DARPA challenge took place in 2004. The cars should travel more than 220 km in the Mojave Desert (USA) without any human interventions. Due to the unbelievable difficulty of such a task, no team managed to complete the distance, and worst, the best team (CMU) has traveled only 12 km. After one year of intensive work, no less than four teams have succeeded in driving the 200 km which was far from being expected. The best car, by Stanford, has completed the distance in only seven hours and won the $1 million first prize.

A brand new edition of DARPA challenge is scheduled for November 2007. Whereas ’04 and ’05 competitions took place in the desert, ’07 edition will be held in urban environment. Therefore, the difficulty is still much higher. Indeed, the cars have to not only take other moving vehicles into account but also obey all traffic regulations. For example, if a car detects an obstacle, it has to modify its trajectory to avoid the collision. A very complex situation occurs when a car arrives at a crossroad and has to check whether no other car is coming before crossing. In regards to the complexity of this edition, the first prize has been increased to $2 million dollars.

Framework of the technologies involved in the DARPA urban challenge: robust GPS communication, robust digital map localization, car and moving object detection

In order to introduce some key technologies involved in the urban challenge, this part focuses on the current status of the Ben Franklin team. This team is the result of an intensive collaboration between GRASP lab at the university of Pennsylvania and RCV lab at KAIST. The project has been divided into two parts. The Korean group is focused on the sensors to gather information from the environment. For example, the goal is to automatically detect road lane markings, moving cars, obstacles and use GPS data, and finally match the results with digital map. These tasks are mainly based on computer vision and artificial intelligence and are very complex. Indeed, for instance, how a machine can detect marking lanes automatically with a 100% accuracy even in presence of shadows, rainy or sunny weather or when lanes are of different colors. Moreover some roads will not have lane marking so the car also has to detect this situation. The US group is working on the car dynamic, that is to say how to control the car, drive it faster or slower, change the direction, etc... It is mainly based on information provided by the Korean team and a small navigation error can lead to a car crash and have dramatic consequences for the expensive embedded equipments. .

To conclude, DARPA challenges have encouraged researchers all over the world to develop new technologies and solve robotic problems that seemed to be impossible even three or four years ago. I am convinced that ’07 edition will bring amazing systems that will set back robotic limits still further.

References:

- http://en.wikipedia.org/wiki/DARPA

- http://en.wikipedia.org/wiki/Urban_challenge

- http://www.benfranklinracingteam.org/

Hot news test example

Can't Knock It Down

Julie J. Rehmeyer

CC500-groupA- The "Comeback Kid" is a wooden toy with an intriguing property: No matter which way you set it down—on its head, for example, or on its side—it turns itself upright. Two factors account for this: the object's shape, and the fact that the bottom of the toy is heavier than the top.

Set the Comeback Kid in any position, and it will turn itself

upright. Theoretically, it's possible to balance the figure on its

head, but the slightest breeze would knock it over and restore

it to its upright stance.

Give mathematicians such a toy, and they're liable to turn it into a math problem.

Mathematicians Gábor Domokos of the Budapest Institute of Technology and Economics and Péter Várkonyi of Princeton University wondered if they could make an improved version that wouldn't require the weight at the bottom to right itself. Could the shape of the object alone be enough to pull it upright?

They started experimenting with flat toys cut from a piece of plywood. They cut out shape after shape and found that the edges of each shape had at least two stable balance points. In addition, each shape's edges had at least two more points on which the mathematicians could balance it if they were very, very careful, but the slightest breeze would knock it over. They refer to those as "unstable balance points." (Similarly, it is possible, barely, to balance the Comeback Kid vertically on its head.)

Eventually, Domokos and Várkonyi managed to prove mathematically that for any flat shape, there are at least two stable balance points and at least two unstable balance points.
Next, the pair began to investigate whether all three-dimensional shapes have at least two stable and two unstable balance points. They tried to generalize their two-dimensional proof to higher dimensions, but it didn't hold up. Therefore, it seemed possible that a self-righting three-dimensional object could exist. Such a shape would have only one stable and one unstable balance point.

They looked for objects in nature that might have such a property. While Domokos was on his honeymoon in Greece, he tested 2,000 pebbles to see if he could find one that would right itself, but none did. "Why he is still married, that is another thing," Várkonyi says. "You need a special woman for this."

Eventually, the team managed to construct an object mathematically that has just one stable and one unstable balance point. The figure is like a pinched sphere, with a high, steep back and a flattish bottom. They sent their equations to a fabricator, who constructed the object. Várkonyi now keeps it in his office. "People like playing with it," he says.

Domokos and Várkonyi used mathematics to design this self-righting object.

Once the pair had built their self-righting object, they noticed that it looked very much like a turtle. They figured that wasn't an accident, since it would be useful for a turtle never to get stuck on its back.

The shape of the Indian Star Tortoise is similar to the self-righting

object that Domokos and Várkonyi created. When turned onto its

back, its shape helps it come close to flipping over without effort,

but the turtle needs to give itself a little boost by kicking its legs.

Now, Domokos and Várkonyi are measuring turtles to see if any of them are truly self-righting, or whether the turtles need to kick their legs a bit to flip themselves back upright. So far, they've tested 30 turtles and found quite a few that are nearly self-righting. Várkonyi admits that most biology experiments study many more animals than that but, he says, "it's much work, measuring turtles."

The mathematicians still face an unanswered question. The self-righting objects they've found have been smooth and curvy. They wonder if it's possible to create a self-righting polyhedral object, which would have flat sides. They think it is probably possible, but they haven't yet managed to find such an object. So, they are offering a prize to the first person to find one: $10,000, divided by the number of sides of the polyhedron.

It sounds like a tempting challenge, but there's a catch: Domokos and Várkonyi are guessing that a self-righting polyhedron would have many thousands of sides. So the prize might only amount to a few pennies.
References:

Domokos, G. 2006. My lunch with Arnold. Mathematical Intelligencer 28 (Fall):31-33. Reprint available at http://www.szt.bme.hu/Munkatrs/domokos/cikk_archiv/99/final/99.pdf.

What is a CPU?

What is a CPU in computer science?

JC Bazin -A processing unit (CPU), or sometimes simply processor, is the component in a digital computer that interprets computer program instructions and processes data. CPUs provide the fundamental digital computer trait of programmability, and are one of the necessary components found in computers of any era, along with primary storage and input/output facilities. A CPU that is manufactured as a single integrated circuit is usually known as a microprocessor. Beginning in the mid-1970s, microprocessors of ever-increasing complexity and power gradually supplanted other designs, and today the term "CPU" is usually applied to some type of microprocessor. The phrase "central processing unit" is a description of a certain class of logic machines that can execute computer programs. This broad definition can easily be applied to many early computers that existed long before the term "CPU" ever came into widespread usage. However, the term itself and its initialism have been in use in the computer industry at least since the early 1960s (Weik 1961). The form, design and implementation of CPUs have changed dramatically since the earliest examples, but their fundamental operation has remained much the same. Early CPUs were custom-designed as a part of a larger, usually one-of-a-kind, computer. However, this costly method of designing custom CPUs for a particular application has largely given way to the development of mass-produced processors that are suited for one or many purposes. This standardization trend generally began in the era of discrete transistormainframes and minicomputers and has rapidly accelerated with the popularization of the integrated circuit (IC).

example of CPU

The IC has allowed increasingly complex CPUs to be designed and manufactured in very small spaces (on the order of millimeters). Both the miniaturization and standardization of CPUs have increased the presence of these digital devices in modern life far beyond the limited application of dedicated computing machines. Modern microprocessors appear in everything from automobiles to cell phones to children's toys.

posting attempt

this is the second post for testing blogger
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