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Computer networking and
maintenance :
Artificial Neuron:
Definition - What does Artificial Neuron mean?
An artificial neuron is a digital construct that seeks to simulate the behavior of a biological
neuron in the brain. Artificial neurons are typically used to make up an artificial neural network
- these technologies are modeled after human brain activity.
Artificial Neuron:
Essentially, an artificial neuron is composed of a set of weighted inputs, along with a transformation function and an activation function. The activation function at the end would correspond to the axon of a biological neuron. The weighted inputs would correspond to the inputs of a biological neuron that take electrical impulses moving through the brain and work on them to transmit them to subsequent layers of neurons.
Artificial neurons, as parts of artificial neural networks, are driving deep learning and machine learning capabilities. They are helping computers to “think more like humans” and produce more sophisticated cognitive results.
Artificial Neural Networking (ANN):
Definition - What does Artificial Neural Network (ANN)
mean?
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output.
ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found.
ANN is also known as a neural network.
Artificial Neural Network (ANN)
An ANN has several advantages but one of the most recognized of these is the fact that it can actually learn from observing data sets. In this way, ANN is used as a random function approximation tool. These types of tools help estimate the most cost-effective and ideal methods for arriving at solutions while defining computing functions or distributions. ANN takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANNs are considered fairly simple mathematical models to enhance existing data analysis technologies.
ANNs have three layers that are interconnected. The first layer consists of input neurons. Those
neurons send data on to the second layer, which in turn sends the output neurons to the third layer.
Training an artificial neural network involves choosing from allowed models for which there are
several associated algorithms.
Deep Convolutional Inverse Graphics
Networking (DC-IGN):
Definition - What does Deep Convolutional Inverse Graphics
Network (DC-IGN) mean?
The deep convolutional inverse graphics network (DC-IGN) is a particular type of convolutional neural network that is aimed at relating graphics representations to images. Experts explain that a deep convolutional inverse graphics network uses a “vision as inverse graphics” paradigm that uses elements like lighting, object location, texture and other aspects of image design for very sophisticated image processing.
Deep Convolutional Inverse Graphics Network (DC-IGN)
The deep convolutional inverse graphics network has a model that includes an “encoder” and a “decoder” – it is a type of neural network that uses various layers to process input to output results. A typical feedforward neural network includes an input layer, hidden layers and output layer. The deep convolutional inverse graphics network uses initial layers to encode through various convolutions, utilizing max pooling, and then uses subsequent layers to decode with unpooling. Throughout this process, the network uses “scene latent variables” and aspects of gradient descent and backpropagation to learn how to represent aspects of images.
As for popular applications of deep convolutional inverse graphics networks, these networks are often used to create variable outputs for an object such as, for example, a human face. By training the model, the deep convolutional inverse graphics network can work up a dynamic rendering engine based on aspects like angle and shade. The end result is a more intelligent ability to manipulate sophisticated three-dimensional images.
Convolutional Neural
Networking (CNN):
Definition - What does Convolutional Neural Network
(CNN) mean?
A convolutional neural network (CNN) is a specific type of artificial neural network that uses perceptrons, a machine learning unit algorithm, for supervised learning, to analyze data. CNNs
apply to image processing, natural language processing and other kinds of cognitive tasks.
A convolutional neural network is also known as a ConvNet.
Convolutional Neural Network (CNN)
and function on the basis of those weighted inputs – however, in theory, an input layer can be
composed of artificial neurons that do not have weighted inputs, or where weights are calculated differently, for example, randomly, because the information is coming into the system for the first time. What is common in the neural network model is that the input layer sends the data to subsequent layers, in which the neurons do have weighted inputs.
Hidden layer:
Definition - What does Hidden Layer mean?
A hidden layer in an artificial neural network is a layer in between input layers and output layers, where artificial neurons take in a set of weighted inputs and produce an output through an activation function. It is a typical part of nearly any neural network in which engineers simulate the types of activity that go on in the human brain.
Hidden Layer:
Hidden neural network layers are set up in many different ways. In some cases, weighted inputs are randomly assigned. In other cases, they are fine-tuned and calibrated through a process called backpropagation. Either way, the artificial neuron in the hidden layer works like a biological neuron in the brain – it takes in its probabilistic input signals, works on them and converts them into an output corresponding to the biological neuron’s axon.
Many analyses of machine learning models focus on the construction of hidden layers in the neural network. There are different ways to set up these hidden layers to generate various results
- for instance, convolutional neural networks that focus on image processing, recurrent neural networks that contain an element of memory and simple feedforward neural networks that work in a straightforward way on training data sets.
Hot Add:
Definition - What does Hot Add mean?
Hot add refers to the ability to dynamically add hardware, virtual or physical, to a running
system without downtime.
Hot Add:
The evident benefit of a hot adds is that system administrators can re-provision services without shutting down the systems. This may involve looking at some hardware or software requirements and the syntax for a particular administrative language.
In doing a hot add, system administrators need to determine whether the system has the resources to do this kind of provisioning on the fly. They may also have to check for things like server processor affinity for SQL, assess the schedulers and look at how a hot add will affect current operations.
The idea behind hot add is part of the overall scheme of virtualization — that hardware setups can be sliced and diced into more versatile virtual networks where one physical machine may represent five or six virtual machines, and where administrators can tinker with the allocation of processing power and memory to make operations more efficient.
Merkle Tree:
Definition - What does Merkle Tree mean?
A Merkle tree is a specific type of data construct in which each non-leaf node of the tree contains
hash values of its own child nodes. Because a Merkle tree demonstrates effective hashing techniques, it is popular in many industries and is being used to help innovate in finance.
Merkle Tree:
Merkle trees are used in many types of sophisticated data handling technologies, including enterprise software supporting global supply chains, deep learning tools for the healthcare industry, and in the world of finance. The blockchain digital ledger system that drives transparency for bitcoin cryptocurrency transactions also uses Merkle trees. The makeup of the Merkle tree provides the opportunity to reduce the amount of data used in integrity checks, while decreasing input-output packet size and separating data validation from the underlying data.
Merkle trees are also used for a process called “consistency proofing” which helps to check on data outcomes.
Backpropagation:
Definition - What does Backpropagation mean?
Backpropagation is a technique used to train certain classes of neural networks – it is essentially
a principal that allows the machine learning program to adjust itself according to looking at its past function.
Backpropagation is sometimes called the “backpropagation of errors.”
Backpropagation:
Backpropagation as a technique uses gradient descent: It calculates the gradient of the loss function at output, and distributes it back through the layers of a deep neural network. The result is adjusted weights for neurons. Although backpropagation may be used in both supervised and unsupervised networks, it is seen as a supervised learning method.
After the emergence of simple feedforward neural networks, where data only goes one way, engineers found that they could use backpropagation to adjust neural input weights after the fact. Backpropagation can be thought of as a way to train a system based on its activity, to adjust how accurately or precisely the neural network processes certain inputs, or how it leads toward some other desired state.
Deep Belief Network
(DBN):
An activation function is the function in an artificial neuron that delivers an output based on
inputs. Activation functions in artificial neurons are an important part of the role that the artificial neurons play in modern artificial neural networks.
Activation Function:
One way to understand the activation function is to look at a visual “model” of the artificial neuron. The activation function is at the “end” of the neural structure, and corresponds roughly to the axon of a biological neuron.
Another way to understand it is to look at the terminology around its use. IT professionals talk about the activation function when discussing either a binary output – either a 1 or a 0 – or a function that graphs a range of outputs based on inputs. In these cases, IT professionals and others often use the terms “transfer function” and “activation function” interchangeably, although the transfer function is more often associated with the graph that scans a range of outputs. Various functions guide the output that filters through the layers of the neural network to the final output layer of neurons or nodes.
It is also important to distinguish between linear and non-linear activation functions. Where linear activation functions maintain a constant, non-linear activation functions create more variation which utilizes the build of the neural network. Functions like sigmoid and ReLU are commonly used in neural networks to help build working models
Restricted Boltzmann
Machine (RBM):
Definition - What does Restricted Boltzmann Machine
(RBM) mean?
A restricted Boltzmann machine (RBM) is a type of artificial neural network invented by Geoff Hinton, a pioneer in machine learning and neural network design.
This type of generative network is useful for filtering, feature learning and classification, and it employs some types of dimensionality reduction to help tackle complicated inputs.
Restricted Boltzmann Machine (RBM)
The restricted Boltzmann machine is so-called because there is no communication between
layers in the model, which is the “restriction” of the model. Experts explain that RBM nodes make “stochastic” decisions, or that these are randomly determined. Various weights change the structure of the input, and activation functions process the output of a node. Like other types of similar systems, the restricted Boltzmann machine operates with input layers, hidden layers and output layers to achieve machine learning results. The RBM has also been useful in creating more sophisticated models, such as deep belief networks, by stacking individual RBMs together.
What are the five schools of
machine learning?
Q:
What are the five schools of machine learning?
A:
To those who haven't researched what's behind modern machine learning and artificial
intelligence work, all of this effort and research often looks like one big amorphous jumble. However, when you scratch the surface and look at what scientific leaders are doing in these
fields, you see that in a way, there are really five different major approaches to the issue of pushing artificial intelligence forward.
These five "schools" or "tribes" have been popularized by the work of Pedro Domingos in his "Master Algorithm" book on AI development, but they are also under consideration elsewhere in various parts of the scientific world.
The first school of artificial intelligence is called connectionism. This school focuses on the actual neural connections and the physics of the human brain. It relies on the idea of backpropagation, which traces these connections to form results. Some people call the connectionist school an "effort to reverse engineer the human brain."
The next school of artificial intelligence is symbolism. Symbolists use logic and pre-existing knowledge to build models that work intelligently. In some ways, the symbolist approach is similar to what emerged early on in the artificial intelligence world before neural networks were developed. If you compile a big enough knowledge base and deal with it in particular ways, it starts to create a form of artificial intelligence, and that's what's behind the symbolist approach which has now been combined with some of the other modern approaches.
The third school is the school of evolutionism. Here, there's a focus on not only evolution theory, but also on genetics and biophysics as well as bioinformatics. You could see this arm of artificial intelligence as the category that works with the human genome and applies modern technologies to the field of genetics. In that sense, evolutionist artificial intelligence is unique. It's a somewhat different kind of project than the other four schools.
The Bayesian school is the fourth school of artificial intelligence. This is, again, one of the older schools and was applied early on, for example, in the elimination of spam from email folders.
The Bayesian model and approach is a heuristic model. It works on the idea of probability to evolve models that will cut out undesirable results, or pursue other objectives, based on where events are most likely to happen, or on other metrics. Another popular application of Bayesian logic is in network security – over the past few years, security engineers have widely used Bayesian logic to spot threats to a network by modeling where those are likely to occur, and how.
The fifth and last school of machine learning is called analogizing. This is also a school that's perhaps more easy for the average consumer to understand. Recommendation engines from
various processes. However, unlike DevOps, AIOps has more to do with the automation of IT
services. Through an iterative cycle, AIOps enables continuous implementation and delivery for IT services. It is a modernization of the prior operational analytics setups that did more of this type of thing manually. AIOps is set to emerge as a principle of the future tech market.
Artificial Intelligence
(AI):
Definition - What does Artificial Intelligence (AI) mean?
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of
intelligent machines that work and reacts like humans. Some of the activities computers with artificial intelligence are designed for include:
- Speech recognition
- Learning
- Planning
- Problem solving
Artificial Intelligence (AI)
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
- Knowledge
- Reasoning
- Problem solving
- Perception
- Learning
- Planning
- Ability to manipulate and move objects
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.
Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an
object belongs to and regression deals with obtaining a set of numerical input or output
examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub- problems such as facial, object and gesture recognition.
Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.
JavaBeans:
Definition - What does JavaBeans mean?
JavaBeans are reusable software components that can be manipulated visually. Practically, they are Java classes that follow certain conventions.
Like Java, JavaBeans also follow the "write once run anywhere" paradigm. They are persistant, and have the ability to save, store and restore their state. They are also used to encapsulate many objects in a single bean. Thus, they can be passed around in a single bean object instead of multiple individual objects. JavaBean features such as properties, events and methods are managed by the builder tool. These properties can be customized at design time.
JavaBeans
Reusability is the main concern behind the component model. Software components provide predefined services, which allow for easy access to applications.
The builder tool is a platform that allows a developer to work with JavaBeans. Through the design mode of the builder tool, the developer can customize the bean's appearance (by modifying its behavior), interaction with other beans, and compose the bean into applets, applications or servlets.
JavaBeans have conventions that should be followed when they are implemented:
- Beans should have a default (no-argument) constructor.
- (^) Beans should provide getter and setter methods. A getter method is used to read the value of a readable property. To update the property’s value, a setter method should be called.
- Beans should implement java.io.serializable, as it allows applications and frameworks to save, store and restore the state of a JavaBean.
Validation Set:
How is machine learning affecting
genetic testing?
Q:
How is machine learning affecting genetic testing?
A:
Machine learning is being applied to genetic testing in many different ways.
The applications are nearly endless. Machine learning is helping scientists to analyze DNA, decode the human genome, assess disease phenotypes, understand gene expression, and even participate in a process called gene editing, where DNA is actually “spliced” into an organism’s genetic code.
The methods of computer science used in genetic machine learning also vary a good deal. Some projects use supervised learning, where all of the data is previously labeled. Others use unsupervised learning, which builds from unlabeled data sets, or a mix of the two principles called semi-supervised learning.
Many of the consumer-facing genetic testing technologies that we see on the market are using some form of machine learning or artificial intelligence to function. For example, products that help to show individuals more about their genetic makeup may have benefited from machine learning in research and development, or in the ongoing analysis of specimens.
In many ways, genetic testing is it the perfect field for machine learning applications, partly because of the enormous volumes of data that these programs need to contend with. For example, working on the human genome involves deciphering billions of bits of information, and prior to the advent of machine learning, many of these tasks were pretty daunting.
For example, Google has a program called DeepVariant that scientists say can now be used to fully map the human genome – that can be used on the full spectrum of a person’s genetic information.
Agencies like the National Institutes of Health are documenting the many ways that machine learning and artificial intelligence contribute to better understanding of genetics and genomics, the branch of molecular biology that covers genetic science. There’s even a “school” of machine learning called evolutionism that covers many of the classified machine learning tasks relevant to genetic work. In the end, machine learning is acting as a catalyst for quicker and more diverse development in genetic research and engineering.
Gradient Descent Algorithm :
Definition - What does Gradient Descent Algorithm mean?
The gradient descent algorithm is a strategy that helps to refine machine learning operations. The gradient descent algorithm works toward adjusting the input weights of neurons in artificial neural networks and finding local minima or global minima in order to optimize a problem.
The gradient descent algorithm is also known simply as gradient descent.
Gradient Descent Algorithm
To understand how gradient descent works, first think about a graph of predicted values alongside a graph of actual values that may not conform to a strictly predictable path. Gradient descent is about shrinking the prediction error or gap between the theoretical values and the observed actual values, or in machine learning, the training set, by adjusting the input weights. The algorithm calculates the gradient or change and gradually shrinks that predictive gap to refine the output of the machine learning system. Gradient descent is a popular way to refine the outputs of ANNs as we explore what they can do in all sorts of software areas.
Test Set:
Definition - What does Test Set mean?
A test set in machine learning is a secondary (or tertiary) data set that is used to test a machine
learning program after it has been trained on an initial training data set. The idea is that predictive models always have some sort of unknown capacity that needs to be tested out, as opposed to analyzed from a programming perspective.
A test set is also known as a test data set or test data.
Test Set
Many experts would say that a best practice is to have a test data set that is “sequestered” or kept to the end of the process. Engineers look for overfitting of the model and other issues in the training process. Ideally, there is a third set, a validation data set, that tests the classifier parameters. Then, and only then, the test set can be brought out to see how well the program was trained and whether its predictive model is accurate on new data. Although some models may avoid creating a partitioned test set altogether, this is often seen as shortsighted, because a lack of practical testing can leave a program prone to inaccuracy.
Black Box Testing:
Definition - What does Black Box Testing mean?
Black box testing is a software testing technique that focuses on the analysis of software functionality, versus internal system mechanisms. Black box testing was developed as a method of analyzing client requirements, specifications and high-level design strategies.
A black box software tester selects a set of valid and invalid input and code execution conditions and checks for valid output responses.
Black box testing is also known as functional testing or closed-box testing.
Conficker is a worm that infects computers running the Windows operating system by using
known flaws in Windows. Conficker uses dictionary attacks on administrator passwords to hijack machines and link them to a virtual machine that is remotely controlled by its creator.
Conficker
Conficker was first detected in November of 2008. It spread so rapidly that it was considered to
be the biggest computer worm infection since the SQL Slammer of 2003. Researchers believe that by January 2009, it had affected more than 9 million home, business and government computers in more than 200 countries.
The name Conficker is considered a combination of the words "configuration" and "ficker." An alternate origin suggested by Microsoft analyst Joshua Phillips is that it came from trafficconverter.biz, as a rearrangement of the letters of the domain (even though the domain name lacks the letter "k"). This site was used by Conficker as a blind drop to download its updates.
There are five variants of Conficker, designated A through E. Each variant is an improvement of the previous one and contains more defense mechanisms against detection.
The first iteration of the worm was propagated via the internet by exploiting a vulnerability in Windows' network service. The second variant of the virus added the ability to propagate via local area networks, removable storage and network sharing. Subsequent variants have improved the worm’s encryption ability and detection prevention.
Although Conficker's methods are well known by researchers, its combined use of so many defense methods makes it very difficult to totally eradicate. The constant update of the worm also serves to keep it alive. Every time a fix or cure has been made, its authors remove the vulnerability against that cure.
Propeller Head:
Definition - What does Propeller Head mean?
Propeller head is an urban slang term for someone who is exceptionally knowledgeable,
especially in a technical field. This slang has become synonymous with computer geek or techno-geek. Propeller head was first used in 1982, and is still used in technology development companies and organizations. The term was taken from cartoon characters of techie fans who happen to wear a child’s beanie cap with a propeller sticking out at the top of it.
A propelller head may also be called a prophead.
Propeller Head
The propeller beanie cap became a self-parody of science fiction fans with regards to their out- of-this-world imaginations. It was made popular by an American science fiction author and
cartoonist, Radell Faraday Ray Nelson. He claimed to be the creator of the propeller beanie as an insignia of science fiction fanatics. He is also the creator of the Beany character who wears the propeller cap, and who first appeared in a submission to a 1948 cartoon contest.
In mainstream use, the term propeller head or prophead could refer to developers, programmers
and other technically savvy people.
X-Y-Z Matrix:
Definition - What does X-Y-Z Matrix mean?
An X-Y-Z matrix is a three-dimensional structure whereby the x-axis and y-axis denote the first two dimensions and the z-axis is the third dimension. In a graphic image, the x denotes width, y denotes height and the z represents depth.
An X-Y-Z matrix is also known as a 3D matrix.
X-Y-Z Matrix:
With the advent of digital images and image processing, the X-Y-Z matrix has been employed to
represent pixel data. It is most commonly utilized in 3D computer graphics and animation. Using the X-Y-Z matrix system produces a more lifelike view in computing and graphic designs. One big advantage of using the X-Y-Z matrix system is that the manipulation of data becomes very easy and simple algorithms are defined to bring forth changes in the given pixel data of an image under operation.
Computing:
Definition - What does Computing mean?
Computing is the process of using computer technology to complete a given goal-oriented task.
Computing may encompass the design and development of software and hardware systems for a broad range of purposes - often structuring, processing and managing any kind of information - to aid in the pursuit of scientific studies, making intelligent systems, and creating and using different media for entertainment and communication.
Computing
Computing has also been defined as a branch of engineering science that deals with the systematic study of algorithmic processes, which are used to describe and transform information.
It also has specific meanings depending on the context and field in which it is used. For example, cloud computing, social computing, ubiquitous computing, parallel computing and grid computing all fall under the umbrella of the general meaning of computing while still having a specific purpose and definition separate from each other. Essentially, these are different applications of computing.
No matter how you define it, though, computing all boils down to one big fundamental question: What can be successfully automated?
In general, the cost and labor-intensive requirements for this model make it an unusual type of
neural network. More commonly, the entire structure of the artificial neuron’s synapse is modeled by sets of weighted inputs that engineers can manipulate. Unsurprisingly, though, one of the biggest current physical neural network projects is being developed by DARPA, which often works in the vanguard of new and exciting technologies.
Radial Basis Function Network
(RBF Network):
Definition - What does Radial Basis Function Network (RBF
Network) mean?
A radial basis function network is a type of supervised artificial neural network that uses
supervised machine learning (ML) to function as a nonlinear classifier. Nonlinear classifiers use sophisticated functions to go further in analysis than simple linear classifiers that work on lower- dimensional vectors.
A radial basis function network is also known as a radial basis network.
Radial Basis Function Network (RBF Network)
Using a set of prototypes along with other training examples, neurons look at the distance between an input and a prototype, using what is called an input vector.
The activation functions of artificial neurons drive outputs that can be represented in different ways to show how the network classifies data points. The radial basis function network uses radial basis functions as its activation functions. Like other kinds of neural networks, radial basis function networks have input layers, hidden layers and output layers. However, radial basis function networks often also include a nonlinear activation function of some kind. Output weights can be trained using gradient descent. Some consider an RBF approach to be relatively "intuitive" and a good way to address specialized ML problems.
Hebbian Theory
Definition - What does Hebbian Theory mean?
Hebbian theory is a theoretical type of cell activation model in artificial neural networks that
assesses the concept of “synaptic plasticity” or dynamic strengthening or weakening of synapses over time according to input factors.
Hebbian theory is also known as Hebbian learning, Hebb's rule or Hebb's postulate.
Hebbian Theory
Hebbian theory is named after Donald Hebb, a neuroscientist from Nova Scotia who wrote “The Organization of Behavior” in 1949, which has been part of the basis for the development of artificial neural networks.
In modern artificial neural networks, algorithms can update weights of neural connections.
Professionals sometimes talk about “Hebb’s rule” that describes how these connections work and how they change. Part of the appeal of Hebbian theory is the idea that by changing neural weights and associations, engineers can get different results out of sophisticated artificial neural networks.
Feedforward Neural Network
Definition - What does Feedforward Neural Network mean?
The feedforward neural network is a specific type of early artificial neural network known for its simplicity of design. The feedforward neural network has an input layer, hidden layers and an output layer. Information always travels in one direction – from the input layer to the output layer – and never goes backward.
Feedforward Neural Network
The feedforward neural network, as a primary example of neural network design, has a limited architecture. Signals go from an input layer to additional layers. Some examples of feedforward designs are even simpler. For example, a single-layer perceptron model has only one layer, with a feedforward signal moving from a layer to an individual node. Multi-layer perceptron models, with more layers, are also feedforward.
In the days since scientists devised the first artificial neural networks, the technology world has made all sorts of progress in building more sophisticated models. There are recurrent neural networks and other designs that contain loops or cycles. There are models that involve backpropagation, where the machine learning system essentially optimizes by sending data back through a system. The feedforward neural network does not involve any of this type of design, and so it is a unique type of system that is good for learning these designs for the first time.
Bit Stuffing:
Definition - What does Bit Stuffing mean?
Bit stuffing is the process of inserting noninformation bits into data to break up bit patterns to
affect the synchronous transmission of information. It is widely used in network and communication protocols, in which bit stuffing is a required part of the transmission process. Bit stuffing is commonly used to bring bit streams up to a common transmission rate or to fill frames. Bit stuffing is also used for run-length limited coding.
Bit Stuffing
In order to fill bit frames, the position where the new bits are stuffed is communicated to the receiving end of the data link. The receiver removes the extra bits to return the bit streams to their original bit rate. This is used when a communication protocol requires a fixed frame size. Bits are inserted to make the frame size equal to the defined frame size.
