Communication network based on point-to-point communication

LAN typically used transmission technology consisting of single cable to which all machines are connected
. Traditional LAN's run at speeds of 10 to 100 Mbps (but now much higher speeds can be achieved)
. The most common LAN typologies are bus, ring and star
. A typical LAN is shown in Fig

Communication network based on point-to-point communication

Figure  Communication network based on point-to-point communication As a general rule (although there are many exceptions), smaller, geographically localized networks tend to use broadcasting, whereas larger networks normally use are point-to point communication
. Classification based on Scale Alternative criteria for classifying networks are their scale
. They are divided into Local Area (LAN), Metropolitan Area Network (MAN) and Wide Area Networks (WAN)
. Local Area Network (LAN) LAN is usually privately owned and links the devices in a single office, building or campus of up to few kilometers in size
. These are used to share resources (may be hardware or software resources) and to exchange information
. LAN's are distinguished from other kinds of networks by three categories: their size, transmission technology and topology
. LAN's are restricted in size, which means that their worst-case transmission time is bounded and known in advance
. Hence this is more reliable as compared to MAN and WAN
. Knowing this bound makes it possible to use certain kinds of design that would not otherwise be possible
. It also simplifies network management.  

Network Technologies

This system generally also allows possibility of addressing the packet to all destinations (all nodes on the network)
. When such a packet is transmitted and received by all the machines on the network
. This mode of operation is known as Broadcast Mode
. Some Broadcast systems also supports transmission to a sub-set of machines, something known as Multi casting
 Point-to-Point Networks A network based on point-to-point communication is shown in Fig
. The end devices that wish to communicate are called stations
. The switching devices are called nodes
. Some Nodes connect to other nodes and some to attached stations
. It uses FEM or TAM for node-to-node communication
. There may exist multiple paths between a source-destination pair for better network reliability
. The switching nodes are not concerned with the contents of data
. Their purpose is to provide a switching facility that will move data from node to node until they reach the destination. 

Network Technologies

 Network Technologies There is no generally accepted taxonomy into which all computer networks fit, but two dimensions stand out as important: Transmission Technology and Scale
. The classifications based on these two basic approaches are considered in this section
.Classification Based on Transmission Technology Computer networks can be broadly categorized into two types based on transmission technologies: • Broadcast networks • Point-to-point networks Broadcast Networks Broadcast network have a single communication channel that is shared by all the machines on the network as shown in Fig sand  All the machines on the network receive short messages, called packets in certain contexts, sent by any machine
. An address field within the packet specifies the intended recipient
. Upon receiving a packet, machine checks the address field
. If packet is intended for itself, it processes the packet; if packet is not intended for itself it is simply ignored. 

Computer Science and Engineering

The bandwidth was clearly a problem, and in the late 1970,s and early,another new communication technique known as Local Area Networks (LAN,s) evolved, which helped computers to communicate at high speed over a small geographical area
. In the later years use of optical fiber and satellite communication allowed high-speed data communications over long distances.  

Historical Background of Computer Science and Engineering

With the advancement of VLASIC technology, and particularly, after the invention of microprocessors in the early 1970,s the computers became smaller in size and less expensive, but with significant increase in processing power

. New breed of low-cost computers known as mini and personal computers were introduced

. Instead of having a single central computer, an organization could now afford to own a number of computers located in different departments and sections

. Side-by-side, riding on the same VLASIC technology the communication technology also advanced leading to the worldwide deployment of telephone network, developed primarily for voice communication

. An organization having computers located geographically dispersed locations wanted to have data communications for diverse applications

. Communication was required among the machines of the same kind for collaboration, for the use of common software or data or for sharing of some costly resources

. This led to the development of computer networks by successful integration and cross-fertilization of communications and geographically dispersed computing facilities

. One significant development was the APPARENT (Advanced Research Projects Agency Network)

. Starting with four-node experimental network in 1969, it has subsequently grown into a network several thousand computers spanning half of the globe, from Hawaii to Sweden

. Most of the present-day concepts such as packet switching evolved from the PLANETARY project

. The low bandwidth (kHz on a voice grade line) telephone network was the only generally available communication system available for this type of network. 

Historical Background

 Historical Background The history of electronic computers is not very old
. It came into existence in the early 1950,s and during the first two decades of its existence it remained as a centralized system housed in a single large room
. In those days the computers were large in size and were operated by trained personnel
. To the users it was a remote and mysterious object having no direct communication with the users
. Jobs were submitted in the form of punched cards or paper tape and outputs were collected in the form of computer printouts
. The submitted jobs were executed by the computer one after the other, which is referred to as batch mode of data processing
. In this scenario, there was long delay between the submission of jobs and receipt of the results
. In the 1960,s computer systems were still centralize, but users provided with direct access through interactive terminals connected by point-to-point low-speed data links with the computer
. In this situation, a large number of users, some of them located in remote locations could simultaneously access the centralized computer in time-division multiplexed mode
. The users could now get immediate interactive feedback from the computer and correct errors immediately
. Following the introduction of on-line terminals and time-sharing operating systems, remote terminals were used to use the central computer. 

Specific Instructional Objective

On Completion of this lesson, the students will be able to: 
• Define Computer Networks
 • State the evolution of Computer Networks
 • Categorize different types of Computer Networks 
• Specify some of the application of Computer Networks Introduction The concept of Network is not new
. In simple terms it means an interconnected set of some objects
. For decades we are familiar with the Radio, Television, railway, Highway, Bank and other types of networks
. In recent years, the network that is making significant impact in our day-to-day life is the Computer network
. By computer network we mean an interconnected set of autonomous computers
. The term autonomous implies that the computers can function independent of others
. However, these computers can exchange information with each other through the communication network system
. Computer networks have emerged as a result of the convergence of two technologies of this century- Computer and Communication as shown in Fig
. The consequence of this revolutionary merger is the emergence of a integrated system that transmit all types of data and information
. There is no fundamental difference between data communications and data processing and there are no fundamental differences among data, voice and video communications
. After a brief historical background in Section , Section 
introduces different network categories
. A brief overview of the applications of computer networks is presented in Section 
. Finally an outline of the entire course is given in

American Journal of Computer Science and Information Technology

Recently, TQWT is one of the decomposition processes widely using in medical sciences as biomedical signal processing and vibration signals like seismic signals pro

. According to [19], by comparing with other discrete-time wavelet transforms, TQWT is very easy and efficient method in terms of tuning the parameters

. TQWT uses very efficient radix-2 FFT’s and filters having non-rational transfer functions like used in Fractional Spline Wavelet Transform (FSWT)

. In this study, TQWT decomposes the EMG signal into several high pass and low pass sub-bands

. In this paper, we are using two-channel filter banks in 7-stages to decompose EMG signal

. Each filter bank contains a low pass and a high pass filter with their respective scaling factors α and β

. The first level filter bank takes EMG signal as input whose sampling frequency is fs which will give two outputs comprises of a low pass and a high pass subband whose sampling frequencies are αfs and βfs respectively

. The low pass sub-band obtained from first stage filter bank will be the input for second level filter bank and it continues further upto 7 stages. 

American Journal of Computer Science and Information Technology

In this paper, tunable-Q factor wavelet transform (TQWT) based features are extracted for the classification of ALS and healthy EMG signals

. TQWT decomposes EMG signal into subbands and features such as mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy are extracted from the sub-bands

. The obtained statistical features are fed as inputs to k-NN and LS-SVM classifiers to classify ALS and healthy EMG signals

. The organization of the remaining paper is as follows: Section

. 2 contain, ALS and healthy EMG signals dataset, the proposed methodology- TQWT, and features extraction will present

. The results and discussions of classifying ALS and healthy EMG signals are in Section 3 and Section 4 conclude the paper

.The dataset comprises of 89 ALS and 133 healthy EMG signals which have been taken from, dataset R002 at http:// www.emglab.net [17]

. It consists of two groups namely ALS and healthy

. The healthy EMG dataset has been taken from 10 people of 21-37 years age, among them, females are 4 and males are 6, they didn’t have any kind of neuromuscular disorders

. ALS dataset has been taken from 8 patients among them 4 are females and 4 are males whose age is between 35-67 years

. Among the 8 patients taken, 5 have died within a few years

. A standard needle electrode has been used for accession

. Audio and Visual feedback has been used for monitoring the signal quality [18]

. The EMG signal has been amplified at a frequency range of 5 Hz-5 kHz filter settings and sampled at a frequency of 10 kHz

. Figure 1 shows the block diagram of proposed method explaining the whole method stepwise. Figure 2 shows the example of each of the ALS and healthy EMG signals.

American Journal of Computer Science and Information Technology

Discrete cosine transform (DCT) is used to extract the features from one of the MUAPs having a dynamic range, and the obtained feature set has been given as input to the k-Nearest Neighbor classifier (k-NN) to classify EMG signals [15]

. The coefficients obtained from autoregressive analysis have been given as input to neuro-fuzzy system for classifying EMG signals [16].

In this paper, tunable-Q factor wavelet transform (TQWT) based features are extracted for the classification of ALS and healthy EMG signals

. TQWT decomposes EMG signal into subbands and features such as mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy are extracted from the sub-bands

. The obtained statistical features are fed as inputs to k-NN and LS-SVM classifiers to classify ALS and healthy EMG signals

. The organization of the remaining paper is as follows: Section

. 2 contain, ALS and healthy EMG signals dataset, the proposed methodology- TQWT, and features extraction will present

. The results and discussions of classifying ALS and healthy EMG signals are in Section 3 and Section 4 conclude the paper.

American Journal of Computer Science and Information Technology

The feature extraction process plays an important role for analysis and classification of EMG signals

. The various time, frequency, and time-frequency domain features have been used for classifying EMG signals

. Time domain features like autocorrelation, square integral, mean absolute value, variance and integrated EMG have been used for classification of ALS and healthy EMG signal [3,4]

. Frequency domain features like total power, peak frequency, median frequency, and mean power have been explored for classification of ALS and healthy EMG signals [5

]. Time-frequency domain features like root mean square (RMS), turns ratio, autoregressive coefficients have been used for classification of EMG signals [6]

. The classification of ALS and healthy EMG signals has also been done through short time Fourier transform (STFT) [7]

.Motor Unit Action Potentials (MUAPs) analysis and feature extraction was used with binary-support vector machine (SVM) classifier for the discrimination of ALS and healthy EMG signals [8]

. Time-domain based features like autoregression (AR), waveform length, and mean absolute value were given as inputs to multilayer perceptron (MLP) and artificial neural network (ANN) classifiers for classifying ALS and healthy EMG signals [9]

. The ensemble empirical mode decomposition (EEMD) decomposes EMG signal into set of intrinsic mode functions (IMFs), where the noisy IMFs separated using fast independent component analysis (ICA) algorithm and time domain features set used as input to linear discriminant analysis classifier for classification of EMG signals [10]

. Mel-frequency cepstral coefficients (MFCCs) based methods were used for the extraction of features from higher order MUAPs of an EMG signal, the obtained feature set have been applied to the knearest neighbour (k-NN) classifier for classifying ALS and healthy EMG signals [11]

. Spectral features like power spectral density, amplitude modulated bandwidth, and frequency modulated bandwidth were extracted by using improved empirical mode decomposition (IEMD) for classifying EMG signals [12]

. A new hybridization of SVM with particle swarm optimisation (PSO) as PSO-SVM used for classifying EMG signals [13]

. Multiscale principal component analysis (MSPCA) and discrete wavelet transform (DWT) have been used to extract the features and de-noising the EMG signal. The obtained features are applied to decision tree algorithms for classifying EMG signals [14].

American Journal of Computer Science and Information Technology

Electromyography is recording and evaluating the electrical activities during contraction and relaxation of striated muscles
. The obtained electrical signal is called as electromyogram (EMG)
. EMG is being used in many fields such as exercise physiology
, human factors, etc., [1]
. In medical sciences, EMG is used for diagnosis of neuromuscular diseases based on the motor unit
. Motor unit is a combination of muscle fibers and motor neurons
. A motor neuron is a nerve cell originated in the spinal cord
. Upper motor neurons are situated at brain which sends signals to lower motor neurons at spinal cord to control the muscular activities
. Muscles present in eyes
, tongue, and the whole face are under the control of lower motor neurons
. The disorder of these motor neurons will create many diseases
, mostly amyotrophic lateral sclerosis (ALS) [1]
. It was first brought into international attention in 1939 because of Lou Gehrig’s speech at Yankee stadium
, and so this disease is also called with his name
. It results in the gradual degeneration and damage of nerve cells in the brain and spinal cord that controls the individual action of striated muscles
. It results in twitching
, cramping of muscles, slurred
, nasal speech
, and difficulty in chewing or swallowing
. ALS generally affects people around the age group of 65 years and 50 years in case of inherited patients [2]
. The manual analysis of ALS signals is a very monotonous task which requires professionals to diagnose
. An automatic, accurate, and fast method is required to diagnose the ALS EMG signals.

American Journal of Computer Science and Information Technology

Amyotrophic lateral sclerosis (ALS) is a disease, affects the nerve cells in brain and spinal cord that controls the voluntary action of muscles, which identification can be possible by processing electromyogram (EMG) signals

. This study focuses on the extraction of features based on tunable-Q factor wavelet transform (TQWT) for classifying ALS and healthy EMG signals

. TQWT decomposes EMG signal into sub-bands and these sub-bands are used for extraction of statistical features namely mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy

. The obtained features are tested on k-Nearest Neighbour and least squares support vector machines classifiers for the classification of ALS and healthy EMG signals

. The proposed method obtained better classification results as compared to other existing methods.

CS Fundamentals Curriculum Guide

CS Fundamentals Curriculum Guide As always, it is thanks to our generous donors that we were able to develop and offer this curriculum at no cost to schools, teachers, or students
: Microsoft, Infosys Foundation USA, Facebook, Omidyar Network, Google, Ballmer Family Giving, Ali and Hadi Partovi, Bill Gates, The Bill and Melinda Gates Foundation, BlackRock, Jeff Bezos, John and Ann Doerr, Mark Zuckerberg and Priscilla Chan, Quadrivium Foundation, Amazon Web Services, The Marie-Josee and Henry R
. Kravis Foundation, Reid Hoffman, Drew Houston, Salesforce, Sean N
. Parker Foundation, Smang Family Foundation, Verizon
. Who is This For?  Computer Science Fundamentals was built with elementary school educators in mind
. Courses A-F have been specifically tailored to students in Kindergarten through 5th grade, and no prior experience is assumed
. The lessons in CS Fundamentals are presented with the understanding that many teachers will not have any previous computer science training, and educators are therefore encouraged to learn along with their students.