Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
GUI based handwritten digit recognition using CNNAbhishek Tiwari
This project is to create a model which can recognize the digits as well as also to create GUI which is user friendly i.e. user can draw the digit on it and will get appropriate output.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
Proposing a new method of image classification based on the AdaBoost deep bel...TELKOMNIKA JOURNAL
Image classification has different applications. Up to now, various algorithms have been presented
for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate
is an issue which many researches have been carried out about it. This research intends to optimize
the problem with hybrid methods and deep learning. The hybrid methods were developed to improve
the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative
probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In
fact, this method is anunsupervised method, in which all layers are one-way directed layers except for
the last layer. So far, various methods have been proposed for image classification, and the goal of this
research project was to use a combination of the AdaBoost method and the deep belief network method to
classify images. The other objective was to obtain better results than the previous results. In this project, a
combination of the deep belief network and AdaBoost method was used to boost learning and the network
potential was enhanced by making the entire network recursive. This method was tested on the MINIST
dataset and the results were indicative of a decrease in the error rate with the proposed method as compared
to the AdaBoost and deep belief network methods.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
Representation and recognition of handwrittendigits using deformable templates, This working prototype system can detect handwritten digits from a scanned image of an input form by using deformable templates technique.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
A Study of Social Media Data and Data Mining TechniquesIJERA Editor
Artificial Neural Networks (ANNs) has highly interconnected elements (neurons) which unanimously work to solve the specific problems. Recently ANNs are involved in the areas like image and speech recognition, character & pattern recognition with statistical analysis and data modeling for solving the problems related to forecasting & classification. In this paper, we are focusing on learning process of a neural network.
My slides from my 3-hour tutorial on mesoscale structures in networks from the 2016 Lake Como School on Complex Networks (http://ntmb.lakecomoschool.org/).
After my talk, Tiago Peixoto gave a talk on statistical inference of large-scale mesoscale structures in networks. His presentation, which takes a complementary perspective from mine, is available at the following website: https://speakerdeck.com/count0/statisical-inference-of-generative-network-models
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
GUI based handwritten digit recognition using CNNAbhishek Tiwari
This project is to create a model which can recognize the digits as well as also to create GUI which is user friendly i.e. user can draw the digit on it and will get appropriate output.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
Proposing a new method of image classification based on the AdaBoost deep bel...TELKOMNIKA JOURNAL
Image classification has different applications. Up to now, various algorithms have been presented
for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate
is an issue which many researches have been carried out about it. This research intends to optimize
the problem with hybrid methods and deep learning. The hybrid methods were developed to improve
the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative
probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In
fact, this method is anunsupervised method, in which all layers are one-way directed layers except for
the last layer. So far, various methods have been proposed for image classification, and the goal of this
research project was to use a combination of the AdaBoost method and the deep belief network method to
classify images. The other objective was to obtain better results than the previous results. In this project, a
combination of the deep belief network and AdaBoost method was used to boost learning and the network
potential was enhanced by making the entire network recursive. This method was tested on the MINIST
dataset and the results were indicative of a decrease in the error rate with the proposed method as compared
to the AdaBoost and deep belief network methods.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.
Representation and recognition of handwirten digits using deformable templatesAhmed Abd-Elwasaa
Representation and recognition of handwrittendigits using deformable templates, This working prototype system can detect handwritten digits from a scanned image of an input form by using deformable templates technique.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
A Study of Social Media Data and Data Mining TechniquesIJERA Editor
Artificial Neural Networks (ANNs) has highly interconnected elements (neurons) which unanimously work to solve the specific problems. Recently ANNs are involved in the areas like image and speech recognition, character & pattern recognition with statistical analysis and data modeling for solving the problems related to forecasting & classification. In this paper, we are focusing on learning process of a neural network.
My slides from my 3-hour tutorial on mesoscale structures in networks from the 2016 Lake Como School on Complex Networks (http://ntmb.lakecomoschool.org/).
After my talk, Tiago Peixoto gave a talk on statistical inference of large-scale mesoscale structures in networks. His presentation, which takes a complementary perspective from mine, is available at the following website: https://speakerdeck.com/count0/statisical-inference-of-generative-network-models
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
5th sem
1. Course Title: Computer Networks
Course no: CSC-301 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: Discussion on types of networking techniques, Internet, IPV.
Goal: This course introduces concept of computer networking and discuss the different
layers of networking model.
Course Contents:
Unit 1. 33 Hrs.
1.1 Computer Network: Introduction to networking, computer network, Internet, the
network edge: end system, clients, server, connection oriented and connectionless
service, network core, network access and physical media, ISPs and back bone.
1.2 Protocol Layers: Introduction, layered architecture, The Internet protocol stack,
network entities and layers.
1.3 Application Layer: Introduction, principles of application layer protocols, the web
and HTTP, file transfer, Domain Name Service [DNS]: Working of DNS, DNS
records, DNS messages.
1.4 Transport Layer : Introduction, relationship between transport layer and network
layer, transport layer in the Internet, multiplexing and demultiplexing,
connectionless transport, reliable data transfer: Building a reliable data transfer
protocol, pipelined reliable data transfer protocol, Go-Back-N ( GBN ), selective
repeat ( SR ), connection oriented transport : TCP , TCP connection, TCP
segment structure, time estimation and time out, flow control, Principle of
congestion control: The causes and costs of congestion, approaches to congestion
control.
1.5 Network Layer : Introduction, network service model, datagrams and virtual
circuit service, routing principles: A link state routing algorithm, the distance
vector routing algorithm, hierarchical routing, The Internet protocol ( IP ): IPV4
addressing, datagram format, IP datagram fragmentation, Internet Control
Message Protocol [ ICMP], Network address translator, routing in the Internet,
IPV6, Multicasting routing.
Unit 2. 12 Hrs.
2.1 Link Layer and Local Area Networks: Introduction, Data link layer: the services
provided by the link layer, error detection and error correction techniques,
multiple access protocols, LAN addresses and Address Resolution Protocol,
Ethernet, Wireless Links: IEEE 802.11b, Bluetooth, point to poin protocol (PPP),
Asynchronous Transfer Mode (ATM), frame relay.
2. 2.2 Multimedia Networking: Introduction, multimedia networking application,
streaming audio and video.
2.3 Network Management: Introduction, The infrastructure for network management.
Laboratory works: Developing the network system in the small scale.
Text Books: Computer Networking; A Top Down Approach Featuring The
Internet, 2nd
Edition, Kurose James F., Ross W. Keith PEARSON
EDUCATON ASIA
Homework
Assignment: Assignment should be given from the above units in throughout the
semester.
Computer Usage: No specific
Prerequisite: C, Digital logic
Category Content: Science Aspect: 50%
Design Aspect: 50%
3. Course Title: Simulation and Modeling
Course no: CSC-302 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: This course provides the discrete and continuous system, generation
of random variables, analysis of simulation output and simulation
languages.
Goal: This course will provide students the concepts of simulation.
Course Contents:
Unit 1. Introduction to Simulation 6 Hrs.
Continuous and discrete systems, System simulation, Real time simulation, Types of
Simulation Models, Steps in simulation Study, Phases of a simulation study,
Advantages of simulation, Limitations of the Simulation Technique, Areas of
applications
Unit 2. Simulation of Continuous Systems 5 Hrs.
Queuing system, Markov chains, Differential and partial differential equations
Unit 3. Random Numbers 10 Hrs.
Random Numbers, Random Number Tables, Pseudo Random Numbers, Generation
of Random Number, Testing Numbers for Randomness, Uniformity Test, Chi-square
test, Testing for auto correlation, Poker Test
Unit 4. Verification and Validation of Simulation Models 6 Hrs.
Model building, verification and Validation, Verification of Simulation Models,
Calibration and Validation of Models
Unit 5. Analysis of Simulation Output 8 Hrs.
Estimation methods, Simulation run statistics, Replication of runs, Elimination of
internal bias
Unit 6. Simulation Languages 10 Hrs.
Basic concept of Simulation tool, Discrete systems modeling and simulation,
Continuous systems modeling and simulation, Data and control, Hybrid simulation,
Feedback systems: typical applications.
4. Text Books: Jerry Banks, John S. Carson, Barry L. Nelson, David M. Nicol “Discrete -
Event system simulation", Pearson education.
References: G. Gorden, “System Simulation", Prentice Hall of India M. Law and R.F.
Perry, "Simulation: A problem-solving approach", Addison
Wesley publishing company.
M. Law and W.D. Kelton, “Simulation Modeling and analysis", McGraw
Hill, 1991.
Laboratory works: Laboratory exercises using simulation and modeling packages and
student also develop their own simulation software.
5. Course Title: Design and Analysis of Algorithms
Course no: CSC-303 Full Marks: 90+10
Credit hours: 3 Pass Marks: 36+4
Nature of course: Theory (3 Hrs.)
Course Synopsis: Methods and tools for analyzing different algorithms. Different
approaches of designing efficient algorithms like divide and conquer
paradigm, greedy paradigm, dynamic programming. Algorithms
pertaining various problems like sorting, searching, shortest path,
spanning trees, geometric problems etc. NP-complete problems.
Goal: Competency in analyzing different algorithms encountered. Ability to conquer the
problem with efficient algorithm using the algorithm development paradigms.
Course Contents:
Unit 1. 10 Hrs.
1.1 Algorithm Analysis: worst, best and average cases, space and time complexities.
Mathematical background: asymptotic behavior, solving recurrences.
1.2 Data Structures Review: linear data structures, hierarchical data structures, data
structures for representing graphs and their properties. Search structures: heaps,
balanced trees, hash tables.
Unit 2. 14 Hrs.
2.1 Divide and Conquer: Concepts, applications, sorting problems(quick, merge),
searching (binary), median finding problem and general order statistics, matrix
multiplications.
2.2 Greedy Paradigm: Concepts, applications, Knapsack problem, job sequencing,
Huffman codes.
2.3 Dynamic Programming: Concepts, applications, Knapsack problem, longest
common subsequence, matrix chain multiplications.
Unit 3 21 Hrs.
3.1 Graph Algorithms: breadth-first and depth-first search and their applications,
minimum spanning trees (Prim's and Kruskal's algorithms), shortest path
problems (Dijkstra's and flyod's algorithms), algorithm for directed acyclic
graphs (DAGs).
3.2 Geometric Algorithms: Concepts, polygon triangulation, Convex hull
computation.
6. 3.3 NP Completeness: Introduction, class P and NP, cooks theorem, NP complete
problems: vertex cover problem.
3.4 Introductions: Randomized algorithms concepts, randomized quick sort,
approximation algorithms concepts, vertex cover problem.
Textbook: T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to
Algorithms, 2nd
Edition, MIT Press, 2001 ISBN: 0-262-530-910.
Reference: G. Brassard and P. Bratley, Fundamentals of Algorithmics, Prentice-
Hall, 1996 ISBN: 0-13-335068-1.
Prerequisites:Good programming concepts (any language), Data structures and their
properties, mathematical concepts like methods of proof,
algorithmic complexity, recurrences, probability.
Assignments: This course deals with wide range of problem domain so sufficient
number of assignments from each unit and subunit should be given
to the students to familiarize the concepts in depth.
Lab: The motive of this course is to provide good theoretical and mathematical
background of algorithms and their analysis, however it is advisable to provide
programming assignments that aid the students learn the behavior of the
algorithms.
7. Course Title: Knowledge Management
Course no: CSC-304 Full Marks: 90+10
Credit hours: 3 Pass Marks: 36+4
Nature of course: Theory (3 Hrs.)
Course Synopsis: Study of knowledge intensive organization, knowledge management
issues.
Goal: This course introduces fundamental concept of knowledge and different
managerial issues in managing the knowledge.
Course Contents:
Unit. 1 16 Hrs.
1.6 Introduction of Knowledge Management: Knowledge and its importance,:
Definition of Knowledge, Knowledge management, From information to
knowledge.
1.7 The Knowledge Edge: A common theme, Intellectual Capital, Drivers of
Knowledge Management, Knowledge-centric drivers, Technology drivers,
Organizational structure based drivers, Personnel focused drivers, Process
drivers, Economic drivers, Creating the knowledge edge.
1.8 From Information to Knowledge: Different between knowledge and
information, from data to knowledge, Types of knowledge, the three
fundamental steps, Knowledge management systems and existing technology.
Business and knowledge.
Unit 2: 20 Hrs.
2.4 Implementing Knowledge Management: The 10 step knowledge management
road map, Infrastructure evaluation, Knowledge management system analysis,
Deployment, Metrics for performance evaluation.
2.5 The Leverage Infrastructure: Leveraging the Internet, Aligning knowledge
management and business strategy.
2.6 Knowledge Management System Analysis, Design & Development:
Infrastructural foundation, Knowledge audit and analysis, Designing the
knowledge management team, Creating knowledge management system blue
print, Developing the knowledge management system.
Unit 3: 9 Hrs.
3.1 Knowledge Management System Development: Prototyping & development,
Reward structure.
3.2 Metrics for Knowledge Work: Traditional Metrics, Pitfalls in choosing metrics.
8. 3.3 Code Optimization: The principal sources of optimization, Optimization of
basic blocks, loops in flow graphs.
Laboratory works: Developing small scale KM project.
Text / References books: The Knowledge Management Tool Kit, Amrit Tiwana,
Pearson Education Asia
Homework
Assignment: Assignment should be given from the above units in throughout the
semester.
Computer Usage: No specific
Prerequisite: C, Management Information System
Category Content: Science Aspect: 60%
Design Aspect: 40%
9. Course Title: Microprocessor Based Design
Course no: CSC-305 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Goal: The course objective is to apply the knowledge of microprocessor with other
digital/analog system and interfacing to design a complete system.
Course Contents
Unit 1.Interfacing Concept 4 Hrs.
Interfacing, Interfacing Types, Address Decoding, I/O Mapping, Memory
Mapping, I/O Memory Mapping, Registers and Input/output Registers, PC
Interfacing Techniques.
Unit 2.Digital Interfacing 12 Hrs.
Input/output and Microcomputer, Simple input, Simple output, Programmable
Parallel Ports, Handshaking, Single handshaking IO, Double handshaking IO,
Introduction to Programmable Peripheral Interface 8255 A, Functional Block
Diagram, Different Modes of Operations, Introduction to Programmable Interval
Timer 8253 and Difference between 8253 and 8254, Functional Block Diagram,
Different Modes of Operation, Keyboard Interfacing, Alphanumeric Display
Interfacing, Microcomputer ports Interfacing to high-power devices.
Unit 3. Interrupts & Interrupt Controller 6 Hrs.
Interrupt Vector Tables, Types of Interrupts, Assembly Language program and
Interrupt Procedure Hardware interrupts and Applications, Examples of Various
ISR, Introduction to Programmable Interrupt Controller 8259, Functional Block
Diagram, Operations of Interrupt, Programming of 8259
Unit 4.Analog Interfacing 5 Hrs.
Operational Amplifier Basics, Sensors and Transducers, Digital to Analog
Conversion and Analog to Digital Conversion – Basics, Operations, Specification,
Applications and Interfacing, A Microcomputer Based Industry-Process Control
System.
Unit 5.Serial and Parallel Data Communication 6 Hrs.
Synchronous and Asynchronous Data Communication, Parity and other error
control, Baud rates, Serial Interface Device, Serialization, RS 232 Interface Pin
Description, Simplex Connection, Duplex Connection, Full Duplex Connection,
Connection Between DTE to DTE, Connection to Printers and Zero Modem.
10. Unit 6.Microcontroller & Interfacing 8 Hrs.
General Microcontroller Concept, Pin Configuration, I/O Port Structure, Memory
Organization, Special Function Registers, External Memory, Reset Operations,
Instruction Set, Timer Operation, Serial Port Operation, Interrupt Design and
Processing, Assembly Instructions and Programming.
Unit 7.Grounding and Shielding 4 Hrs.
Outline for grounding and shielding, Single point grounding and grouped loop,
Noise, noise coupling mechanism and prevention, Filtering and smoothing,
Different kinds of shielding mechanism, Protecting against electrostatic discharge,
Line filters, isolators and transient suppressors
Laboratory works:Assembly language based programming. PPI, ADC and various
interfacing with RS232, Printer Port should be experimented. At the
semester end, individual project work based on microcontroller for
industry process control should be done.
Lab exercise may comprise some of the followings:
1. Assembly language programming
2. Simple data transfer using PPI
3. Handshake transfer using PPI
4. Interfacing of A/D converter using PPI
5. Interfacing of A/D using Micro controller
6. Interfacing of A/D converter using Printer port
7. Demonstration of other interfacing techniques and devices
8. Writing an interrupt Service Routine
Text / Reference books:
1. D. V. Hall, Microprocessors and Interfacing - Programming and Hardware,
McGraw Hill
2. K. J. Ayala, The 8051 Microcontroller: Architecture, Programming and
Applications, West Publishing
3. K.R. Fowler, “Electronic Instrument Design”, New York Oxford, Oxford
University Press.
4. E.O. Duebelin, “Measurement System Application and Design” Tata McGraw
Hill, New Delhi
11. Course Title: Applied Logic
Course no: CSC-306 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: This course contain the main feature of different logics.
Goal: The course objective is to provide the basic concepts and techniques of the logics
used in computer science.
Course Contents:
Unit 1.Introduction 4 Hrs.
Introduction to Logic, Nature of Argument, Truth and Validity, Symbolic Logic,
Statements, Conditional Statements, Statement Forms
Unit 2.Deduction and Deductive Systems 6 Hrs.
Formal Proof of Validity, The Rule of Replacement, The Rule of Conditional
Proof, The Rule of Indirect Proof, Proofs of Tautologies, Formal Deductive
Systems, Attribute of Formal Deductive Systems, Logicist Systems
Unit 3.Propositional Logic 6 Hrs.
Syntax of Propositional Logic, Semantics of Propositional Logic, Calculations,
Normal Form, Applications
Unit 4.Predicate Logic 8 Hrs.
Predicate Logic, Order of Predicate Logic, Syntax of Predicate Logic, Semantics
of Predicate Logic, Consequences, Calculations, Normal Form
Unit 5.Resolution & Proofs 10 Hrs.
Resolution, Resolution in Propositional Logic, Unification of Clauses, Resolution
in Predicate Logic, Horn Clauses, Proof in Propositional Logic and Predicate
Logic, Axiomatic Systems, Adequacy, Compactness, Soundness.
Unit 6.Program Verification 5 Hrs.
Issue of Correctness, Partial Correctness, Hoare Proof, Total Correctness.
Unit 7.Some Other Logics 6 Hrs.
Intuitionistic Logic, Lukasiewicz Logic, Probabilistic Logic, Fuzzy Logic, Default
Logic, Autoepistemic Logic.
12. Laboratory works: Laboratory exercises should be conducted in any logic
programming language like LISP or PROLOG.
Text / Reference books:
1. Arindama Singh, Logics for Computer Science, Prentice Hall of India
2. Irving M. Copi, Symbolic Logic, 5th Edition, Prentice Hall of India
13. Course Title: E-Governance
Course no: CSC-307 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: This course contains concepts of E-Governance policies and data
warehousing / data mining.
Goal: To provide the knowledge of good governance using information and
communication technologies and case studies of different countries.
Course Contents:
Unit 1. Introduction 4 Hrs.
E-Governance: Needs of E-Governance, Issues in E-Governance applications and the
Digital Divide; Evolution of E-Governance, Its scope and content; Present global
trends of growth in E-Governance: Other issues.
Unit 2. Models of E-Governance 10 Hrs.
Introduction; Model of Digital Governance: Broadcasting/ Wilder Dissemination
Model, Critical Flow Model, Comparative Analysis Model, Mobilization and
Lobbying Model, Interactive-service Model/Government-to-Citizen-to-Government
Model (G2C2G); Evolution in E-Governance and Maturity Models: Five Maturity
Levels, Characteristics of Maturity Levels, Key areas, Towards Good Governance
through E-Governance Models.
Unit 3. E-Governance Infrastructure and Strategies 6 Hrs.
E-readiness: Digital System Infrastructure, Legal Infrastructural Preparedness,
Institutional Infrastructural Preparedness, Human Infrastructural Preparedness,
Technological Infrastructural Preparedness; Evolutionary Stages in E-Governance.
Unit 4. Data Warehousing and Data Mining in Government 5 Hrs.
Introduction; National Data Warehouses: Census Data, Prices of Essential
Commodities; Other areas for Data Warehousing and Data Mining: Agriculture,
Rural Development, Health, Planning, Education, Commerce and Trade, Other
Sectors.
Unit 5. Case Studies 20 Hrs.
Nepalese Context: Cyber Laws, Implementation in the Land Reform, Human
Resource Management Software; India: NICNET, Collectorate, Computer-aided
Administration of Registration Department (CARD), Smart Nagarpalika, National
Reservoir Level and Capacity Monitoring System, Computerization in Andra
Pradesh, Ekal Seva Kentra, Sachivalaya Vahini, Bhoomi, IT in Judiciary, E-Khazana,
DGFT, PRAJA, E-Seva, E-Panchyat, General Information Services of National
14. Informatics Centre; E-Governance initiative in USA; E-Governance in China; E-
Governance in Brazil and Sri Lanka.
Text / Reference books:
1. E-Governance: Concepts and Case Studies, C.S.R. Prabhu, Prentice-Hall of India
Private Limited, 2004.
2. Backus, Michiel, e-Governance in Developing Countries, IICD Research Brief, No. 1,
March 2001.
15. Course Title: Concepts of Wireless Networking
Course no: CSC-308 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.)
Course Synopsis: This course contains the concept of wireless networking technology.
Goal: To provide the concept and working principle for wireless communication and
networking.
Course Contents:
Unit 1. Introduction 4 Hrs.
History of wireless communication, Challenges in wireless communication
networking, Wireless communication standards.
Unit 2. Wireless Channel Characterization 6 Hrs.
Multipath propagation environment, Linear time-invariant channel model, Channel
correlation function, Large-scale path loss and shadowing, Small-scale multipath
fading.
Unit 3. Bandpass Transmission Techniques 7 Hrs.
Introduction, Signal space and decision regions, Digital modulation, Power spectral
density, Probability of transmission error.
Unit 4. Receiver Techniques for fading Dispersive Channels 5 Hrs.
Overview of channel impairment mitigation techniques, Diversity, Channel
equalization.
Unit 5. Fundamental of Cellular Communications 8 Hrs.
Introduction, Frequency reuse and mobility management, Cell cluster concept,
Cochannel and adjacent channel interference, Call blocking and delay at the cell-site,
Other mechanism for capacity increase, channel assignment strategies.
Unit 6. Multiple Access Technologies 5 Hrs.
Multiple access in a radio cell, Random access, Conflict-free multiple access
technologies, Spectral efficiencies.
Unit 7. Mobility Management in Wireless Networks 5 Hrs.
Introduction, Call admission control (CAC), Handoff management, Location
management for cellular networks, Location management for PCS networks, Traffic
calculation.
16. Unit 8. Wireless/Wireline Internetworking 5 Hrs.
Introduction, Mobile IP, Internet protocol (IP), Transmission control protocol (TCP),
Network performance, Wireless application protocol (WAP), Mobile AD HOC
networks.
Text / Reference books:
1. Wireless Communications and Networking, Jon W. Mark and Weihua Zhuang,
Prentice-Hall of India Private Limited, 2005.
2. Principles of Wireless Networks, Pahlavan, Prentice-Hall of India Private Limited,
2005.
17. Course Title: International Business Management
Course no: MGT-309 Full Marks:
90+10
Credit hours: 3 Pass Marks: 36+4
Nature of course: Theory (3 Hrs.)
Course Synopsis: Examination and analysis of international business in its historical,
theoretical, environmental, and functional dimensions. Topics
include the nature and scope of international business; the
institutional, socio-cultural, political, legal, ethical, and economic
environments; trade, foreign investment, and development;
transnational management, including global operations, strategic
planning, human resources, marketing, and finance; and
international business diplomacy and conflict resolution.
Goal: To develop the student’s understanding of international business and the
globalization of the economy. Students will learn fundamental concepts and
procedures, which will help them analyzing the international opportunities.
Unit 1. Introduction to International Business 4 Hrs.
Unit 2. Global business Environment 7 Hrs.
The cultural environment, The political and legal environment, The economic
environment
Unit 3. Global Trade and Investment 8 Hrs.
International trade theory, Government influence on trade, Regional economic
integration, Foreign direct investment, International business negotiations and
diplomacy
Unit 4.Financial Environment 4 Hrs.
Foreign exchange market, Determination of exchange rates
Unit 5. Choosing Where to Operate 4 Hrs.
Country evaluation and selection, Collaborative strategies, Control strategies
Unit 6. Management of Business Functions 8 Hrs.
Marketing, Export and import strategies, Global manufacturing, Global supply chain
management
Unit 7. International Finance, Accounting and Taxation 6 Hrs.
Basic concepts of multinational companies, Multinational finance function,
Multinational accounting and tax functions
18. Unit 8. International Human Resource Management 4 Hrs.
Textbooks: Daniels, John D., Radebaugh, Lee H. and Sullivan, Daniel P.,
International Business Environments and Operations, Pearson
Education (Singapore), India, 2004 ISBN: 81-297-0411-0
References: Czinkota, Ronkainen, and Moffett, International Business, 4th
Edition, Dryden 1996.
Hill, Charles, International Business: Competing in the Global
Marketplace, 3rd
Edition, Irwin.
Bennett, Roger, International Business, 2nd
Edition, Pearson
Education
Sharan, Vyuptakesh, International Business: Concept,
Environment and Strategy, Pearson Education
Homework
Assignments: Home works shall be given to the students with emphasis on small cases.
19. Course Title: International Marketing
Course no: MGT-310 Full Marks:
90+10
Credit hours: 3 Pass Marks: 36+4
Nature of course: Theory (3 Hrs.)
Course Synopsis: Introduction to global marketing environment and related issues.
Goal: This course helps developing understanding of important international marketing
terms and concepts and the students ability to think and communicate in creative,
innovative and constructive ways about the concepts within international
marketing.
Unit 1. Introduction to Global Marketing 4 Hrs.
Marketing concept, Concept and importance of global marketing, Forces affecting
global marketing
Unit 2. Global Economic Environment 4 Hrs.
Economic system – Market allocation, command allocation and mixed system,
Market development stages, Marketing and economic development, Trade patterns –
Merchandise and services trades, International trade alliances, World Trade
Organization (WTO), Regional trade group/agreements
Unit 3. Social–Cultural Environment and Global Marketing 4 Hrs.
Basic understanding of society and culture, Impact of social and cultural environment
in marketing industrial and consumer product
Unit 4 Political and Legal Environment of Global Marketing 4 Hrs.
Political environment – Nation state and sovereignty, political risk, taxes dilution of
equity control and expropriation, Legal environment – International law, intellectual
property, antitrust, bribery and corruption and dispute settlement
Unit 5. Global Information System and Marketing Research 3 Hrs.
Sources of market information, Marketing research
Unit 6. Segmentation, Targeting and Positioning 4 Hrs.
Market segmentation, Global targeting, Global product positioning
Unit 7. Global Marketing Strategy 3 Hrs.
Sourcing: Exporting and importing, Market expansion strategies
20. Unit 8. Product and Pricing Decisions 5 Hrs.
Basic concepts of products, Product positioning and product design considerations,
Basic concepts of pricing, Environmental influence on pricing decisions, Global
pricing objectives and strategies
Unit 9. Global Channel and Logistics 4 Hrs.
Basic concepts of global marketing channels, Basic concepts of physical distribution
and logistics
Unit 10. Global Advertising and E-Marketing 4 Hrs.
Global advertising and branding, Basic concepts of e. marketing
Unit 11. Management of Global Marketing 4 Hrs.
Textbooks: Keegan, Warren J., Global Marketing Management, Prentice-
Hall of India, New Delhi, 2003, ISBN: 81-203-2066-2
References: Cateora, Philip R. and Graham, John L., International
Marketing, Burr Ridge, Illinois: McGraw-Hill Publishers, 2005
ISBN: 0-07-283371-8
Homework
Assignments: Home works shall be given to the students with emphasis on small cases.
21. Course Title: Computer Hardware Design
Course no: CSC-312 Full Marks: 90+10
Credit hours: 3 Pass Marks: 36+4
Nature of course: Theory (3 Hrs.)
Course Synopsis: To introduce students to theoretical and practical concepts relevant
to the structure and design of modern digital computers. The course
covers computer architecture from gate-level logic through processor
design to multiprocessor and network issues.
Goal: This course will make the student able to design the hardware components.
Course Contents:
Unit 1. Introduction, Computer Abstractions and Technology 2 Hrs.
Hierarchical approach to understanding & designing a complex system, Software,
Hardware, Computer components, Processor: Control, Data path. Memory, Input &
output, Components of retail price in the computer industry, Overview of computer
hardware, IO, Computer processors; CISC, RISC, DSP, Hybrid, Measuring
performance. Execution time, Operations per second, Throughput, Real-time
computing and performance metrics
Unit 2. Digital Logic Design 6 Hrs.
Gates, truth tables, and logic equations, Combinational logic and basic components.
PLAs and ROMs, Memory elements. Finite state machines
Unit 3. Data Representation, Manipulation and Addressing 6 Hrs.
Signed and unsigned numbers, Addition and subtraction. Design of ALUs.
Multiplication. Floating-point representation, Addressing: An application of unsigned
integers: Byte-addressed memory, Byte ordering conventions, Big-endian, Little-
endian, Pointers: Address vs. contents, Signed representations of integers
Unit 4. Basic Processor Design 12 Hrs.
Design of the datapath of an ALU that executes the add, sub, and, or instructions,
Control signals for the ALU, State elements and clocking,
Block view of a single-clock-cycle processor datapath, Control of the single-clock-
cycle implementation, Control of the multiple-clock-cycle implementation,
Exceptions and interrupts, Karnaugh maps, Multiplexors, Adders, Decoders, Data
paths. Single-cycle, control. Multi-cycle control, Microprocessor design:
Microprogramming, Hardwired programming. Parallel processors, SIMD computers--
Single Instruction Stream, Multiple Data Streams, MIMD Computers--Multiple
Instruction Streams, Multiple Data, Streams; Programming MIMDs, MIMDs
connected by a single bus, MIMDs connected by a network, Future directions for
parallel processors, Programming for parallel processors in a higher-level language
22. Unit 5. Sequential Logic Circuits 5 Hrs.
Outputs and next state as vectors of Boolean functions of inputs and present state,
Latches: Set and reset latches, SR latch, CSR latch, JK latch, D latch, Master-slave D
flip-flop, Lightning introduction to finite state machines
Unit 6. Pipelining 5 Hrs.
A pipelined data path, Pipelined control, Visualization of pipelined data flow,
Pipeline diagrams, Gantt charts, Data hazards, Compiler elimination of data hazards,
Hardware control for data hazards: Reducing data hazards: Forwarding, Branch
hazards, Performance of pipelined systems, Programming for a pipelined processor in
a higher-level language
Unit 7. Memory Hierarchies 3 Hrs.
Hardware implementations of 1-bit memory, DRAM, SRAM, ROM, Hardware
implementations of multiple-bit memory, DRAM, SRAM, ROM, SRAM and DRAM
chip and system architectures, System bus architectures (processor to/from memory),
Hierarchical memory systems, The processor-memory speed gap, Interleaved
memory, Caches, Direct-mapped caches, Fully associative caches
Set-associative caches, Virtual memory, A common framework for memory
hierarchies
Unit 8. Multiprocessors 2 Hrs.
Single-bus networks, Cache consistency, Networks and clusters.
Unit 9. Introduction to Assembly Language 4 Hrs.
Instructions, The fetch-execute cycle, Format of an assembly-language program,
Comments, Directives, Data declarations in SPIM, Executable instructions, Survey of
differences between SAL (Simple Abstract Language), human-coded MIPS assembly
language, and true MIPS assembly language, Load-store architectures, Addressing
modes, MIPS addressing modes and the corresponding formats in assembly language
and object code, Implementation of I/O, Arrays, Usage of arithmetic and logical
instructions in SAL, Branch instructions in SAL and SPIM, Stacks, Support for
procedures in computer hardware, Alternatives to the MIPS approach
Textbooks: David A. Patterson and John L. Hennessy. "Computer Organization and
Design: The Hardware/Software Interface"
References: M. M. Mano "Digital logic Design”
Prerequisite(s):
Fundamentals of design methodology and descriptive tools; performance
and cost; overview of instruction set issues; processor implementation
techniques; memory hierarchy; input/output; parallel computer systems,
introduction to formal computer aided design tools and simulations.
23. Course Title: Introduction to Cryptography
Course no: CSC-313 Full Marks: 70+10+20
Credit hours: 3 Pass Marks: 28+4+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Goal: The course objective is to familiarize basic concepts of cryptography so as the
students can use their understanding for information security purpose.
Course Contents:
Unit 1. Introduction 4 Hrs.
Security, Attacks, Attack Types, Viruses, Worms, Trojan Horses, Classical Cryptography
Unit 2. Basics of Modern Cryptography 5 Hrs.
Plaintext, Ciphertext, keys, simple ciphers, public key cryptography, digital
signatures
Unit 3. Conventional Encryption / Secret Key Cryptography 10 Hrs.
Cryptography, Cryptanalysis, Cipher Structure, Encryption Algorithms, Data
Enncryption Standard (DES), International Data Encryption Algorithm (IDEA),
Advanced Encryption Standard (AES), Modes of Operation, Symmetric Block
Ciphers, Cipher Block Chaining (CBC), Multiple Encryption DES
Unit 4. Public Key Cryptography 6 Hrs.
Basic Number Theory, Factorization, Diffie-Hellman Key Exchange, Public Key
Cryptography Algorithms, RSA.
Unit 5. Digital Signatures 4 Hrs.
One-time signatures, Digital Signature Standard (DSS).
Unit 6. Hashing and Message Digests 6 Hrs.
Hashes, Motivation and applications. Cryptographically Secure Hashing, Secure Hash
Algorithm (SHA), Encryption with Message Digest (MD), MD5.
Unit 7. Authentication and Public Key Infrastructure (PKI) 5 Hrs.
Overview of Authentication Systems (Password, Address, Cryptographic), Security
Handshake Pitfalls, Authentication Standards, Kerberos, PKI Trust Models.
Unit 8. Network Security 5 Hrs.
IP Security, Web Security, Secure Socket Layer (SSL), Transport Layer Security
(TLS), Different versions of SNMPs, PGP.
24. Text / Reference books :
1. D. R. Stinson. Cryptography: Theory and Practice. CRC Press
2. William Stallings, Network Security Essentials-Applications & Standards, Pearson.
3. Charlie Kaufman, Radia Perlman, Mike Speciner, Nework Security Private
Communication in a Public World, Second Edition, 2004,Pearson.
4. Matt Bishop, Computer Security, Art and Science, Pearson
5. Bruce Schneier, Applied Cryptography, Pearson