This course provides a deep understanding of engineering systems at a level intended for research on complex engineering systems. It provides a review and extension of what is known about system architecture and complexity from a theoretical point of view while examining the origins of and recent developments in the field. The class considers how and where the theory has been applied, and uses key analytical methods proposed. Students examine the level of observational (qualitative and quantitative) understanding necessary for successful use of the theoretical framework for a specific engineering system. Case studies apply the theory and principles to engineering systems.
Search Results (24)
This course provides an introduction to the technology and policy context of public communications networks, through critical discussion of current issues in communications policy and their historical roots. The course focuses on underlying rationales and models for government involvement and the complex dynamics introduced by co-evolving technologies, industry structure, and public policy objectives. Cases drawn from cellular, fixed-line, and Internet applications include evolution of spectrum policy and current proposals for reform; the migration to broadband and implications for universal service policies; and property rights associated with digital content. The course lays a foundation for thesis research in this domain.
Students learn about complex networks and how to represent them using graphs. They also learn that graph theory is a useful mathematical tool for studying complex networks in diverse applications of science and engineering, such as neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Topics covered include set theory, defining a graph, as well as defining the degree of a node and the degree distribution of a graph.
Topics on the engineering and analysis of network protocols and architecture, including: architectural principles for designing heterogeneous networks; congestion control; unicast and multicast routing; wireless and mobile networking; network quality of service; router design; network security; streaming and multicast applications; naming; content distribution; and peer-to-peer networking. Readings from original research papers, industry white papers, and Internet RFCs. Semester-long project and paper.
Using a website simulation tool, students build on their understanding of random processes on networks to interact with the graph of a social network of individuals and simulate the spread of a disease. They decide which two individuals on the network are the best to vaccinate in an attempt to minimize the number of people infected and "curb the epidemic." Since the results are random, they run multiple simulations and compute the average number of infected individuals before analyzing the results and assessing the effectiveness of their vaccination strategies.
In the year 1970’s when relational database came into picture, data schema to be worked upon were reasonably elemental and simple wherein the data items were to be arranged as a set of formally described tables with rows and columns. But with the need to store volumes and variety of data (unstructured) in recent years, non-relational database technologies (document-oriented, graph based, column based, key-value and hybrid) have emerged to address the requirement that allow data to be grouped together more naturally and logically. One of the most popular ways of storing data is a document-oriented database, basically employed for storing, managing and retrieval of semi-structured data where each record and its associated data is considered of as a “document”. A document-oriented database is also termed as a document store or simple document, is one of the kind of NoSQL database.
This three credit course offered at Macomb Community College emphasizes the architecture ofautomotive electronics with attention to electric vehicles and is a required course for MCC's Electric Vehicle DevelopmentTechnology Certificate. Topics included are review of electrical and electronics theory, vehicle network theory, vehicle controllers, automotive bus systems, On-Board Diagnostics(OBD) systems,controller area nework (CAN), sensors, actuators, and selected topics in power control. Using simulators, students will gain a broad knowledge of the networks used in an automotive system. Included educational materials for this course are classroom exercises, manuals, PowerPoint presentations, system specificguides, and syllabus. Homework assignments and exams are not included. The course outline is as follows: electrical and electronic systems in a vehicle, networking principles, vehicle network, bus systems, electronics systems architecture, electronic components in vehicles, control unit, automotive sensors, sensor measuring principles, sensor types, actuators,vehicle electrical systems, vehicle controllers, vehicle On-Board Diagnostics (OBD), andhybrid drives.
Students use graph theory to create social graphs for their own social networks and apply what learn to create a graph representing the social dynamics found in a dramatic text. Students then derive meaning based on what they know about the text from the graphs they created. Students learn graph theory vocabulary, as well as engineering applications of graph theory.
Students analyze their social networks using graph theory. They gather data on their own social relationships, either from Facebook interactions or the interactions they have throughout the course of a day, recording it in Microsoft Excel and using Cytoscape (a free, downloadable application) to generate social network graphs that visually illustrate the key persons (nodes) and connections between them (edges). The nodes in the Cytoscape graphs are color-coded and sized according to the importance of the node (in this activity, nodes are people in students' social networks). After the analysis, the graphs are further examined to see what can be learned from the visual representation. Students gain practice with graph theory vocabulary, including node, edge, betweeness centrality and degree on interaction, and learn about a range of engineering applications of graph theory.
Students simulate disease transmission by collecting data based on their proximity to other students. One option for measuring proximity is by having Bluetooth devices "discover" each other. After data is collected, students apply graph theory to analyze it, and summarize their data and findings in lab report format. Students learn real-world engineering applications of graph theory and see how numerous instances of real-world relationships can be more thoroughly understood by applying graph theory. Also, by applying graph theory the students are able to come up with possible solutions to limit the spread of disease. The activity is intended to be part of a computer science curriculum and knowledge of the Java programming language is required. To complete the activity, a computer with Java installed and appropriate editing software is needed.
This website is a comprehensive look at digital citizenship for K-12 students, parents and teachers. It covers a wide variety of topics including: online security, online relationships and cyberbullying, digital footprint, digital citizenship, the use of copyrighted information and much more.
Students learn about complex networks and how to use graphs to represent them. They also learn that graph theory is a useful part of mathematics for studying complex networks in diverse applications of science and engineering, including neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Students are also introduced to random processes on networks. An illustrative example shows how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students, and demonstrates how scientists and engineers use mathematics and computers to model and simulate random processes on complex networks for the purposes of learning more about our world and creating solutions to improve our health, happiness and safety.
Graph theory is a visual way to represent relationships between objects. One of the simplest uses of graph theory is a family tree that shows how different people are related. Another application is social networks like Facebook, where a network of "friends" and their "friends" can be represented using graphs. Students learn and apply concepts and methods of graph theory to analyze data for different relationships such as friendships and physical proximity. They are asked about relationships between people and how those relationships can be illustrated. As part of the lesson, students are challenged to find the social graph of their friends. This prepares students for the associated activity during which they simulate and analyze the spread of disease using graph theory by assuming close proximity to an infected individual causes the disease to spread.
Students learn to solve minimum cost network flow problems.
Building on their understanding of graphs, students are introduced to random processes on networks. They walk through an illustrative example to see how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students. This demonstrates how scientists and engineers use mathematics to model and simulate random processes on complex networks. Topics covered include random processes and modeling disease spread, specifically the SIR (susceptible, infectious, resistant) model.
With the expansion of huge and complex real time data that is wandering across the internet today, the dimensions of data transmitted are escalating exponentially with each passing years. This makes working with standard database systems or on personal computers difficult because of its inability to handle outsized, unstructured and complicated data. Various institutes stores and uses massive amounts of data which are further utilized for generating reports to guarantee stability regarding the services they proposes. However, the challenge is how to analyze, capture, share, store, transfer, visualize, query, update and finally manipulate an impressive volume of data that has to be delivered through the internet to reach its destination intact maintaining its information privacy. Almost all the applications developed using any programming languages requires some external component to store and access data. The components for the same could be a local network, a cloud file or even a database. While sources like the network and cloud file systems store the unstructured data, the structured data is usually stored in a typical Relational Database Management System or RDBMS. The RDBMS operates with relational data model using schema for storing data into tables and is usually queried with SQL (Structured Query Language) for data operations. Usually it’s a time consuming process to define, structure, distribute and access data from RDBMS through SQL and hence, an alternative was developed for this called the NoSQL ("Non SQL", "Non relational" or "Not only SQL") database. This edited book chapter provides NoSQL databases hands on and attention has been paid to various types of NoSQL databases focusing on the details such as installation, creation, modification and various updation of one database belonging to each type.
In this activity, learners use cooperation and logical thinking to find solutions to network problems on the playground. Learners act both as computer routers, figuring out with each other how to effectively get data to the place it's being sent, and as the actual data, because the learners travel various edges of a network to get to their destination or "home" point. Learners use geometry skills to determine the most efficient routes in the network.
To get a better understanding of complex networks, students create their own, real social network example by interacting with their peers in the classroom and documenting the interactions. They represent the interaction data as a graph, calculate two mathematical quantities associated with the graph—the degree of each node and the degree distribution of the graph—and analyze how these quantities can be used to infer properties of the social network at hand.