Data structures and abstractions with java pdf download

Some of the dynamic behavior that can be generated by JavaScript is validating form, performing specific actions e. JavaScript is an open language and anyone can use it. It also shares m any of the features and structures of the Java programming language, though it is not really related to Java. It was developed independently. CSS is used to enable separation of document content from document presentation.

CSS helps us achieve layout design and control much easier. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language. These properties make JSON an ideal data-interchange language. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array.

In most languages, this is realized as an array, vector, list, or sequence. These are universal data structures. Virtually all modern programming languages support them in one form or another. It makes sense that a data format that is interchangeable with programming languages also be based on these structures. It makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler with an easy-to- use API that works across a multitude of browsers.

With a combination of versatility and extensibility, jQuery has changed the way that millions of people write JavaScript. PHP is free software i.

It is used for creating dynamic web pages that interact with the user and can include functionalities such as getting user input, manipulation of the input and storage of this data in a suitable DBMS. PHP is also easy to integrate with web pages. Initially MySQL was free and some versions of it are still free though if you desire to use MySQL for commercial purposes you will need to purchase a license. It is non-proprietary, easily extensible and platform independent.

Its downside is that it lacks a graphical user interface; therefore you need to know how the database works to make the most efficient use of it. The various development tools used in the project were also discussed in this chapter. The next chapter will focus on the design characteristics and aspects of the system to be developed. The EMS is modeled in terms of objects and classes and their interactions with each other.

Design of the User Interface is also discussed. The system shall be responsible for maintaining information about employees, thus their personal profile. The following activity diagram is used to model the leave application function. Figure 4. Scenario 1: Admin add new employee 1. The user logs in by providing correct username and password. If username and password are not found on the database access into the system is denied. If the credentials are identical to the ones found on the database, access is granted.

User enters the details of the new employee. The user input is written to the database. If username and password are not found on the database access into the system is denied 3. The user creates a project and assigns members. If the username and password are not found in the database access into the system is denied. User requests for leave form. User enters leave details. Details are written to the database.

A message confirming details have been submitted is displayed to the user. The website should have a similar look and feel on every page. This refers to the fact that there is need to separate content from layout, so that you can easily change your page design without editing every page on the site.

The layout of each page should have a good contrast between the text and background area. Monitor size should also be taken into consideration. Users should not have a hard time trying to navigate the site. Navigation links should be consistent and clearly labeled. When designing the site consider different browser environments. Extensive testing should be done on each page in all the major browsers and the design changed appropriately to cater for all.

The use of color, text, fonts and graphics should be carefully considered and used to ensure that the site is visually appealing to its visitors.

The performance of a website is mostly rated by its up -time and downtime. These terms refers to the amount of time it takes the site to respond to requests. Prereq: Graduate student standing. Orientation and configuration coordinate transformations, forward and inverse kinematics and Newton-Euler and Lagrange-Euler dynamic analysis. Planning of manipulator trajectories.

Force, position, and hybrid control of robot manipulators. Analytical techniques applied to select industrial robots. Recommended preparation: EMAE Digital images are introduced as two-dimensional sampled arrays of data. The course begins with one-to-one operations such as image addition and subtraction and image descriptors such as the histogram. Basic filters such as the gradient and Laplacian in the spatial domain are used to enhance images.

The 2-D Fourier transform is introduced and frequency domain operations such as high and low-pass filtering are developed. It is shown how filtering techniques can be used to remove noise and other image degradation.

The different methods of representing color images are described and fundamental concepts of color image transformations and color image processing are developed. One or more advanced topics such as wavelets, image compression, and pattern recognition will be covered as time permits. Programming assignments using software such as MATLAB will illustrate the application and implementation of digital image processing.

Artificial Intelligence: Probabilistic Graphical Models. This course is a graduate-level introduction to Artificial Intelligence AI , the discipline of designing intelligent systems, and focuses on probabilistic graphical models.

These models can be applied to a wide variety of settings from data analysis to machine learning to robotics. The models allow intelligent systems to represent uncertainties in an environment or problem space in a compact way and reason intelligently in a way that makes optimal use of available information and time.

The course covers directed and undirected probabilistic graphical models, latent variable models, associated exact and approximate inference algorithms, and learning in both discrete and continuous problem spaces.

Practical applications are covered throughout the course. Artificial Intelligence: Sequential Decision Making. This course will study the formulation and solution of decision making problems by automated agents. Topics covered include one-shot decision making decision trees and influence diagrams , Markov decision processes MDPs , automated classical and probabilistic planning, reinforcement learning RL , hierarchical planning and RL, partially observable MDPs, Bayesian RL, collaborative multi-agent systems.

This course gives students an overview of the stat of the art in natural language processing. We will discuss computational aspects of language modeling through probabilistic models, computational approaches to syntax parsing and semantic representations, discourse and dialog.

We will study the applications of these techniques to a variety of problems including information extraction, translation and summarization. At the end of the course a student should be able to i understand the various statistical models and algorithms for NLP ii modify them as needed or design novel approaches for specific NLP tasks and iii understand how to evaluate the performance of these models and compare them to alternatives.

This course introduces basic algorithmic techniques in robotic perception and planning. Course is divided into two parts. The first part introduces probabilistic modeling of robotic motion and sensing, Gaussian and nonparametric filters, and algorithms for mobile robot localization.

The second part introduces fundamental deterministic and randomized algorithms for motion planning. Students in this course may be expected to perform one or more of the following teaching related activities: grading homeworks, quizzes, and exams, having office hours for students, running recitation sessions, providing laboratory assistance. Survey of research issues in robotics. Primarily a project-based lab course in which students design real-time software executing on multi-processors to control an industrial robot.

This course will provide Ph. Students in this course may be expected to perform one or more of the following teaching related activities running recitation sessions, providing laboratory assistance, developing teaching or lecture materials presenting lectures.

Research course taken by Plan B M. Case School of Engineering. Educational Philosophy The CDS department is dedicated to developing high-quality graduates who will take positions of leadership as their careers advance. The program values for all of the degree programs in the department are: mastery of fundamentals creativity social awareness leadership skills professionalism Stressing excellence in these core values helps to ensure that our graduates are valued and contributing members of our global society and that they will carry on the tradition of industrial and academic leadership established by our alumni.

Undergraduate Programs. Admission Graduate students shall be admitted to the MS degree program upon recommendation of the faculty of the CS program. Students should have knowledge equivalent to that in the courses: CSDS Introduction to Data Structures CSDS Algorithms Any one course listed as an undergraduate Computer Science Breadth Requirement Students deficient in one or more of these areas admission with provision may be required to satisfy this requirement by taking the corresponding courses listed above.

Registration Course registration can be performed through the SIS system. Advising Each MS student will be assigned an academic advisor, who will assist the student in formulating an academic program. Requirements of different tracks The Course-Focused MS degree program requirements consist of the completion of 30 hours of approved coursework, satisfactory completion of a comprehensive exam, i. If a student wishes to switch from one track to another, the following requirements apply: Deadline.

Course-only or Project to Thesis. A course-only student may request to switch to the thesis track only if she 1 has already taken at least 9 credit hours of letter graded CSDS courses and 2 has a GPA of 3. Course-only to Project. A course-only student may request to switch to the thesis track only if she 1 has a TOEFL score of 90 or higher and 2 has the recommendation of a CDS advisor or co advisor. Thesis to Project, or Thesis or Project to Course-only. Such a transfer needs approval from the student's advisor and the department chair.

If a student fails to satisfy the transfer requirements, a petition may be submitted by a CDS advisor or co advisor to the department chair. In no case, petitions may be submitted by non-CDS faculty members or by students. Course Requirements For all three tracks, at least 18 hours of coursework must be at the level or above. Admission Requirements for admission include a strong record of scholarship in a completed bachelor's degree program in computer science and related areas, and fluency in written and spoken English.

PhD Requirements Each student must satisfy requirements in the following categories: Course Work Mathematics and Science Requirement Research Proposal Qualifying Examination Dissertation All programs of study must contain at least 36 hours of courses past the undergraduate degree. Academic Advisor and Research Advisor Upon arrival, each graduate student is assigned an academic advisor from the Computer Science CS program faculty, typically the CS graduate representative. The Academic Program lists all courses taken beyond the undergraduate degree and shows how these courses satisfy the following course requirements for the PhD: 1.

The following courses taken for credit will be acceptable: All , , level courses. The above courses must include the following: 1. Note: The courses for items 1.

These approved Computer Science courses are listed below. Format: The student will select a research area from the following list: Algorithms and Theory Artificial Intelligence Bioinformatics Computer Networks and Systems Databases and Data Mining Security and Privacy Software Engineering The exam committee will ask the student to write a report that adequately demonstrates the student's ability to perform research in their chosen area of research.

Scoring : Each of the three committee members will prepare a report rating the student's exam performance according to the following criteria: Fundamentals: Does the student have broad knowledge of fundamental concepts in computer science that will enable the student to understand and tackle the challenges in the specific research area?

Knowledge of Chosen Area: Does the student have sufficient technical depth and command of the key challenges and the state-of-the-art in the chosen area of research? Vision: Does the student demonstrate a solid understanding of the relevance of the problem in the context of scientific progress and societal needs? Does the student show creativity in innovating their chosen area of research? Can the student explain the concepts in an accessible and comprehensible manner and handle questions effectively?

Possible ratings are 2 Pass , 1 Retake , or 0 Fail. Advancement to Candidacy A student formally advances to candidacy after passing the Qualifying Examination and finding a faculty member who agrees to be the student's research advisor.

Dissertation Proposal The PhD student must write a formal thesis proposal and defend it in an oral presentation to his or her Dissertation Advisory Committee. Dissertation The student's dissertation must be original research in CS which represents a significant contribution to existing knowledge in the student's research area, a portion of which must be suitable for publication in reputable research journals or selective peer-reviewed conferences.

Additional Department Facilities Jennings Computer Center and Undergraduate Computer Lab Supported by an endowment from the Jennings Foundation, this lab provides our students with the educational resources necessary for their classwork and exploration of the art of computing. Nord Computer Laboratory This is a general-purpose computer facility that is open 24 hours a day, to all students.

Virtual Worlds Gaming and Simulation Laboratory The Virtual Worlds Gaming and Simulation Laboratory provides software and hardware to support education and research in computer gaming and simulation activities within the Computer and Data Sciences Department and the University at large.

Courses CSDS Fundamentals of Robotics. Linux Tools and Scripting. Software Craftsmanship. Independent Projects. Special Topics. Discrete Mathematics. Computer Architecture. Computer Networks I. Data Mining for Big Data. Compiler Design. Web Data Mining. Computer Security. Data Privacy. Computational Perception. Computer Graphics. Modern Robot Programming. Mobile Robotics.

App Development for iOS. Software Engineering. CSDS T. Graduate Teaching I. Analysis of Algorithms. Database Systems. Data Mining. High Performance Computing. Machine Learning. Causal Learning from Data. Smartphone Security. Applied Graph Theory. Computer Vision. Advanced Algorithms.

Computational Neuroscience. Robotics I. Digital Image Processing. Algorithmic Robotics. CSDS Colloquium. Seminars on current topics in Computer and Data Science. Graduate Teaching II.

Robotics II. Graduate Teaching III. Independent Study. Special Projects. Thesis M. Credit as arranged. Project M. Dissertation Ph. Print Options. Send Page to Printer. Download Page PDF. MATH STAT ECSE BIOL Hopsworks - A data-intensive platform for AI with the industry's first open-source feature store. The Hopsworks Feature Store provides both a feature warehouse for training and batch based on Apache Hive and a feature serving database, based on MySQL Cluster, for online applications.

Polyaxon - A platform for reproducible and scalable machine learning and deep learning. QuestDB A relational column-oriented database designed for real-time analytics on time series and event data. Supports FoLiA format. Kaldi is intended for use by speech recognition researchers. Sequence Analysis ToPS - This is an object-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet. Infections-clj - Rails-like inflection library for Clojure and ClojureScript.

General-Purpose Machine Learning tech. Infer - Inference and machine learning in Clojure. Data Analysis tech. PigPen - Map-Reduce for Clojure. Tensorflex - Tensorflow bindings for the Elixir programming language. Read the paper here. Reinforcement learning gold - A reinforcement learning library. IRIS - Cortical. Stanford Parser - A natural language parser is a program that works out the grammatical structure of sentences.

Tregex, Tsurgeon and Semgrex - Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes the name is short for "tree regular expressions". Stanford Tokens Regex - A tokenizer divides text into a sequence of tokens, which roughly correspond to "words". Stanford SPIED - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion.

MALLET - A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. OpenNLP - a machine learning based toolkit for the processing of natural language text. LingPipe - A tool kit for processing text using computational linguistics.

General-Purpose Machine Learning aerosolve - A machine learning library by Airbnb designed from the ground up to be human friendly. Datumbox - Machine Learning framework for rapid development of Machine Learning and Statistical applications. ELKI - Java toolkit for data mining. Encog - An advanced neural network and machine learning framework.

Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks. Mahout - Distributed machine learning. Meka - An open source implementation of methods for multi-label classification and evaluation extension to Weka.

Samoa SAMOA is a framework that includes distributed machine learning for data streams with an interface to plug-in different stream processing platforms. RankLib - RankLib is a library of learning to rank algorithms. RapidMiner - RapidMiner integration into Java code. Stanford Classifier - A classifier is a machine learning tool that will take data items and place them into one of k classes. SystemML - flexible, scalable machine learning ML language.

Weka - Weka is a collection of machine learning algorithms for data mining tasks. LBJava - Learning Based Java is a modeling language for the rapid development of software systems, offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application. Onyx - Distributed, masterless, high performance, fault tolerant data processing.

Written entirely in Clojure. Spark - Spark is a fast and general engine for large-scale data processing. Storm - Storm is a distributed realtime computation system. Impala - Real-time Query for Hadoop. DataMelt - Mathematics software for numeric computation, statistics, symbolic calculations, data analysis and data visualization.

Retext - Extensible system for analyzing and manipulating natural language. Gaussian Mixture Model - Unsupervised machine learning with multivariate Gaussian mixture model. Bayesian-Bandit - Bayesian bandit implementation for Node and the browser. It has advantage on large dataset and multi-threaded training.

Netron - Visualizer for machine learning models. Misc stdlib - A standard library for JavaScript and Node. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

Implemented in literate JavaScript with no dependencies, designed to work in all modern browsers including IE as well as in Node. Lyric - Linear Regression library. Pipcook - A JavaScript application framework for machine learning and its engineering. Demos and Scripts The Bot - Example of how the neural network learns to predict the angle between two points created with Synaptic.

Half Beer - Beer glass classifier created with Synaptic. It can even tell when one wears the mask incorrectly. PGM - A Julia framework for probabilistic graphical models. Regression - Algorithms for regression analysis e. Neural - A neural network in Julia. GLM - Generalized linear models in Julia. Gaussian Processes - Julia package for Gaussian processes. Clustering - Basic functions for clustering data: k-means, dp-means, etc. MultivariateStats - Methods for dimensionality reduction.

NMF - A Julia package for non-negative matrix factorization. ANN - Julia artificial neural networks. ManifoldLearning - A Julia package for manifold learning and nonlinear dimensionality reduction.

Flux - Relax! LightGraphs - Graph modeling and analysis. Julia Data - library for working with tabular data in Julia. Hypothesis Tests - Hypothesis tests for Julia. Gadfly - Crafty statistical graphics for Julia. Stats - Statistical tests for Julia. RDataSets - Julia package for loading many of the data sets available in R. DataFrames - library for working with tabular data in Julia.

Current analytical methods for examining cells and tissues, and molecular components important in understanding drug and protein biodistribution and metabolism will be discussed. Discussion topics will include the chemical and physical properties of small molecules, proteins, nucleic acids and lipids and their impact on cellular and subcellular structures and ultimately of either adverse or therapeutic benefit.

Same as PHCH Prerequisite: Graduate standing or consent of instructor. Convective Heat and Momentum Transfer. The formulation and solution of steady- and unsteady-state convective heat and momentum transfer problems.

Applications of boundary layer equations to free and forced convection with study of similarity and integral methods of solution for laminar and turbulent flow; development of analogies; transport properties from kinetic theory of gases viewpoint; introduction to numerical methods.

A concurrent course in partial differential equations is helpful. Advanced Transport Phenomena II. The formulation and solution of steady- and unsteady-state mass transfer problems including those complicated by momentum and heat transfer. The mathematical approach predominates and the methods available for determining suitable mass transfer coefficients are covered. Basic rheology including classification of classical bodies based on their stress and strain tensors, rheological equation of state, material functions, generalized Newtonian and general linear viscoelastic fluids, mechanical models such as those of Jeffreys and Maxwell.

An introduction to the rapidly growing and continuously evolving field of tissue engineering. Tissue engineering applies principles and methods of engineering and life sciences toward understanding and development of biological substitutes to restore, maintain and improve tissues functions. In this course, students study the basic science, engineering and medicine required for tissue engineering, learn state-of-the-art technology and practice, and create a literature-based proposal for a tissue engineered medical product.

Prerequisite: Senior or graduate standing in engineering; or consent of instructor. Introduction to Electrochemical Engineering. Basic principles of electrochemical engineering as they are applied to energy conversion and storage devices, industrial electrolytic processes and corrosion.

Areas covered range from electrochemical thermodynamics, ionic phase equilibria, electro-kinetics and ionic mass transport to mathematical modeling of electrochemical systems.

Knowledge of the theoretical basis for these techniques and processes will be demonstrated within a class project. Topics covered include crystal growth, oxidation, solid-state diffusion, ion implantation, photolithography, chemical vapor deposition, eqitaxial growth, metallization, and plasma etching of thin films. A term paper on an approved topic of fabrication referencing current peer reviewed literature is required.

Additional assignments commensurate with the graduate-level course designation are required for this section.

Prerequisite: Graduate-level standing in Engineering, or consent of instructor. Additional assignments on current research directions in the field commensurate with the graduate-level course designation are required for this section. Prerequisite: Graduate-level standing in engineering, or consent of instructor.

Electrochemical basis of corrosion. Types of corrosion and corrosive atmospheres. Corrosion control measures and industrial problems. Advanced Reservoir Engineering. Physical principles of petroleum production; gas drive performance; partial water drive performance; pressure maintenance through gas and water injection. Topics covered include gradient methods, penalty functions, linear programming, nonlinear and integer programming, stochastic optimization approaches, and treatment of constrained problems.

Homework problems involving theoretical concepts and a theoretically-based semester project are required. Introduction to Flow in Porous Media. Generalized Darcy's law, vector equations, solutions of partial differential equations with various boundary conditions as applied to the flow of fluids in porous media. Enhanced Petroleum Recovery. A study of improved oil recovery processes such as miscible displacement, microemulsion displacement, and thermal methods. A study of phase behavior and equilibrium from a molecular perspective.

Focus will be on vapor-liquid, liquid-liquid and solid-liquid equilibrium with advanced topics in compressed and supercritical fluids, petroleum applications, ionic solutions and others. For the remainder of the year, the seminar will involve presentation of current research and other topics of interest to chemical and petroleum engineers. These presentations will be made by invited guests, faculty, and advanced graduate students. Student attendance is required. One hour per week in which the staff introduces entering graduate students to research.

Topics include discussion of research methods, methods of effectively tapping library resources, preparation of literature surveys, and presentation of results. Faculty members of the department will make presentations of their current research interests. Offered fall only. A forum in which graduate and postdoctoral students, and faculty present the results of CEBC research and literature surveys that support the mission of CEBC.

Petroleum Management Seminar. Structure, operation, and problems of the petroleum industry from a management viewpoint. Presentations will be made by faculty, advanced students, and invited guests.

Graduate Problems in Chemical and Petroleum Engineering. Advanced laboratory problems, special research problems, or library reading problems. Three hours maximum acceptable for master's degree. Preparation for the Ph. Comprehensive Examination. Preparation of a research proposal in an area assigned by the student's advisory committee.

The grade received on the Ph. Industrial Development of Catalytic Processes. Students adopt an interdisciplinary team approach to developing strategies for the design and optimization of catalytic processes.

Examples of case studies will be derived from industry or from research testbeds. Students collaborate in multiscale process development involving catalyst and reactor design, reaction system design, modeling and optimization, economic analysis and environmental assessment needed for the development of a catalytic process at either the pilot or production scale.

Graduate students engage in an industrial research internship experience with collaborators in industry. Future university instructors learn how to critically examine course content and teaching strategies, and prepare courses that will address the learning needs of the diverse student populations of the future. Students participate in weekly in-class workshops and symposia, as well as a teaching practicum experience during this course.

Advanced study in process modeling, simulation or control on topics which may vary from year to year. Advanced study in various branches of chemical and petroleum engineering on topics which may vary from year to year. Heat and Mass Transport in Porous Media. A study of industrial problems involving heat and mass transport in porous media such as packed columns, catalyst beds, chemical reactors, and petroleum reservoirs.

Mechanisms of interphase and intraphase transport, diffusion, and dispersion. Included are methods of solution of the describing differential equations. Heat Transport with Phase Change.

A fundamental treatment of heat transfer occurring during boiling and condensation. Included are nucleate and film boiling, film and dropwise condensation, and two-phase flow. Industrial Separation Processes. Determination and treatment of vapor-liquid separations, including methods for obtaining and treating equilibrium data, procedures for calculating multi-component separations by distillation, absorption, extraction, and adsorption.

Industrial applications of fluid mechanics including compressible flow, flow of non-Newtonian fluids, flow of drag reducing systems all to be considered in laminar and turbulent flow regimes, and within conduits, and porous media.

Advanced study in various branches of transport phenomena on topics which may vary from year to year. Data Analysis in Engineering and Natural Sciences. Statistical inference and data analysis, emphasizing interpretation of observations from areas of engineering and natural sciences where controlled experimentation is not possible.

The basics of elementary statistics and matrix algebra are covered, followed by topics in time, series analysis, map analysis, including automatic contouring, and multivariate procedures such as principal components, discrimination and factor analysis. A suite of computer programs is provided. Students are encouraged to use data from their own graduate research in class projects. ARCE Introduction to Architectural Engineering. An introduction to the study of and careers in architectural engineering.

Topics include problem solving and study skills, the building design and construction process, design documents, and professional practice issues such as licensing requirements and ethics.

Computer-Assisted Building Design. Introduction to computer-aided design CAD tools. Includes architectural and structural design; mechanical, electrical, and plumbing MEP design; and modeling using the Family Editor in Revit.

Electric Circuits and Machines. Introduction to DC and AC electrical circuit analysis techniques, AC power calculations, transformers, three-phase systems, magnetic circuits, and DC and AC machines with a focus on applications. Not open to electrical or computer engineering majors.

Prerequisite: A course in differential equations and eight hours of physics. An introduction to the structural, thermal, electrical, and optical properties of building materials. Manufacturing, testing, integration, and specification of materials with emphasis on commercial, institutional, and industrial buildings. Building Materials Science, Honors. Manufacturing, testing, integration, and specification of materials with emphasis on commercial, institutional, and industrial buildings with added honors-enhancement activities.

The activities include one or more of the following: extra meetings outside the classroom, written work, projects, and presentations. Special problems in architectural engineering. The study of a particular problem involving individual research and report. Prerequisite: Students must submit, in writing, a proposal including a statement of the problem the student wishes to pursue, the methodology the student plans to use in the program, and objectives of the special problems.

The student must also have a signed agreement with the faculty member proposed as instructor for the course. Consent of the instructor. An introduction to the physics of sound. Objective and subjective evaluation and control of sound as applied to architectural spaces. Room shaping, mechanical and electrical system noise and vibration control, and electro-acoustic sound reinforcement.

A study of electro-acoustic sound reinforcement and reproduction systems for buildings. Problems in Architectural Acoustics.

Capstone architectural engineering design course that includes the analysis, design, and integration of a building's acoustical system. Building codes, standards, performance, and sustainability are addressed. Corequisite: CMGT This course introduces the design of commercial and industrial power systems. Emphasis is placed on the proper selection, specification, and installation of materials and equipment that comprise commercial and industrial power systems. This course covers the application of materials and equipment in accordance with industry standards, independent laboratory testing, and the National Electrical Code.

Power Systems Engineering II. A continuation of ARCE that integrates system components into functional, safe, and reliable power distribution systems for commercial, industrial, and institutional CII facilities. Service entrance design, distribution system layout and reliability, emergency and standby power system design, medium-voltage distribution systems, symmetrical fault analysis, and special equipment and occupancies. This course introduces techniques and methods used to analyze and predict the performance of commercial and industrial power systems and equipment under balanced and unbalanced fault conditions.

Emphasis is placed on the selection, application, and coordination of protective devices to detect and clear power system faults in a safe and reliable manner. Electric Machines and Drives. Introduction to electric machine theory, operation, and control. Electric machines covered include DC generators and motors, AC synchronous generators and motors, AC induction generators and motors, as well as fractional horsepower and special purpose motors.

Motor starting and controls for both DC and AC machines are also covered including an introduction to power electronics and variable frequency drives VFD. Electric Energy Production and Storage. An introduction to the design of utility scale and small scale distributed generation electric energy production and storage systems. This course addresses the technical, operational, economic, and environmental characteristics associated with both traditional and nontraditional electric energy production systems along with associated grid integration, energy delivery, and regulatory issues.

Traditional energy production systems covered include fossil fuel, hydroelectric, and nuclear power plants. Non-traditional energy productions systems covered include fuel cells, photovoltaics PV , concentrated solar power CSP , wind, geothermal, and other emerging technologies. Introduction to the analysis of commercial, industrial, and utility power systems.

Emphasis is placed on modeling system components which include transmission and distribution lines, transformers, induction machines, and synchronous machines and the development of a power system model for analysis from these components. System modeling will be applied to short-circuit studies and used to analyze symmetrical faults, to develop sequence networks using symmetrical components, and analyze unsymmetrical faults.

The impact of alternative energy sources, energy storage, DC transmission and interties, and other emerging technologies on power system operation and reliability will be addressed throughout the course.

Students are introduced to lighting fundamentals, measurement, and technology and to their application in the analysis and design of architectural lighting systems. Prerequisite: PHSX or consent of instructor. The fundamentals of moist air processes, air and moisture exchange, and building heat transfer. Determination of heating and cooling loads under steady-state and transient conditions.

Prerequisite: ME Analysis and design of heating, ventilating, air-conditioning, and refrigeration equipment and systems. The analysis and design of hydronic systems for buildings including piping, plumbing, pumping, and the water-side of heating, ventilating, and air-conditioning HVAC. Energy usage in commercial buildings and industry, energy auditing methodology, utility analysis, management measures, and economic evaluation are covered.

Includes fieldwork. An introduction to human response, fire science, combustion calculations, compartment fires, piping and sprinkler design, and smoke management. Analytical methods, experimental data, codes, case-studies, and videos are presented in this engineering design course.

A quantitative and qualitative study of active, passive, wind, and photovoltaic energy conversion systems for buildings. Solar radiation and system performance prediction. Building Thermal Science, Honors. Determination of heating and cooling loads under steady-state and transient conditions with added honors-enhancement activities. The discussion section and its assignments are required.

Not open for those with credit for ARCE An introduction to the physics and measurement of sound, wave phenomena, acoustics, and methods of noise and excessive vibration control for various applications.

The study of a particular problem in architectural engineering involving individual research and presentation. Prerequisite: Student must submit, in writing, a proposal including a statement of the problem the student wishes to pursue, the methodology the student plans to use in the program, and objectives of the special problems. Consent of instructor. Research a particular architectural engineering problem.

Research will involve defining the problem, developing a research methodology, applying the research methodology and gathering data, analyzing and interpreting the data, and presenting the results of the research. The student must have a faculty sponsor and submit a proposal in writing stating the objective of the research, the planned research method that will be used, and the method of reporting the results. Prerequisite: Participation in the University Honors Program, consent of instructor, and approval of the chair are required.

Comprehensive Design Project. Capstone architectural engineering design course that includes the analysis, design, and integration of a building's structural, mechanical, electrical, and lighting systems. Building codes, standards, performance, and sustainability are addressed, and BIM software utilized. Directed Readings in Architectural Engineering. Individual study of special topics and problems. May be repeated for credit. Prerequisite: Student must submit, in writing, a proposal including a statement of the problem the student wishes to pursue and a bibliography of the articles and books required to complete the project.

This course will cover daylighting design concepts, solar position, daylight availability, sky luminance distribution models, daylight delivery methods, integration of daylighting and electric lighting controls, physical modeling, and computer analysis techniques. Advanced analysis, design, and modeling of luminous environments. It covers impact of lighting on human perception and interaction with space, human factors in lighting, camera-aided light measurement technologies, advanced computer-aided lighting simulations, effective and efficient integration of natural and artificial lighting, modeling and analysis of light sources and spaces, simulation of lighting systems, and design of lighting control systems.

Lighting Measurement and Design. This course will cover conventional lighting and solid-state lighting measurement, daylighting measurement, camera-aided lighting measurement technologies and applications, and design and development of custom luminaries in an LED workshop and innovative daylighting devices.

Automatic Controls for Building Mechanical Systems. An introduction to controls for building mechanical systems. Discussions of the theory, design, and equipment used for control systems. The benefits of pneumatic, electrical, and electronic DDC controls will be examined. Advanced Thermal Analysis of Buildings. Manual and computational methods for determining steady-state and transient thermal loads in buildings. Advanced analysis of energy consumption given choices in building materials and mechanical systems.

Directed study and reporting of a specialized topic of interest to the architectural engineering profession. Directed research and reporting of a specialized topic of interest to the architectural engineering profession.

CE Introduction to Civil Engineering. A discussion of engineering logic through examination of current concepts in engineering education, practice and professional development. Not open to juniors and seniors. The principles of statics, with particular attention to engineering applications.

This course introduces engineering applications of surveying and geographic information systems GIS using surveying instruments and ArcGIS. The focus of this course is on practical application of geomatics to civil engineering problems.

Two lectures periods and one lab period per week. The principles of kinematics and kinetics, with particular attention to engineering applications. A combination of statics and dynamics covered in CE and CE This course must be taken as a five-hour unit. Three one-hour lectures and one two-hour laboratory. Principles of stress and deformation in structures and machines.

Principles of stress and strain in deformable bodies under load. This course covers the fundamentals of fluid mechanics and includes the topics fluid properties, hydrostatics, applications of conservation of mass, energy and momentum equations, pipe flow, dimensional analysis and open channel flow.

This is an experimental course that consists of several laboratory experiments intended to illustrate the concepts presented in CE , Fluid Mechanics. Corequisite: CE Structural Engineering Materials.

Study of the engineering properties of structural materials and their control with emphasis on timber, concrete, and steel.

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