The Acoustics of Speech and Hearing is an H-Level graduate course that reviews the physical processes involved in the production, propagation and reception of human speech. Particular attention is paid to how the acoustics and mechanics of the speech and auditory system define what sounds we are capable of producing and what sounds we can sense. Areas of discussion include:

the acoustic cues used in determining the direction of a sound source,

the acoustic and mechanical mechanisms involved in speech production and

the acoustic and mechanical mechanism used to transduce and analyze sounds in the ear.

Short Description:
This text is an OER remix of the following resource: Clark, J. S., Porath, S., Thiele, J., & Jobe, M. (2020). Action research. New Prairie Press.

Word Count: 30262

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The 16 lectures in this course cover the topics of adaptive antennas and phased arrays. Both theory and experiments are covered in the lectures. Part one (lectures 1 to 7) covers adaptive antennas. Part two (lectures 8 to 16) covers phased arrays. Parts one and two can be studied independently (in either order). The intended audience for this course is primarily practicing engineers and students in electrical engineering. This course is presented by Dr. Alan J. Fenn, senior staff member at MIT Lincoln Laboratory.
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Adjustment theory can be regarded as the part of mathematical geodesy that deals with the optimal combination of redundant measurements together with the estimation of unknown parameters. It is essential for a geodesist, its meaning comparable to what mechanics means to a civil engineer or a mechanical engineer. Historically, the first methods of combining redundant measurements originate from the study of three problems in geodesy and astronomy, namely to determine the size and shape of the Earth, to explain the long-term inequality in the motions of Jupiter and Saturn, and to find a mathematical representation of the motions of the Moon. Nowadays, the methods of adjustment are used for a much greater variety of geodetic applications, ranging from, for instance, surveying and navigation to remote sensing and global positioning.

The two main reasons for performing redundant measurements are the wish to increase the accuracy of the results computed and the requirement to be able to check for errors. Due to the intrinsic uncertainty in measurements, measurement redundancy generally leads to an inconsistent system of equations. Without additional criteria, such a system of equations is not uniquely solvable. In this introductory course on adjustment theory, methods are developed and presented for solving inconsistent systems of equations. The leading principle is that of least-squares adjustment together with its statistical properties.

The inconsistent systems of equations can come in many different guises. They could be given in parametric form, in implicit form, or as a combination of these two forms. In each case the same principle of least-squares applies. The algorithmic realizations of the solution will differ however. Depending on the application at hand, one could also wish to choose between obtaining the solution in one single step or in a step-wise manner. This leads to the need of formulating the system of equations in partitioned form. Different partitions exist, measurement partitioning, parameter partitioning, or a partitioning of both measurements and parameters. The choice of partitioning also affects the algorithmic realization of the solution. In this introductory text the methodology of adjustment is emphasized, although various samples are given to illustrate the theory. The methods discussed form the basis for solving different adjustment problems in geodesy.

This book provides a summary of adult immunizations with guidelines, indications, and contraindications. It was developed for Internal Medicine residents at The Ohio State University Wexner Medicine Center. The book is meant to be a simple read with high yield points to serve as a reference in the primary care clinic setting.

This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science.

This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.

General James Clapper, former United States Director of National Intelligence and the National Geospatial-Intelligence Agency (NGA), once said \everything happens somewhere.\" He stressed that there are aspects of time and place to every intelligence problem. In this course, you will examine how time and place work with general intelligence techniques to create geospatial intelligence. You will learn and apply critical thinking skills, structured analytical techniques, and other intelligence methods in a geospatial context. You'll also learn how to reduce personal and organizational bias by conducting an Analysis of Competing Hypotheses, by R. Heuer, a 45-year veteran of the CIA. As a result, you will be better prepared for the world of geospatial intelligence analysis."

Following a brief classroom discussion of relevant principles, each student in this course completes the paper design of several advanced circuits such as multiplexers, sample-and-holds, gain-controlled amplifiers, analog multipliers, digital-to-analog or analog-to-digital converters, and power amplifiers. One of each student’s designs is presented to the class, and one may be built and evaluated. Associated laboratory assignments emphasize the use of modern analog building blocks. This course is worth 12 Engineering Design Points.

This graduate-level course focuses on current research topics in computational complexity theory. Topics include: Nondeterministic, alternating, probabilistic, and parallel computation models; Boolean circuits; Complexity classes and complete sets; The polynomial-time hierarchy; Interactive proof systems; Relativization; Definitions of randomness; Pseudo-randomness and derandomizations;Interactive proof systems and probabilistically checkable proofs.

Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structure.
Acknowledgments
Thanks to videographers Martin Demaine and Justin Zhang.

This course will focus on advanced techniques and concepts in database management. Emphasis will be on Structured Query Language (SQL) and current industry best practices for utilizing data and databases. This course will be using hands-on exercises and real-world case studies for topics such as database optimization, database scalability, performance tuning, and concurrency control.
Learning Objectives

Choose appropriate data models for projects.
Implement data design in a relational database.
Use query language (SQL) to create, access, update, and delete.
Articulate how to collect and handle data
Visualize and present data in ways that are easy to communicate with others
Write efficient SQL statements
Practice and implement database modeling and design
Utilize and implement industry best practices for security and data standards
Program a complex project using industry standard techniques.
Describe the issues related to database performance.
Show the advantages and disadvantages of various database types.

This course introduces the basic components of an airframe structure and discusses their use and limitations. The realities of composite design such as the effect of material scatter, environmental knockdowns, and damage knockdowns are discussed and guidelines accounting for these effects and leading to robust designs are presented.

The resulting design constraints and predictive tools are applied to real-life design problems in composite structures. A brief revision of lamination theory and failure criteria leads into the development of analytical solutions for typical failure modes for monolithic skins (layup strength, buckling under combined loads and for a variety of boundary conditions) and stiffeners (strength, column buckling under a variety of loads and boundary conditions, local buckling or crippling for one-edge and no-edge-free conditions). These are then combined into stiffened composite structures where additional failure modes such as skin-stiffener separation are considered. Analogous treatment of sandwich skins examines buckling, wrinkling, crimping, intra-cellular buckling failure modes. Once the basic analysis and design techniques have been presented, typical designs (e.g. flange layup, stiffness, taper requirements) are presented and a series of design guidelines (stiffness mismatch minimization, symmetric and balanced layups, 10% rule, etc.) addressing layup and geometry are discussed. On the metal side, the corresponding design practices and analysis methods are presented for the more important failure modes (buckling, crippling) and comparisons to composite designs are made. A design problem is given in the end as an application of the material in this Part of the course.

An open-source textbook covering vector calculus, ordinary and partial differential equations, and Fourier series. The textbook is used in a first-year graduate level course in the Department of Mechanical Engineering at the Colorado School of Mines. It undergoes extensive revisions annually, but is relatively complete.

Designed to familiarize students with theories and analytical tools useful for studying research literature, this course is a survey of fluid mechanical problems in the water environment. Because of the inherent nonlinearities in the governing equations, we shall emphasize the art of making analytical approximations not only for facilitating calculations but also for gaining deeper physical insight. The importance of scales will be discussed throughout the course in lectures and homeworks. Mathematical techniques beyond the usual preparation of first-year graduate students will be introduced as a part of the course. Topics vary from year to year.

This course is a survey of principal concepts and methods of fluid dynamics. Topics include mass conservation, momentum, and energy equations for continua; Navier-Stokes equation for viscous flows; similarity and dimensional analysis; lubrication theory; boundary layers and separation; circulation and vorticity theorems; potential flow; introduction to turbulence; lift and drag; surface tension and surface tension driven flows.

1.364 examines site characterization and geotechnical aspects of the design and construction of foundation systems. Topics include: site investigation (with emphasis on in situ testing), shallow (footings and raftings) and deep (piles and caissons) foundations, excavation support systems, groundwater control, slope stability, soil improvement (compaction, soil reinforcement, etc.), and construction monitoring. This course is a core requirement for the Geotechnical Master of Engineering program at MIT.

The purpose of this tutorial (along with the “Advanced Keyword Searching, Part I” tutorial) is to help the student master comprehensive, keyword searching. Performing a truly comprehensive, keyword search is more difficult than performing a similarly thorough, subject heading search. We hope that this tutorial will make the steps involved in this complex process easier to understand and master.

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Word Count: 8074

Included H5P activities: 8

ISBN: 978-1-55195-451-6

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This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.