Special Topics Course Descriptions - page 2

Operations

OIE 598E. ENGINEERING ECONOMICS

The intention of this course is to aid all engineering students in understanding economics and business constraints on engineering decision making. Topics may include but will not be limited to: evaluation of alternative; the six time-value-of-money factors; present worth, annual cash flow and rate-of-return analysis; incremental analysis; depreciation and income taxes; replacement analysis; inflation; handling probabilistic events; public economy; break-even and minimum cost points; and foreign exchange.

OIE 598. ST: SUSTAINABLE SUPPLY CHAIN & OPERATIONS MANAGEMENT

This course is intended to provide students with understanding the intra- and inter-organizational implications of environmental practices and policies.  The role of organizational operational and supply chain management functions, activities, tools and methods and their relationship to the natural environment will be introduced and discussed. At the end of the course a successful student should be able to: grasp the scope of general operations and supply chain management and environmental sustainability as they relate to the firm, be able to relate to the manners in which management may respond and collaborate/cooperate with suppliers, customers, and various other stakeholders influencing and influenced by operational and supply chain activities from practical and theoretical case studies and able to evaluate various factors and understand trade-offs in management decisions as they pertain to environmental operations and supply chain management.

Robotics Engineering

RBE 521 ST. ADVANCED ROBOTICS: PARALLEL AND WALKING MECHANISMS

Foundations and principles of parallel and walking mechanisms. Topics include advanced spatial/3D kinematics and dynamics of parallel manipulators and legged/walking mechanisms including workspace analysis, inverse and forward kinematics and dynamics, gait analysis of walking mechanisms, motion analysis of parallel mechanisms as well as legged and walking mechanisms, stability/balance analysis of walking mechanisms, and control of parallel manipulators and walking mechanisms. The course will be useful for solving problems dealing with parallel manipulators as well as multi-legged walking mechanisms including humanoid robots, quadruped robots, hexapod robots and all other types of legged walking mechanisms. A final term project would allow students to apply all this information to design, analyze, and simulate parallel and walking mechanisms. Students taking this course are expected to have a background in kinematics and dynamics.

RBE 595 ST. ADVANCED ROBOT NAVIGATION

In recent years, robots have become part of our everyday lives. Leaving the research labs to be part of the common tools of a household, tools such as robotic vacuum cleaners (iRobot Roomba, Kalorik), pool cleaners (Polaris, Maytronics), Lawn mowers (Landroid, LawnBott) and more abound. For navigating safely, these robots need the ability to localize themselves autonomously using their onboard sensors. 
Potential applications of such systems include the automatic 3D reconstruction, 3D reconstruction of buildings, inspection and simple maintenance tasks, metric exploitation, surveillance of public places as well as in search and rescue systems. In this course, we will dive deep into the current techniques for 3D localization, mapping and navigation that are suitable for robotic applications. Required prerequisites: RBE 500 - Foundations of Robotics, RBE 501 – Robot Dynamics, RBE 502 – Robot Control

RBE 595 ST. MOBILE ROBOTICS

Do you want to learn how to program a Google Car?  This course covers the basics and principles of mobile robotics. This course will teach you the Artificial Intelligence (AI) for mobile robotics, including: kinematics, planning and search, localization, tracking and control. More advanced topics include state estimation using Bayes Filters, Kalman Filters, and Particle Filters. Beyond wheeled mobile robotics, the course will also cover legged locomotion and wearable exoskeleton robots. Programming examples and assignments will help students to understand the principles and apply the methods in the context of building self-driving cars.

RBE 595 ST. SOFT ROBOTICS

Soft robotics studies ‘intelligent’ machines and devices that incorporate some form of compliance in their mechanics. Elasticity is not a byproduct but an integral part of these systems, responsible for inherent safety, adaptation and part of the computation in this class of robots. This course will cover a number of major topics of soft robotics including but not limited to design and fabrication of soft systems, elastic actuation, embedded intelligence, soft robotic modeling and control, and fluidic power. Students will implement new design and fabrication methodologies of soft robots, read recent literature in the field, and complete a project to supplement the course material. Required Background: Differential equations, linear algebra, stress analysis, kinematics.

RBE 595 ST. DEEP REINFORCEMENT LEARNING

This course will cover latest advances in Reinforcement Learning and Control, such as, deep Q learning, actor-critic methods, learning and planning, concurrent trajectory optimization and policy learning, inverse reinforcement learning, hierarchical reinforcement learning methods, forward predictive models, deep model predictive control, exploration strategies, adaptive control, applications to deep robotic learning. Prerequisites: RBE500, Undergraduate course in Linear Algebra, Foundation of Machine Learning or RBE595 Deep Learning for Robot Perception, and Proficiency in Python.

RBE 595 ST. DEEP LEARNING FOR ADVANCED ROBOT PERCEPTION

This course will cover deep learning and its applications to perception in many modalities, focusing on those relevant for robotics (images (RGB and RGB-D), videos, and audio). Deep learning is a sub-field of machine learning that deals with learning hierarchical features representations in a data-driven manner, representing the input data in increasing levels of abstraction.

The course will cover the fundamental theory behind these techniques, with topics ranging from sparse coding/filtering, autoencoders, convolutional neural networks, deep belief nets, and Deep reinforcement networks. We will cover both supervised and unsupervised variants of these algorithms, and we will work with real-world examples in perception-related tasks, including robot perception (object recognition/classification, activity recognition, loop closure, etc.), robot behavior (obstacle avoidance, grasping, navigation, etc.), and more.

The course will involve a project where students will be able to take relevant research problems in their particular field, apply the techniques and principles learned in the course to develop an approach, and implement it to investigate how these techniques are applicable.

RBE 595 ST. ARTIFICIAL INTELLIGENCE FOR AUTONOMOUS VEHICLES

This course is an introduction to deep neural networks and its application in the domain of self-driving cars. Deep learning algorithms are becoming the disruptor agent that is allowing the rapid developments of autonomy in its multidimensional level. Currently, we can find research of deep learning algorithms trying to improve driving performance, detecting pedestrians, detecting traffic, detecting and taking actions while navigating in a highway, reading the traffic signs and evaluating in real-time the readiness of the driver while he/she is on control and more.

Students who enroll in this course will master state-of-the-art technologies that are shaping the future of the field. Students will be exposed to interactive projects in vehicle perception, vehicle cognition, vehicle controls, localization, motion planning, and more.

The course will involve the development of a project where students will be able to take relevant research problems in the field of autonomous vehicles, apply the techniques and principles learned in the course to develop an approach, and implement it to investigate how these techniques are applicable. Required prerequisites: RBE 500 “Foundations of Robotics”, Undergraduate or graduate level course in Linear Algebra, RBE 502 “Robot Control”, RBE 501 “Robot Dynamics, Proficiency in Python

RBE 595 ST. ROBOT PERCEPTION

This course will cover deep learning and its applications to perception in many modalities, focusing on those relevant for robotics (images (RGB and RGB-D), videos, and audio). Deep learning is a sub-field of machine learning that deals with learning hierarchical features representations in a data-driven manner, representing the input data in increasing levels of abstraction. 

The course will cover the fundamental theory behind these techniques, with topics ranging from sparse coding/filtering, autoencoders, convolutional neural networks, deep belief nets, and Deep reinforcement networks. We will cover both supervised and unsupervised variants of these algorithms, and we will work with real-world examples in perception-related tasks, including robot perception (object recognition/classification, activity recognition, loop closure, etc.), robot behavior (obstacle avoidance, grasping, navigation, etc.), and more. 

The course will involve a project where students will be able to take relevant research problems in their particular field, apply the techniques and principles learned in the course to develop an approach, and implement it to investigate how these techniques are applicable.

RBE 595 ST. SENSORS FUSION AND PERCEPTION FOR AUTONOMOUS VEHICLES

This course focuses in Sensor Fusion, Image Processing and Computer Vision techniques for Autonomous Vehicles. The class covers four topics: Image Processing (Image Enhancement, Filtering, Advanced Edge and Texture), 2D/3D Vision (3D Geometry from Multiple view geometry, Motion Processing and Stereo) Sensor fusion (homogeneous fusion, heterogeneous fusion and sensor integration) and Image Segmentation and Object Recognition. Students will be introduced to several existing software toolboxes from Vision and Robotics, and will implement a number of smaller projects 

Moreover, this course presents a variety of tools and approaches for solving fundamental problems involving sensor fusion and perception. Topics to be covered include the mathematical formulation of fusion algorithms, the use of sensor fusion to solve visual perception degeneratives, time domain discrepancies, and accurate reconstruction, and the design and implementation of heterogeneous sensor fusion approaches.  Prerequisite: RBE 500

Systems Engineering

SYS 579. SOFTWARE ENGINEERING FOR SYSTEMS ENGINEERING

Software Engineering for Systems Engineers prepares students to enter the world of systems engineering where software is one of the primary technologies for implementation or integration. This course covers a broad range of topics: process, requirements, modeling, integration, testing and system evaluation. While the scope is ambitious, it covers the salient concepts illustrated by practical projects in the topical areas. The range of topics helps understand how software is practiced, thereby creating critical insights on the process. (Recommended prerequisites: SYS 501).

SYS 579C. COMPLEX DECISION MAKING

One of the biggest ways that you can influence the quality of your life is by improving the quality of your decisions. Complex Decision Making is intended for professionals in management positions and/ or those individuals, regardless of industry, who seek to enhance both their career potential and their overall quality of life. Based on logical principles, and informed by what we know about the limitations of human judgment and decision-making in complex situations, the course trains managers how to think about and structure decisions. These decisions incorporate both their everyday decisions as well as the tough, complex decisions that involve uncertainty, risk, several possible perspectives, and multiple competing objectives, thus improving the quality of the resulting decisions. In addition to teaching formal decision theory and application, we will explore cognitive biases that prevent us from being completely rational in our thinking and deciding. Exit this course able to define the right decision problem, clearly specify your objectives, create imaginative alternatives, understand consequences, grapple with trade-offs, clarify uncertainties, and think hard about your individual values and risk tolerance.

SYS 579D. ENGINEERING DEPENDABLE AND SECURE SYSTEMS

This course considers all facets of engineering dependable and secure systems, i.e., systems that are reliable, available, secure, and can be depended upon to deliver their intended capabilities despite hardware failures, software failures, network failures, external attack, and unexpected behavior. Topics include building dependable system architectures; resilience; security and quality of service of networks; dependability assessment; and software reliability. The class will consist of lectures, case studies, and a class project. (Prerequisite: SYS 501.)

SYS 579R. SYSTEM RELIABILITY ENGINEERING

This course will present reliability, maintainability, and related topics with the breadth of techniques and depth of detail that will benefit the systems engineer by allowing him/her to understand how they relate to the specification, development, testing, and fielding of reliable systems. The reliability of electronics, mechanical equipment, and software will be covered from the component level through their application at the system level. Other key topics will be: reliability prediction; failure modes, effects, and criticality analysis; stress testing; accelerated life testing; and reliability management. In addition, a series of relevant case studies will be studied and discussed.

SYS 579S. SYSTEM OF SYSTEMS ENGINEERING

An innovative approach to engineering complex systems of systems is developed. This approach relies heavily on case studies to drive the discovery of effective techniques. We will discuss complex systems of systems characteristics and behaviors, enterprises, the principled engineering of systems of systems, and distinctions between these forms and conventional approaches. A forward-looking, people-focused approach will be developed, with emphasis on systems thinking; posing a guiding architecture (not just architectural views) up-front that does not change much as the system evolves; balancing competing factors rather than subsystem optimization; pursuing opportunities as opposed to just mitigating risks; sharing information to build interpersonal trust; and communicating individual perspectives to collectively garner better views of the underlying reality. The overall goal is to revisit and broaden one’s “mindsight” in order to build more effective, resilient, scalable, and durable systems. Prerequisites: SYS 501 and SYS 510.

SYS 579X. MODEL-BASED SYSTEMS ENGINEERING II

The course expands upon the principles of MBSE I and demonstrates a variety of modeling languages, analytical methods and tools to help analyze a system. Upon completion of the course the student should have an understanding of the different types of modeling languages, methods and tools available and the limitations of each. This is a survey course that will demonstrate through lecture and case study the use of many languages, methods and tools. (Prerequisite: SYS 521.)

SYS 579Y. MODEL-BASED SYSTEMS ENGINEERING III

The course will require the student to narrow in on a particular modeling language and develop depth in its use to analyze a variety of complex issues that could pop up in higher level systems such as Health Care, Finance, Transportation, etc., that require several engineering disciplines for their development and sustainment. The student is expected to become fully competent in at least one modeling language and demonstrate proficiency in its use and fully understand its limitations. (Prerequisite: SYS 521.)

SYS 579Z. CURRENT TOPICS IN SYSTEMS ENGINEERING

Central to the practice of Systems Engineering is a broad base upon which to build and view problems from. This class will hone the students’ academic skills and provide a strong experience base that would otherwise take many projects to develop. This course will cover two or more topics each week, generally through guest lecturers using their own personal experience or case studies, covering a broad range of topics. In addition to active participation in class and attendance at guest lectures, the student will be expected to conduct independent research and reading about each topic and write a short paper about its applicability to a fictitious or actual project. The fictional project will be provided to the class. The student will also prepare a thesis in an area chosen from a list of topics which could include: Systems Thinking, System Optimization, Use of non-Engineering Disciplines on the SE Team, Systems of Systems Environments, Using Systems Engineering in Social Systems, Using Systems Engineering in Software Intensive Systems and Surety Engineering.

SYS 579#. PROTECTION PLANNING ACROSS THE PROGRAM LIFE CYCLE*

This course presents a strategic view of how to implement Protection Planning theories and methods across a program’s life cycle in terms of tradeoffs among risks, costs and benefits. Topics covered include using criticality analysis to identify mission-critical functions and components and to determine the consequences of losing mission capability; using threat analysis and vulnerability assessment to identify and manage the likelihood of losing mission capability; implementing countermeasures to mitigate or neutralize threats and vulnerabilities; and methods for implementing these and other systems security engineering practices across the various phases of the system life cycle.

SYS 579#. PRACTICAL APPLICATIONS OF SYSTEMS SECURITY ENGINEERING

This course examines several applications or case studies of Systems Security Engineering in practice. The course starts with an overview of Systems Security Engineering and the areas of domains; threats, vulnerabilities, and countermeasures; passwords; and authentication schemes. The course then covers design and architectural trends and techniques in the areas of multilevel and multilateral security; security domains; physical protection; biometrics; emissions; and network defense. Other topics such as cryptography, supply chain risk management, information assurance, software assurance and system evaluation and assurance are explored.