Exploration of how such a variety of devices can use a big range of technologies to connect seamlessly to each other. In the second half of the course we translate the basic knowledge of the protocols to more hands-on exercises in containerization ... (Docker) and at the end we give an introduction to Kubernetes, that is an open-source system for automating deployment and management of containerized applications.
Prerequisite: ENPM602

This course builds on the topics discussed in TCP/IP Networking (ENPM 631) and provides more in depth discussion of networking and application development. Highlights include IPv6, Socket programming, Docker, Kubernetes, Helm charts, Multi-protocol ... Label Switching (MPLS), and Internet security.
Prerequisite: ENPM631

This course will introduce students to HDL-based design of modern digital systems, and will cover in depth the design and implementation of digital systems using the Verilog HDL. Students will learn fundamental concepts of the Verilog language; ... modeling of complex digital systems; simulation and veri.cation; and Verilog coding styles for synthesis. Hands-on experience will be developed through practical designs, exercises, and projects. Students will use state-of-the-art EDA tools to design, simulate, and test digital systems. The latter part of the course will focus on customized programmable platforms such as graphics processors (GPUs) multicore platforms and FPGAs as well as coding, building, and debugging for such platforms.
Previously offered as ENPM808R.

An overview of VLSI test process and equipment, faults, fault modeling, fault simulation, combinational logi ATPG, sequential logic ATPG, Iddq testing, function testing, memory testing, delay testing, design for testability, BIST (Built-In ... Self-Test) and boundary scan.
Prerequisite: ENEE244; or students who have taken courses with similar or comparable course content may contact the department.

Focuses on networking aspects, protocols and architectures for Wireless Sensor Networks. Provides a thorough description of the most important issues and questions that have to be addressed in a wireless sensor network.

Machine learning may be used to greatly expand the capabilities of robotic systems, and has been applied to a variety of robotic system functions including planning, control, and perception. Adaptation and learning are particularly important ... for development of autonomous robotic systems that must operate in dynamic or uncertain environments. Ultimately we would like for the robots to expand their knowledge and improve their own performance through learning while operating in the environment (on-line and/or lifelong learning). Robot Learning covers the application of learning techniques including Reinforcement Learning, Learning from Demonstration, and Robot Shaping that may be used with a variety of machine learning paradigms for which data is used to generate (through induction) models that are then used by the robot to perform tasks. A wide variety of paradigms are available to generate models (e.g., CMAC, KNN, MLP, lazy learning, LWR, RBF, deep networks). These learning techniques and paradigms are then combined with traditional robotic control approaches (e.g., motor schema, behavior-based, direct and inverse methods) to create controllers to control the robots while operating in real-world environments. This graduate course will explore the application of machine learning techniques, paradigms, and control design to robotic systems, focusing primarily on key useful representations and model building techniques for application in non-stationary robotic systems.
Formerly: ENPM808F.

This course teaches the fundamentals of secure programming in C. An in depth discussion on various security vulnerabilities (e.g., buffer overflows) in C applications will be taught with hands-on demo of concepts during the class. Students ... will learn how a C program runs “under-the-hood”. The course will teach nitty-gritty of C programs by analyzing at the assembly level. The course discusses best practices (e.g., coding standards) and design principles for secure programming so that security can be built-in during design time. In addition to assignments, students are required to present papers related to this course.

This course is an introduction to the diverse field of cryptography and network security. We start by an introduction to the principles of cryptography and review common encryption standards as the basis for many security protocols. We review ... current standards for cryptography, hash functions, message authentication, digital signatures, as well as certificates and key management. The second part of the course reviews some of the well-known security protocols at different network layers (Application, Transport, IP, and MAC) as examples of how fundamental concepts are utilized for providing the corresponding security requirements. The course combines theoretical discussions and hands-on assignments to provide the students with a longer lasting understanding of the concepts.
Prerequisite: An operating systems and/or network protocol course or equivalent. Formerly: ENPM808N.

Provides an in-depth review of the Internet with a focus on the end-to-end effects of technologies and protocols that operate in different layers. All protocols and technologies are covered in a holistic framework with an emphasis on their ... effect on the network and application performance. The course also includes a brief introduction of more modern concepts in the field of networking such as SDN and NFV to encourage deeper study of those topics.
Note: Previously offered as ENPM808A

Communications and Signal Processing
The course will look at the components of manufacturing robots, including architectures, knowledge representation, planning, control, safety, standards, and human-robot interaction. Students will explore the work that is being performed around ... the world in each of these areas, and will perform small hands-on exercises in class to gain a deeper understanding of how a selected set of these technologies can be applied to real-world challenges. This course will have invited presentations from experts in the field.

Computer Engineering
The goal of this course is to learn network systems from a practical viewpoint. The course begins with an introduction to network architecture and protocols, and continues with an in-depth hardware oriented look at the key elements of network ... systems, such as switches. The focus is on the implementation aspects of a network, without going much deep into the details of network layers and protocols themselves.

The course will cover the challenges and rewards from harvesting renewable energy offshore. The phenomenal growth of data assets (both within organizations and publicly) combined with advances in technology (in cloud computing and machine ... learning) have allowed organizations to leverage their data assets to enhance their decision making processes in order to achieve their missions. In addition, the availability of low-cost computing resources and open-source tools have accelerated this process. This course focuses on practical methods to extract meaningful insights from (internal and external) data sources and to communicate those findings to stakeholders so they can be incorporated in operational work processes to achieve organizational goals. Students will use relevant tools, technologies and approaches for data manipulation, statistical analysis, predictive modeling, and visualization in case studies/projects. Cloud computing and distributed computing platforms including architectural approaches in implementing a Big Data Platform for decision making will be covered. Students will use analytics tools and scripting languages for the implementation of projects. Some example languages/tools introduced are: Python and Tableau. Prior knowledge of scripting language such as Python or R will be helpful – but not required.

Data science encapsulates the interdisciplinary fields for creating data-centric products and applications towards specific needs. This course’s goal is to provide an introduction to data science from the perspective of security expert. Due ... to increased sophistication of targeted attacks and insufficiencies of traditional passive security measures in mitigating persistent targeted hackers, data science with the use of Big Data techniques is considered as a promising alternative for identifying threats and attacks. This specialized field includes (a) systems for storing and processing massive data sets; (b) data analytics for extracting information from such data sets; (c) security for assessing the cyber attacks.

As the robotics industry continues to grow and evolve, software's role in these products and systems is also becoming more critical. From embedded controls to advanced perception and learning, software permeates today's robots. Building off ... domain expertise developed in other robotics courses, this course teaches the tools and processes to develop professional quality software for deployed systems and products. Students will learn the best practices of taking new ideas or prototypes, and understanding what it takes to build the complex software that is so important to today’s commercialized robotic systems. The course is split into two parts: the first will review the C++ programming language, object-oriented programming (OOP) concepts, version control, testing, and agile software development processes; the second will introduce the popular Robot Operating System (ROS) framework with intensive programming assignments and projects. Students should be proficient in using Linux, programming with C/C++ and understand the concepts of object-oriented programming.

This course provides students with an understanding of the fundamental concepts of artificial intelligence and machine learning and explores applications of these concepts to real world problems.  We will review the topics of design of ... intelligent agents, logic and symbolic reasoning, probabilistic reasoning and decision making, knowledge representation, problem solving techniques, and deep learning. The students will be assigned to apply AI and machine learning techniques in various areas of their interest like cybersecurity, robotics, advanced electronics, software verification, database validation, data cleaning and imputation, product design, environmental engineering, healthcare, drug discovery, etc.  Upon completion of the course students should be able to develop solutions to concrete problems and create models and tools based on AI and deep leaning techniques.
Prerequisites: There are no formal requirements for this class although students should be familiar with basic concepts in probability theory and statistics, linear algebra, and have some coding skills (e.g., Python)

Communication & Signal Processing
This course covers the fundamental aspects of Internet of Things (IoT) including IoT architecture, network protocols, data management and access technologies, IoT security threats and technologies

This course will introduce methods for analyzing and understanding the structure and function of networks, including social networks, web graphs, and sensor networks. The course will introduce students to the math and science of network analysis. ... Through real world examples, including analysis of their own networks, students will develop skills for describing and understanding the structure, patterns, and functionality of networks. Students will read classic and cutting edge articles and books about these topics and discuss their applicability to various network types. The class will culminate with a capstone project in which students will apply the analysis methods they have learned to understanding a particular question about a network they choose.

Communications and Signal Processing
Linear System Theory is used to derive Shannon’s Sampling Theorem.Shannon Sampling and Linear System Theory are used to derive the ideal analog-to-digital converter model. Bennett’s quantization noise model is used to derive performance ... metrics for the ideal analog-to-digital converter. Test and measurement of analog-to-digital converters is presented. Practical applications for sampling theory and analog to digital conversion are discussed.

Computer Engineering
...
Prerequisite: ENEE244, ENEE245 (or equivalent)

Communications and Signal Processing
The course will teach the concepts and techniques for Software Defined Networking (SDN), including an overview of available controllers, switches, and tools. More specifically, the course will cover a review of basic networking concepts, motivation ... and use cases for SDN, general SDN architecture and building blocks, Network Functions Virtualization (NFV), SDN protocols, and existing SDN applications.

Computer Engineering
The course will teach the algorithms, heuristics, and code development skills in solving numerous sorting, graph theory, string processing, and job scheduling problems. It provides both a broad coverage of basic algorithms and data structures ... and in-depth coverage on selected topics. Moderate to heavy programming in C is expected. Through this study the students will improve their problem solving skills via various algorithm development examples and exercises.