Donghua University

The English-taught Master of Engineering (M.E.) in Information and Communication Engineering program extensively enrolls and cultivates worldwide master’s degree students under the primary discipline Information and Communication Engineering, including the two sub-disciplines of Communication and Information Systems, Signal and Information Processing. 

The main research themes of the discipline relates to the access, processing, transmission, and control of information in various communication and information systems. The research fields cover wireless communication and remote monitoring, network communication and sensor network, internet of things, coding and information security, image processing and pattern recognition. 

The program has endeavored in cultivating high-level international talents with comprehensive capability in Communication and Information Systems, Signal and Information Processing.

In line with the Chinese university system, Masters degrees in Donghua University last for two years and a half and the school starts from fall. A typical workload of the first year full-time study is a set of courses worth a total of 34 credits, composed of 22 compulsory and 12 elective credits. From the second year, students will undertake a research project and write a dissertation. 

Students who are academically qualified, successfully fulfill 34 credits within designated years, accomplish the dissertation, pass the thesis defense and finally be approved by Donghua University Academic Degree Evaluation Committee will be awarded Master Degree in Engineering of Donghua University.

Compulsory Courses (22 credits)

• Modern mathematical methods (3 credits) 
• Modern signal processing (3 credits) 
• Information theory and coding (3 credits) 
• Image communication and information processing (3 credits) 
• Introduction to China (2 credits) 
• Chinese Language (8 credits) 

Elective Courses (at least 4 from the courses below)

• Modern communication technology (3 credits)
• Embedded systems: theory and application (3 credits) 
• Information security of networks(3 credits) 
• Data Mining (3 credits) 
• Internet of Things (3 credits) 
• Pattern Recognition: Theory and Technology (3 credits) 

Modern Mathematical Methods

Introduction to Modern Mathematics presents a collection of expository introductions to, and surveys of, several active and important topics in mathematics. This course focuses on the general theoretical framework and basic method of modeling and simulation, and focuses on several classical methods and modern methods based on system identification and modeling methods. Through the analysis of typical cases, the idea and method of system simulation are expounded. It is helpful to understand the basic theory of system simulation, the representative of the domestic and international research, and to lay the foundation for the study of the students in this direction. The objective is to make the students have a comprehensive understanding of modern mathematics, which is a specific and clear understanding of the application of modern mathematics method in solving scientific problems.

Modern signal processing

The goals of this course are to enable you to apply digital signal processing concepts to your own field of interest, to make it possible for you to read the technical literature on digital signal processing, and to provide the background for the study of more advanced topics and applications. The topics of this course include: 1) Review of Linear Continuous-Time Signal Processing; 2) Introduction to Real-Time Computation; 3) Sampling and Reconstruction; 4) Discrete-Time Signal Processing; 5) Discrete Spectral Analysis; 6) Real-Time Simulation Methods Using Difference Equations; 7) Filter Design -Continuous and Discrete; 8) Statistical Signal Processing.

Information theory and coding

Information theory is based on probability theory and statistics. Coding theory is one of the most important and direct applications of information theory. This course is of two parts: part one introduces basic results in the information theory and part two introduces the fundamentals of coding theory and applications to state-of-the-art error correcting and error detecting codes. Furthermore, a wide range of examples of coding are also provided.

Image Communication and Information Processing

This course introduces the basic concepts, basic principles and problem-solving methods for image communication and information processing. So that students master the basic image processing methods, understand the various applications related to information processing technology. The main contents of this course include the procedure of image processing and image communication, fundamentals of image communication, point operations, local operations, global operations, region oriented segmentation, contour-oriented segmentation, Hough transform, morphological image processing, texture analysis, pattern recognition and image sequence analysis. Training students to master the ability to solve practical problems related to image processing under VC ++ environment.

Modern Communication Technology

This course is for graduated students in communication, electronic, automation, and computer engineering. It aims to provide the frontier of modern communication and networks technologies, which includes1) introduction of communications and networks; 2) mathematical foundations for wireless communication and networks; 3) hot topics for current wireless communication area in theory and systems; 4) hot topics for current networking area in theory, system, and applications; 5) experiments in wireless communication and networks related area. 

Embedded systems: theory and application

Embedded system is a widespread-used computer system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints. The learning objectives of this course focus on how to design an integrated embedded system based on Programmable System On Chip 5 Low Power (PSOC5LP) with ARM Cortex-M3 core from Cypress, and include: foundation of embedded systems, introduction of ARM Cortex-M3 and PSOC5LP, Proteus embedded systems simulation software, PSOC Creator Integrated Development Environment (IDE), programmable analog subsystem of PSOC5LP, programmable digital subsystem of PSOC5LP, c/os III Real Time Operation System (RTOS), and design of integrated embedded system.

Data Mining

Data mining refers to a set of techniques that have been designed to efficiently find interesting pieces of information or knowledge in large amounts of data. Interest in the field is motivated by the growth of computerized data collections which are routinely kept by many organizations and commercial enterprises, and by the high potential value of patterns discovered in those collections. This course is to introduce basic concepts and techniques of data mining, and present the techniques most commonly employed in the analysis of large volumes of data, in the extraction of knowledge from this data, and in making decisions based on the knowledge acquired. In this course we explore how this interdisciplinary field brings together techniques from databases, statistics, machine learning, and information retrieval. We will discuss the main data mining techniques currently used, including data cleaning, clustering, classification, regression, forecasting, association rules mining, and recent techniques for web mining. After finishing this course, students will be able to understand typical data mining process and the different mining algorithm available by popular commercial data mining software; and know the different types of problems (tasks) that can be addressed through data mining.

Internet of Things

The Internet of Things (IoT) refers to the network of physical objects or “things” embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. On the basis of better usage of resources, better control over infrastructure, assets or service, IoT has considerably improved user’s decision. As one of the most significant and innovative technology, IoT has been applied successfully to build smart cities, smart environment, smart agriculture as well as major improvements in industrial application, security & emergency operation, health monitoring and home automation. The main objective of the course is to provide a comprehensive understanding of the IoT into the world of “smart” solutions, including basic skills to understand the architectural design and fundamental concepts of IoT. Topics mainly include an overview of the current researches in the IoT, connected product concepts, development platforms, user experience, challenges and future directions. This course enables students to understand what IoT technologies are and what is required in certain scenarios. After the course the students should have some knowledge of the architectures, models and applications, and understand the basic principles behind them.

Image Communication and Information Processing

This course introduces the basic concepts, basic principles and problem-solving methods for image communication and information processing. So that students master the basic image processing methods, understand the various applications related to information processing technology. The main contents of this course include the procedure of image processing and image communication, fundamentals of image communication, point operations, local operations, global operations, region oriented segmentation, contour-oriented segmentation, Hough transform, morphological image processing, texture analysis, pattern recognition and image sequence analysis. Training students to master the ability to solve practical problems related to image processing under VC ++ environment.

Pattern Recognition: Theory and Technology

With the ever increasing amounts of data in electronic form, the need for automated methods for data analysis continues to grow. The goal of pattern recognition is to develop methods that can automatically detect knowledge hidden in data, and then to use the uncovered patterns to predict future data or other outcomes of interest. This course provides a detailed introduction to pattern recognition theory and techniques, and includes worked examples drawn from application domains such as molecular biology, text processing, computer vision, and robotics. The aim of this course is to provide descriptions of the most useful pattern processing techniques including many of the recent advances in nonparametric approaches to discrimination and Bayesian computational methods developed in the statistics literature and elsewhere. Discussions provided on the motivations and theory behind these techniques will enable the practitioner to gain maximum benefit from their implementations within many of the popular software packages.