Master of Science in Biomedical Engineering

Year 1, Fall semester

9 credit hours

• 0402580Introduction to Biomedical Engineering3 credits

  This course serves as an introduction to and overview of the field and areas of biomedical engineering. The biomedical engineering areas such as bioelectric phenomena, bioinformatics, biomaterials, biomechanics, bioinstrumentation, biosensors, biosignal processing, biotechnology, computational biology and complexity, genomics, medical imaging, optics and lasers, radiation imaging, tissue engineering, and moral and ethical issues will be covered in this course. Historical perspective of the major developments in a specific biomedical engineering domain as well as the fundamental principles that underlie biomedical engineering design, analysis, and modeling procedures in that domain are also included. In addition, examples of some of the problems encountered, as well as the techniques used to solve them, are provided.

• 1440515Mathematical Methods for Bioengineering3 credits

  The course offers mathematical methods for solving application problems in biomedical engineering, including computational techniques centered at many aspects of systems biology and bioengineering research. Mathematical concepts related to modelling of physiological bio-molecular processes are considered. It will cover the fundamental technique to Ordinary Differential Equations (ODE) using Laplace transformation, Fourier series, integrals, and solve Partial Differential Equations (PDE) including Bessel function, Legendre polynomials, and introduce complex analysis. Theory of supervised and unsupervised learning, Monte Carlo computation, analysis of gene expression data and genome sequence data. The course will also cover classical equations for mathematical physics: heat equations, wave equations, and potential equations. Representation and analysis of bio-signals, biological fluid mechanics, pharmacokinetics and mathematical diffusion will be covered as well. Numerical solving will be based on the use of MATLAB.

• 0402501Engineering Research Methodology3 credits

  Students learn how to apply the engineering research process and methods of inquiry to solve engineering problems. Literature survey for research work, building expertise in the areas of interest, this involves critiquing current research work. Basic principles of experimental designs; analyze and evaluate the results. Evaluate the quality of the results and limitations. They will also learn how to communicate findings in specific engineering formats to specialist audiences. Students will learn basic project management and teamwork skills in addition to research ethics. Course project will allow the students to apply research methodology components on research problems of their choice. Students, possibly in small teams, are expected to present and defend their research proposals.

Year 1, Spring semester

9 credit hours The Hospital Labs Rotation carries 2 credit hours in the course list and study plan; its course description calls it a non-credit course.

• 0502500Hospital Labs Rotation2 credits

  Masters students in Biomedical Engineering are required to take lab rotations at the University Hospital Sharjah (UHS). This non-credit course will introduce the students to biomedical equipment and tools used in various clinical departments. For example, the students will rotate in the anesthesia and surgical department, interventional and stress-test cardiovascular procedures labs, orthopedic and physical rehabilitation facilities, radiology department, medical diagnostic labs, and central hospital laboratory.

• 0402582Graduate Seminar1 credit

  Students are required to attend seminars given by faculty members, visitors, and fellow graduate students. Each student is also required to present a seminar outlining the research topic of the master thesis.

• Elective Course 13 credits
• Elective Course 23 credits

Year 2, Fall semester

9 credit hours

• Elective Course 33 credits
• Elective Course 43 credits
• 0402600Master Thesis3 credits

  The student has to undertake and complete research topic under the supervision of a faculty member. The thesis work should provide the student with an in-depth understanding of a research problem in Biomedical Engineering. It is expected that the student, under the guidance of the supervisor, will be able to conduct research somewhat independently, and may also be able to provide solution to that problem.

Year 2, Spring semester

6 credit hours (programme total: 33)

• 0402600Master Thesis6 credits

Elective track: Bioelectronics and Instrumentation

Elective Courses 1 to 4 (12 credit hours) are chosen, with the supervisor's guidance, from the tracks below. R = Recommended, O = Optional; the offering college or department follows each title. Codes and descriptions are shown where the page publishes them.

• 0402583Biomedical Sensors and Instrumentation (Recommended, EE)3 credits

  This course will identify basic principles involved in biomedical sensors instrumentation, mechanisms of different sensors, their classification, regulation and ethical use. Biosensors principles, types and properties, performance factors in biosensors, enzymatic biosensors etc. Development of an understanding of the measurement principles of medical instrumentation, including noise-filtering instrumentation amplifier, computer control, sampling, data collection of bioelectrical signals (ECG, EEG, EMG), measurement of respiratory function, cardiac variables, blood pressure, and blood flow. This knowledge will be applied to solve real world problems of medical device development, troubleshooting, and the identification of ethical principles in the use of medical sensors in patient care.

• 0402584Implantable Biomedical Microsystems (Optional, EE)3 credits

  A general overview on the multi-disciplinary field of implantable biomedical microsystems is introduced in the course. The material to be covered comprises extensive contents and in-depth discussions on both system- and circuit-level aspects of the design of implantable microsystems. This includes wireless interfacing, microelectrode array fabrication, and circuit design for implantable neural recording microsystems. Different design aspects of neural stimulation microsystems, cochlear implants, and visual prostheses are also reviewed briefly. Key issues of biomaterial/tissue interactions such as foreign body response and biocompatibility and biocompatibility assessment are covered. Issues concerned with design for implantability and envisions for testability are also dealt with.

• 0402585Mixed Analog-Digital IC Design (Recommended, EE)3 credits

  This course will provides a solid understanding and an overview of analog and mixed-signal integrated circuit analysis, design, simulation, and layout consideration for Low frequency applications. Examination of Gilbert multipliers; operational amplifiers; frequency compensation techniques; advanced biasing techniques; voltage references; and mixed-signal systems such as compactors and data converters including analog-to-digital converters (ADC) and digital-to-analog converters (DAC). Students will learn transistor-level design of analog and digital circuits, layout techniques for analog and digital circuit modules, and special physical considerations that arise in a mixed-signal integrated circuit. Students will design a custom mixed-signal integrated circuit over the semester in the course project that will be submitted at the end of the semester.

• 0402586Advanced Signal Processing for Biomedical Engineering (Optional, EE)3 credits

  Introduction to advanced concepts in biomedical signal processing that go beyond the conventional analysis of linear, stationary, normal signals. Allow students to develop computational algorithms for analysis of clinically relevant physiological signals. Identifying modern signal processing tools used to analyze major physiological signals in order to investigate the generation, the form, the dynamics and the information content of the signals; and draw diagnostics/prognostic conclusions, based on quality signal processing, about the normality and abnormality of the organ systems.

• Biomedical Nanotechnology (Optional, EE/Chem)3 credits

Elective track: Biomedical Systems and Feedback Control

• Measurement and Instrumentation in Physiology and Medicine (Recommended, MD/HS)3 credits
• 0402586Advanced Signal Processing for Biomedical Engineering (Recommended, EE)3 credits
• Modelling in Physiology and Medicine (Optional, MD/CS)3 credits
• Linear and Non-Linear Multivariable Control System (Optional, EE/MD)3 credits
• Human-Machine Interaction (Optional, MD)3 credits
• Robotics and Dynamics control in Biomedicine (Optional, EE/MD)3 credits
• Neural Networks and Biomedical Applications (Optional, EE/MD)3 credits
• Robust Feedback Control (EE)3 credits
• Biomedical Image Processing (Optional, EE/MD/HS)3 credits

Elective track: Applied Computational Bioengineering

• Statistics, Data Analysis and Algorithms in Genomic Biology (Recommended, CS/MD)3 credits
• 0402593Applied Parallel Programming for Bioengineering (Optional, EE/CS/MD)3 credits

  Introducing fundamental issues in design and development of parallel programs for various types of parallel computers. The course will cover various programming models including linear programming based on SSR (Sequence Selection and Repetition) according to both machine type and application area. Cost models, debugging, and performance evaluation of parallel programs with actual application examples. Parallel programming with emphasis on developing applications for processors with many computation cores. Computational thinking, forms of parallelism, programming models, mapping computations to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, and application case studies from the bioengineering High-Performance Application field.

• 0900720Introduction to System Biology Modelling (Optional, CS/MD)3 credits

  The goal of this course is to highlight elementary design principles inherent in biology. Many of the underlying principles governing biochemical reactions in a living cell can be related to network circuit motifs with multiple inputs/outputs, feedback and feedforward. This course introduces the student to methods that can be used to tackle complex systems head-on using case studies that comprise the foundation of systems biology. The initial lectures of the course focus on bringing students quickly up to speed with a variety of modeling methods in the context of a synthetic biological circuit. This is later applied on much more complicated network models are addressed, including transcriptional, signaling, metabolic, and even integrated multi-network models. In order to achieve those objectives the course introduces the student to mathematical techniques for quantitative analysis and simulations of basic circuits in genetic regulation, signal transduction, and metabolism. Several continuous and discrete mathematical models such as ordinary, partial differential equations, dynamical systems and stochastic processes are used to formulate evolutionary biological models. Numerical methods are used in explaining the self-organization of biological networks; and biochemical simulations are discussed using recent and suitable software. The course will use case studies on topics that include end-product inhibition in biosynthesis, optimality and robustness of the signaling networks and kinetic proofreading.

• 0900707Bioinformatics Networks (Optional, CS/MD)3 credits

  This course will introduce students to Biological databases especially GenBank at the NCBI in addition to some of the most commonly used software and tools for genetic analysis of nucleic acid, protein sequences and designing primers and probes for PCR. In addition, the course explores and explains some of the computational biology tools found on the Internet and how they can be applied to problems in genomics and molecular biology to extract genomic signature that can shed light on the molecular mechanism of disease.

• 1411531Machine Learning (Optional, CS/MD)3 credits

  This course provides a broad introduction to machine learning. Topics include Regression: Simple and Multiple, Ridge, Kernel Feature, Feature Selection Lasso; Classification: supervised learning such as Linear Classifiers Logistic Regression; Decision Trees, support vector machines, and neural networks; unsupervised learning (such as clustering, recommender systems, deep learning); and best practices in machine learning such as Overfitting/Regularization and bias/variance theory.

• 1411565Data Mining (Optional, CS/MD)3 credits

  Data mining has become one of the most interesting and rapidly growing fields. Data mining techniques are used to uncover hidden information, such as patterns, in databases and perform predictions. The data to be mined may be complex data including multimedia, spatial, and temporal. Topic include data processing, association rules, clustering, and classification. This course is designed to provide graduate students with a solid understanding of data mining concepts and tools.

• Neural Networks and Biomedical Applications (Optional, EE/MD)3 credits

Elective track: Biomedical Physics and Imaging

• 0502502Medical Imaging and Instrumentation (Recommended, HS/Phy)3 credits

  This course covers the physics, equipment and technical principles underlying the following medical instrumentation methods: tissue culture and in vitro imaging, X-ray radiography, computed tomography (CT), single photon and positron emission tomography (SPECT), positron emission tomography (PET) including SPECT/CT and PET/CT, magnetic resonance imaging (MRI), ultrasound (US) and doppler imaging techniques. It will also address the mathematical framework describing image encoding/decoding, point-spread function/modular transfer function, signal-to-noise ratio, contrast behavior for each of the medical imaging modalities. The use of commercial software is advised for the implementation and study of basic concepts.

• Biomedical Image Processing (Recommended, EE/MD/HS)3 credits
• 0502503Molecular Imaging Application (Optional, MD/HS)3 credits

  This course introduces a new discipline that combines cell biology, molecular biology, and diagnostic imaging. Two basic applications of molecular imaging are diagnostic imaging and therapeutic. This course will focus on the role of diagnostic imaging in detecting molecules, genes, and cells in vivo that are specific to a disease; mainly by the ability to identify receptor sites related to target molecules characterizing the disease studied. Emphasis will be on how these molecular imaging techniques can help study molecular mechanisms of disease in vivo. Topics include DNA/protein synthesis, transgenic mice, novel contrast agents and small animal imaging.

• Modelling in Physiology and Medicine (Optional, MD/CS/HS)3 credits
• 1430501Biomedical photonic (Optional, MD/Phy/Pharm)3 credits

  This course studies interaction between light and biological materials; it then uses the information gathered to search and study for non-invasive diagnostic methods. The course should provide knowledge about light sources and light delivery systems, optical biomedical imaging techniques, optical measurement technologies and their specific applications in medicine. Fundamental principles will be accompanied by practical and contemporary examples. Different selected optical systems used in diagnostics and therapy will be discussed, new techniques for live cell imaging used in early diagnosis for cancer, diabetes, or other diseases will be also reviewed. Fluorescent probes and some nanotechnology applications like quantum dots will be included.

• 0407550Radiation Measurements and Instrumentation (Optional, NE/HS/Phy)3 credits

  This course covers a background in therapeutic radiotherapy instrumentation, dosimetry and treatment planning. Clinical radiation generators considered include kilovoltage units, Van de Graafs, Linacs, beatatron, microtron, cyclotron and radionuclide based units. Means for dose measurement using ionization chambers, solid state detectors (TLD), calorimetry, film and chemical dosimetry as well as dosimetric calculation methods employing depth doses, tissue air ratios, tissue maximum ratios, irregular field techniques and methods for inhomogeneity corrections. The concept of Monte Carlo will be introduced through simulation lab to help students understand the characteristics of ionizing radiation in simple and complex situations.

• 0502501Advanced Radiobiology and Radiation Protection (Optional, HS/Phy/NE)3 credits (2+1)

  The course covers the basic principles of ionizing radiation and its physical and biological effects. The physical interactions of photons as well as of charged particles; the factors which underpin the differing radio-sensitivities of different tumors and normal tissue versus tumor tissue; fundamentals in dosimetry; deterministic as well as stochastic effects; and fundamental knowledge about radiation protection. Generation of ionizing radiation including the x-ray tube, the clinical linear accelerator, and different radioactive sources in radiology, and radiotherapy are addressed. Applications in radiology, clinical radiotherapy, and radiation protection are studied through practical scenarios.

• Biophysics (Optional, HS/MD)3 credits
• Principles of Tissue Engineering and Gene Therapy (Optional, MD/Biotech)3 credits

Elective track: Biomaterials, Cellular, and Tissue Engineering

• Principles of Tissue Engineering and Gene Therapy (Recommended, MD/Biotech)3 credits
• Advanced Cell Biology (Recommended, Biotech)3 credits
• 0900707Bioinformatics Networks (Optional, CS/MD/Biotech)3 credits
• Biomaterials for Medical Applications (Optional, CH/ME)3 credits
• Cellular and Molecular Neuroscience (Optional, MD)3 credits
• Novel Drug Delivery Systems (Optional, Pharm)3 credits
• Stem Cell Biology and Engineering (Optional, Biotech)3 credits

General Electives

• Independent Studies in Biomedical Engineering (Optional, ALL)3 credits
• Selected Topics in Biomedical Engineering (Optional, ALL)3 credits
• Commercialization of Biomedical Innovation (Optional, ALL)3 credits
• Healthcare Operation, Planning, and Risk Management (Optional, ALL)3 credits

Also described on the programme page

The page describes this course, which its elective tables do not list under this title.

• 1401020Computational Data Analysis for Bioengineering3 credits

  Fundamentals of data analysis in bioengineering, including statistical methods, hypothesis testing, and model fitting. Methods include regression analysis, ANOVA, Bayesian statistics, and machine learning approaches for high-dimensional data. Applications of these methods to bioengineering problems, such as genomics, proteomics, and imaging data analysis, will be emphasized. The course will include hands-on experience with software tools for data analysis.

Remedial courses

Two remedial courses are assigned according to the bachelor's background: Bachelor's Degree in Engineering or Science (except Biotechnology): 0901720 Human Anatomy and Physiology (2+1, Medicine), 1450251 Cell Biology (3, Biotechnology) Bachelor's Degree in biotechnology: 0901720 Human Anatomy and Physiology (2+1, Medicine), 1440262 Mathematics for Engineers (3, Math) Bachelor's Degree in medicine, Dental Medicine, or Health Science: 1440261 Differential Equations for Engineers (3, Math), 1440262 Mathematics for Engineers (3, Math) Bachelor's Degree in pharmacy: 1440262 Mathematics for Engineers (3, Math), 1450251 Cell Biology (3, Biotechnology)