International Master of Science (M.Sc.) in Embedded Artificial Intelligence

Introduction

The International Master of Science Program in the Field of Embedded Artificial
Intelligence (M.Sc. EmbeddedAI), jointly funded by the Erasmus+ program of the European Union (EU), is a collaboration involving three European universities - University of Siegen (USI), Kungliga Tekniska Högskolan (KTH), Alpen-Adria-Universität Klagenfurt (AAU) - and five Nigerian universities - (University of Port Harcourt (UPH), University of Abuja (UoA), Michael Okapra University of Agriculture Umudike (MOU), Abubakar Tafawa Balewa University (ATB), and Obafemi Awolowo University (OAU).

EmbeddedAI is designed to equip Nigerian students with the latest digital trends, improving employability and entrepreneurship. It is also meant to help Nigerian Higher Educational Institutions (HEIs) engage with various EU HEIs and cultural backgrounds to promote globalisation and technology growth based on their preferences and expectations.

For years, Artificial Intelligence (AI) has been at the top of the technology must-watch list for evolving trends and applications. With the ability to build smart machines that simulate human intelligence, the implications for technological advancement across numerous sectors are endless. Over the past several years, an important shift has occurred from cloud-level to device-level processing of artificial intelligence tasks, data and results. Embedded AI is one direct consequence of the shift to edge computing. Embedded Artificial Intelligence (AI) applies machine and deep learning in software at the device level. 

The use and application of embedded AI are vast, as seen in automating processes, providing advanced analytics and business insights, and improving customer service, among numerous other benefits. The application of Embedded AI cuts across several industries such as agriculture, aviation, finance, healthcare, manufacturing, retail, shipping, and supply chain.

The embedded AI skillset is particularly useful for promoting entrepreneurship, where young people are equipped with the complete competence to realize new products that utilize artificial intelligence. Digitalization is identified as the most extended and significant technological development in all sectors of the Nigerian economy. Embedded AI will allow Nigerian HEIs, industries, and entrepreneurs to utilize data to facilitate economic growth.

Philosophy

The philosophy of EmbeddedAI is to establish an international Master of Science program in the field of embedded Artificial Intelligence with key specialization in embedded systems and a strong emphasis on industrial involvement, which includes application domains for agriculture, healthcare, telecommunication, and manufacturing. It includes establishing a corresponding curriculum with well-defined rules for admission, evaluation, teaching methodologies, certification awarding, and accreditation. In addition, EmbeddedAI aims to use AI and related technologies as an opportunity to create and reinforce diversity. The key to this will be to facilitate and promote the skills development of diverse people and make concerted efforts to level the playing field for women and other minorities in the industry.

Objectives

The objectives of the program are to:

1. Equip Nigerian students with the latest digital trends, improving employability and entrepreneurship.

2. Train and develop manpower for Embedded AI industries and opportunities.

3. Develop Embedded AI Solutions in relevant fields: Agric, Medicine, oil & Gas, Energy, Power, etc.

4. Foster cooperation and collaboration among Nigerian universities.

5. Address unemployment and boost international collaboration for the Nigerian universities.

Admission Requirements

In addition to satisfying the School of Postgraduate Studies regulations for higher degrees, candidates seeking admission for the M.Sc. Embedded AI Program must have the following qualifications:    

1. Five O/Level credit passes including English, Mathematics, Physics and Chemistry.

2. A Bachelors’ degree with at least a Second Class Lower (2:2) classification or equivalent in any of the   Engineering  fields or  Physical Sciences (e.g.,  Computer Science,  Mathematics, Statistics, Physics,  Information Technology, etc., or related fields).

3. Candidate with an upper credit pass in the Postgraduate diploma would also be considered.

Mode of Study and Duration

The Mode of study shall be Full Time

The duration shall be a minimum of three semesters and maximum of six semesters, that is, minimum of 18 months and maximum of 36 months.

Good Standing

Any student who fails in any course, shall repeat such a course or do a resit. A student whose cumulative Grade Point Average (CGPA) falls below 2.5 at the end of 2 consecutive semesters shall be required to withdraw from the programme.

Graduation Requirements:

To be awarded the M.Sc. in Embedded AI, a candidate must have taken and passed the prescribed number of Compulsory and Elective Courses selected from the approved list, and totalling 42 units. This is in addition to the dissertation which carries six (6) Units, totalling forty two (48) Credit Units as follows:

Core Courses 24

Elective Courses 18

Dissertation   6

Total 48

Course assessments shall be at the end of every semester. The minimum pass score in any course shall be  50% (C); continuous assessment shall constitute not less than 30% of the examination for each course.

Curriculum

NCU=Nigerian Credit Units.   EEC= European Equivalent Units (The courses have been mapped to the European course units to allow for transferability)

CORE COURSES

NB:  A student must register and passed at least   total of 48 Nigerian   credit units to graduate.

Course Structure

1st SEMESTER COURSES

Compulsory Courses                     Credit Units Status

EAI 801  Fundamentals of Embedded System Design  3  C

EAI 802  Embedded Artificial Intelligence  Control 3 C

EAI 803  Introduction to Artificial Intelligence and 

  Machine Learning               3 C

EAI 804  Mathematical Methods with MATLAB & 

               Python  for  AI                 3 C

EAI 805  Embedded AI Hardware       3 C

EAI 806 Robotics and Automation       3 C

EAI 807  Entrepreneurship                3 C

EAI 808 Design Thinking, Research & Seminar 3  C

Total Units 24

2nd SEMESTER COURSES                     Credit Units     Status

Elective Courses (Students are to choose 6 elective courses) 

EAI 811  AI Ethics, Privacy and Security                                 3 E

EAI 813  Embedded Computer Vision                                 3 E

EAI 814  Internet of Things and Cloud Integration                 3 E

EAI 816  Pathology Lab Services in Africa                         3 E

EAI 817  Imaging for Cardiovascular & 

               Stroke Phenotypes of SCD                                 3 E

EAI 818  Knowledge based systems and Expert systems                  3 E

EAI 819  Speech and Language Technologies                         3 E

EAI 820  Wireless and Mobile Systems Technologies                 3 E

Total Units  =  18

3rd  SEMESTER COURSES Credit Units           Status

EAI 809  MSc Dissertation 6 C

Total Units  =  6

Course Contents

EAI 801:   Fundamentals of Embedded System Design – 3 units, Core

Overview of embedded AI Systems, applications, technologies and their industrial implementations and significance. Smart technologies in Medicine, Agriculture, Oil & Gas, Energy, Electricity, Power systems, Telecommunications, Finance, security, government etc. Introduction to embedded system, components, characteristics, applications. 

• Hardware – I/O, memory, busses, devices, control logic, interfacing hardware to software. 

• Software – C and assembly programming, device drivers, low level real-time issues, scheduling, Concurrency, interrupts 

• Software/Hardware interactions: functionality (hardware or software) optimization, performance, memory requirements (RAM and/or FLASH ROM). Programming, logic design, architecture, Algorithms, and simple sense Expert Systems, scheduling, resource handling, Design and analysis of real time system software, Modelling and verification of real time systems, Interrupts, Distributed real time systems, Real time communication, Real time systems for multiprocessor systems.

 EAI 802:  Embedded Artificial Intelligence Control – 3 units, Core

The course content will include, modelling continuous, discrete, and hybrid systems, the composition of state machines, concurrent models of computation, introduction to sensors and actuators, embedded processors, memory architectures, input and output, scheduling and multitasking, invariant and temporal logic, equivalence and state refinement, reachability analysis and model checking, quantitative analysis, security in embedded devices. 

EAI 803: Introduction to Artificial Intelligence and Machine Learning - 3 Units, Core

Basic concepts of Artificial Intelligence, artificial intelligence in control applications, machine intelligence, machine learning

Introduction to artificial neural network, adaline, madaline, BAM, Hopfield memory, back propagation, counter-propagation networks, self-organising maps, adaptive resonance theory, fuzzy logic and evolutionary computing in engineering applications

Supervised Learning basics: nearest neighbors, decision trees, linear classifiers, and simple Bayesian classifiers; feature processing and selection; avoiding over-fitting; experimental evaluation. Unsupervised learning: clustering algorithms; generative probabilistic models; the More supervised learning: neural networks; back-propagation; dual perceptron; kernel methods; support vector machines; machine learning applications in classification, fraud detection, anomaly detection, economic performance prediction, deep learning for vision and object identification, etc.

EAI 804: Mathematical Methods with MATLAB & Python for Artificial Intelligence – 3 units, Core

Single and Multi-Variable Calculus:  Definition of Functions, Concept of Gradient of function, Derivatives and Integration of functions, common rules of differentiation, chain rule, partial derivatives, directional derivative and application of differentiation, local/global maxima and minima, saddle point, convex functions. Application of Integration. Differential Equations.
Linear Algebra: Vectors, definition, scalars, addition, scalar multiplication, inner product (dot product), vector projection, cosine similarity, orthogonal vectors, normal and orthonormal vectors, vector norm, vector space, linear combination, linear span, linear independence, basis vectors. Matrices: Definition, addition, transpose, scalar multiplication, matrix multiplication, matrix multiplication properties, Hadamard product, functions, linear transformation, determinant, identity matrix, invertible matrix and inverse, rank, trace, popular type of matrices- symmetric, diagonal, orthogonal, orthonormal, positive definite matrix. Eigenvalues & eigenvectors:  Principal component analysis concept, properties, applications. Singular value decomposition concept, properties, applications.Mathematical Modeling: Methodology of mathematical model formulation and design; identifications, formulation and solution of problems cause effect diagrams.  Equation types Algebraic, ordinary and partial differential, difference, integral and functional equations. Applications of Mathematical models to plural, biological, social and behavioural sciences. 
Probability and statistics: Basic rules and axioms. events, sample space, frequentist approach, dependent and independent events, conditional probability. Random variables- continuous and discrete, expectation, variance, distributions- joint and conditional. Bayes’ Theorem, MAP, MLE. Probability Distributions- binomial, Bernoulli, poisson, exponential, Gaussian. Information theory- entropy, cross-entropy, KL divergence, mutual information. Markov Chain- definition, transition matrix, stationarity. Introduction to MATLAB/ SIMULINK.  Solving engineering mathematics with MATLAB.

COMPUTER HANDS-ON:   MATLAB/ SIMULINK & Python Applications in Mathematical Methods.

EAI 805:  Embedded AI Hardware - 3 Units, Core

Introduction to Embedded Artificial Intelligence Hardware:The architecture of Embedded Systems and their Internal Communications Channels.Definition and characteristics of embedded systems AI hardware.Overview of hardware platforms for embedded AI.Comparison of embedded AI hardware platforms, including performance, power consumption, and cost. Hardware Components for Embedded AI:Processing element architectures and selection criteria for embedded AI, Memory types and organisation for embedded AI, Sensors and interfaces for embedded AI, including cameras, microphones, and accelerometers, Power management for embedded AI systems.Embedded AI Hardware Platforms: Overview of popular embedded AI hardware platforms, including Xilinx ACAP architecture.Hardware architecture and features of embedded AI platforms. Configuration and setup of embedded AI hardware platforms for development and deployment, Xilinx Vitis AI toolchain, and Apache TVM. Programming Embedded AI Hardware: Introduction to programming languages for embedded AI, including C++, Python: Basic programming concepts for embedded AI, including data types, control structures, and functions. Interfacing with sensors and other hardware components using programming languages and APIs .Optimizing Embedded AI Hardware Performance :Parallel processing for embedded AI, including threading and SIMD instructions. Hardware acceleration for embedded AI, including GPUs and TPUs. Techniques for minimising power consumption and maximising performance in embedded AI hardware. Integration of Hardware and Software for Embedded AI :Trade-offs between hardware and software in embedded AI development. Resource allocation and latency considerations in embedded AI systems.Case studies of embedded AI applications in robotics, computer vision, and other domains.

LAB : Programming and problem solving with Python, C++.  AI Hardware practicals.

EAI 806: Robotics and Automation – 3 Units, Core

Robot classification and manipulation. Technology and history of development of robots. Applications. Direct and inverse kinematics: arm equation. Workspace analysis and trajectory planning. Differential motion and statics. Manipulator dynamics. End-of arm tooling. Automation sensors. Robot vision. Work-cell support systems. Robot and system integration. Safety. Human interface. Robot control system. Circuit and system configuration. Task oriented control. Robot control programming. Fuzzy logic and AI based robot control. Fundamentals of automation. Strategies and economic consideration. Integration of systems. Impact to the production factory. Evaluation of conventional processes. Analysis of automated flow lines. Assembly systems and line balancing. Automated assembly systems.. Robot applications. Automated materials handling and storage systems. Automation in inspection and testing. Linear feedback control system. Optimal control. Computer process control. Computer integrated manufacturing systems. Future automated factory.

EAI 807: Entrepreneurship – 3 units, Core

Introduction to entrepreneurship, definition of an entrepreneurship, entrepreneurship forms and activity, entrepreneurial traits, entrepreneurship as a process, innovation and business, critical thinking, soft skills, entrepreneurship policy, Business environment , Environmental assessment, Business forecasting methods and analytical tools, Macro Environmental Analysis, Meso-environment analysis , Micro environmental analysis, Market research, Final modelling for appropriate action, Business plan Components, financial statement and business section of the plan, Ownership and Responsibilities , Write a Business plan, Business models , Strategies in the business models ,Strategy levels , process of strategic management, Hierarchy and relationships in business (vision, strategy, tactics and values ) , Components of the process, Strategy formulation, implementation, Business models, Business model patterns Strategies for cooperation, Types of cooperation, Exit strategies, Functions of management, Decision making and decision-taking, Roles and Regulations, legal form and current business stage, Sole proprietorship, Partnerships, Corporations, Keys for success Marketing environment, research, ideas segmentation, mix, strategy concept; Marketing objectives, plan, strategy.  Advantages and disadvantages of different firms, Challenges in the different business, succession plan, Sources of conflicts and exit, How to Start, Run, and Expand, Finance type (investment, loans, grants, etc.), assets evaluation, liabilities and equity structure, The balance sheet statement, Income statement components, Innovation, Innovation process, protection of innovation, innovation management.

EAI 808: Design Thinking, Research Methodology and Theory of Science – 3 Units, Core

Theory of Science: Introduction to Theory of Science: Definition, Scope, and Importance; Philosophy of Science: Positivism, Interpretivism, Realism, and Constructivism; Scientific Method: Induction, Deduction, and Abduction

Hypothesis Testing: Null Hypothesis and Alternative Hypothesis; Theory Building: Inductive and Deductive Approaches; Epistemology: Justification, Truth, and Knowledge ;Critique of Theory of Science: Limitations, Challenges, and Future Directions.

Research Methodology: Introduction to Research: Definition, Types, and Characteristics; Research Design: Experimental, Non-Experimental, and Quasi-Experimental; Sampling Techniques: Probability Sampling and Non-Probability Sampling; Data Collection Methods: Surveys, Interviews, Focus Groups, Observations, and Secondary Data

Data Analysis Techniques: Descriptive Statistics, Inferential Statistics, and Qualitative Analysis; Validity and Reliability: Ensuring the Accuracy and Consistency of Research Findings; Ethical Considerations in Research: Informed Consent, Confidentiality, and Privacy

Design Thinking  :Introduction to Design Thinking: Definition, Principles, and Benefits, Design Thinking Process: Empathy, Define, Ideate, Prototype, and Test; Tools and Techniques for Design Thinking: Mind Mapping, Brainstorming, User Journey Mapping, Storyboarding, etc. Human-Centered Design: User Research, User Persona, User Testing; Business Model Canvas and Value Proposition Canvas: Designing and Validating Business Ideas; Design Thinking in Practice: Case Studies, Best Practices, and Real-World Examples; Critique of Design Thinking: Limitations, Challenges, and Future Directions.

Project/ Seminar Write-up format. 

EAI 809: MSc Dissertation – 6 Units, Core

Innovative and problem-solving dissertation work. Should be aimed at developing Embedded AI Solutions in relevant fields of Agriculture, Medicine, oil & Gas, Energy, Power, etc.

EAI 811: AI Ethics, Privacy and Security – 3 Unita, Core

This course divides into three parts.  One, on the ethics of AI, is about ethical and philosophical problems raised by AI—such as the alignment problem, the technological singularity, the attribution of responsibility for autonomous agents.  The second, on fairness and bias in ML, will concern conceptions of algorithmic fairness and bias, the accuracy/bias trade-off, and practical approaches to these.  The third, on law, will present the GDPR and its impact on AI that involves personal data, as a key illustrative example of an important law affecting AI/ML; related laws and regulation may be presented.

EAI 813: Embedded Computer Vision – 3 Units, Core

Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images, videos, point clouds etc.) in the same way that humans do. The human visual system is great at interpreting visual content and a decade ago the general idea was that a computer would never beat a human doing this. Modern computer vision (CV) is built on the deep learning revolution where machines learn to interpret visual content from data and, today, we see super-human performance within many fields. CV has become one of the hottest research fields out there and is the key enabling technology for everything from self-driving cars to automated medical image analysis. The focus of this course will be modern CV while still giving you a small glimpse of how things looked like prior to the introduction of the famous AlexNet in 2012, as well as some CV-related tasks where more traditional methods still outperform the new data driven methods. 

Course Contents

• This module will introduce the concept of computer vision and how it can be used to solve problems. It will cover how digital images are created and stored on a computer. Demonstration of how neural networks can be used for classification of simple images.  A task to train an image classifier and deploy it to an embedded system will be undertaken.

• In this module, convolutional neural networks (CNNs) will be used to create a robust image classification model. The concept of data augmentation to help provide more data to the training process of the CNN will be covered. A task to train a CNN on image classification and deploy it to an embedded system will be undertaken.

• In this module, the basics of object detection and how it differs from image classification will be covered. Object detection performance measurement. Introduction to object detection models. Demonstration of the process required to train such a models in Edge Impulse and deployment on an embedded system.

EAI 814: Internet of Things and Cloud Integration – 3 Units, Core

• Introduction to the IoT: Wire-line, ad hoc and sensor networks, PANs, LANs, WANs
Node and network technology, applications and links to enabling technologies (e.g. DSP, machine learning).
•  IoT Platforms Hardware and Software: Transceivers, microcontrollers, interfacing, operating systems and APIs, examples.
•  Networking for IoT: Revision of basic networks, wired vs wireless, protocols, state machines, infrastructure vs ad hoc, channel access, reliable transmission, addressing, routing, congestion
•  Protocol stack for energy-constrained nodes and ad hoc networks: Wireless Physical Layer, Medium Access Control, Routing Protocols, Transport Protocols and Application Layer Protocols
•  IoT Security: Basic IoT security requirements, introduction to cryptography, securing different stack layers (e.g. LANs, network, and transport).
•  Network performance, modelling and simulation: Performance metrics, review of Markov chains, simple queuing theory and application to protocols, discrete event simulation.

EAI 816: Pathology Lab Services in Africa and   Molecular diagnostic pathology - 3 units,  Elective

Basic concepts of molecular pathology & clinical diagnostics including nucleic acids, chromosomes, DNA replication, transcription, proteins, mutations & chromosome changes that underlie inherited & acquired diseases. Haematopathology service, Turn Around Time, principles of DNA extraction procedures and digital pathology.  Standard molecular diagnostic techniques and cutting-edge technologies and their applications to clinical pathology practice; flow cytometry and immunocytochemistry, etc. Introduction to the basics of real time PCR, Sanger sequencing, restriction fragment length polymorphism (RFLP), Isoelectric focusing (IEF), capillary electrophoresis (CE), SNPs (single nucleotide polymorphisms), gene editing (CRISPR). Basics of bioinformatics and application in diagnostic pathology. Artificial intelligence (AI) in surgical pathology, AI in prognosis and treatment of cancers, telepathology, precision medicine. Concept of "augmented intelligence".

EAI 817: Imaging for Cardiovascular & Stroke Phenotypes of SCD - 3 units, Elective

Sickle cell disease, genetics, epidemiology, diagnosis, pathophysiology, clinical presentation, age related complications including cardiovascular and stroke phenotypes. Health maintenance and prevention, comprehensive care.  Surveillance of complications with the use of transcranial doppler ultrasound screening, ECG echocardiographic findings in patients with sickle cell disease. The Sickle Pan African Research Consortium SCD Registry in Nigeria (Africa), Big data, use of AI in clinical practice to monitor disease progression and response to treatment.

EAI 818: Knowledge based systems and Expert systems - 3 units, Elective

Intelligent machines and expert systems, symbolic representation of knowledge, symbolic processing with prolog, data dependency as an alternative paradigm to imperative programming, rule based problem solver and knowledge representation techniques implemented in Prolog, Heuristic search and inference, data driven reasoning, goal-driven reasoning, domain-specific search, Application of Expert systems to engineering problems (process control systems) e.g. electricity utility systems, distillation columns etc

Learning outcome with hands-on experience

• Developing of computer based- expert systems 

• Real life application of expert system in solving basic engineering problems

EAI 819:  Speech and Language Technologies - 3 units, Elective

Speech synthesis, Speech recognition, Language recognition. Speech synthesis, allophones, dictionary, morphology and rule-based letter to sound approaches, stress and intonation. Automatic translation, summarizers, spell checkers, grammar checkers. Mobile speech and language applications development.

List of Academic Staff