CCMAS Course Search

Limits, continuity, differentiation, introduction to linear first order differential equations, partial and total derivatives, composite functions, matrices and determinants, vector algebra, vector calculus, directional derivatives.

Introduction to ordinary differential equations (ODEs); theory, applications, methods of solution; second order differential equations. Advanced topics in calculus (vectors and vector- valued function, line integral, multiple integral and their applications). Elementary complex analysis including functions of complex variables, limits and continuity.

, that is, possess a breadth of knowledge in the area covered; 2. possess an in-depth knowledge upon which a solid foundation can be built in order to demonstrate a depth of understanding in advanced mathematical topics; 3. develop simple algorithms and use computational proficiency; 4. write simple proofs for theorems and their applications; and 5. communicate the acquired mathematical knowledge effectively in speech, writing and collaborative groups. Course Contents Linear Algebra. Elements of Matrices, Determinants, Inverses of Matrices. Theory of Linear Equations. Eigen Values and Eigen Vectors. Analytical Geometry. Coordinate Transformation. Solid Geometry. Polar, cylindrical and spherical coordinates. Elements of functions of several variables. Surface Variables. Ordinary Integrals. Evaluation of Double Integrals, Triple Integrals, Line Integrals and Surface Integrals. Derivation and Integrals of Vectors. The gradient of scalar quantities. Flux of Vectors. The curl of a vector field, Gauss, Greens and Stoke’s theorems and applications. Singular Valued Functions. Multivalued Functions. Analytical Functions. Cauchy Riemann’s Equations. Singularities and Zeroes. Contour Integration including the use of Cauchy’s Integral Theorems. Bilinear transformation.

Series solution of second order linear differential equations with variable coefficients. Bessel and Legendre equations. Equations with variable coefficients. Sturm-Liouville boundary value problems. Solutions of equations in two and three dimensions by separation of variables. Eigen value problems. Use of operations in the solution of partial differential equations and Linear integral equations. Integral transforms and their inverse including Fourier, Laplace, Mellin and Handel Transforms. Convolution integrals and Hilbert Transforms. Calculus of finite differences. Interpolation formulae. Finite difference equations. RungeKutta and other methods in the solutions of ODE and PDEs. Numerical integration and differentiation.

Project management fundamentals – definitions, project environment, nature and characteristics, development practice, management by objectives, and the centrality of engineering to projects, infrastructures, national and global development. The scope of project management – organisational, financial, planning and control, personnel management, resource management. Identification of project stakeholders, beneficiaries and impacted persons – functions, roles, responsibilities. Project community relations, communication and change management. Project planning, control and timeliness – decision making, forecasting, scheduling, work breakdown structure (WBS), deliverables and timelines, logical frameworks (log frames), risk analysis, role of subject matter experts (SMEs), role conflicts; Gantt Chart, CPM and PERT. Optimization, linear programming as an aid to decision making, transport and materials handling. Monitoring and Evaluation – key performance indices (KPIs), methods of economic and technical evaluation. Industrial psychology, ergonomics/human factors and environmental impact considerations in engineering project design and management. Project business case - financial, technical and sustainability considerations. Case studies, site visits and invited industry professional seminars.

Descriptive statistics, frequency distribution, populations and sample, central tendency, variance data sampling, mean, median, mode, mean deviation, percentiles, etc. Probability. Binomial, poison hyper-geometric, normal distributions, etc. Statistical inference intervals, test hypothesis and significance. Regression and correlation. Introduction to big data analytics and cloud computing applications. Introduction to the R language; R as a calculator; Vectors, matrices, factors, data frames and other R collections. Iteration and looping control structures. Conditionals and other controls. Designing, using and extending functions. The Apply Family. Statistical modelling and inference in R. GET 307: Introduction to Artificial Intelligence, Machine Learning and Convergent Technologies (3 Units C: LH 45) Learning Outcomes At the completion of the course, the students are expected to be able: 1. explain the meaning, purpose, scope, stages, applications and effects of artificial intelligence; 2. explain the fundamental concepts of machine learning, deep learning and convergent technologies; 3. demonstrate the difference between supervised, semi-supervised and unsupervised learning; 4. demonstrate proficiency in machine learning workflow and how to implement the steps effectively; 5. explain natural languages, knowledge representation, expert systems and pattern recognition; 6. describe distributed systems, data and information security and intelligent web technologies; 7. explain the concept of big data analytics, purpose of studying it, issues that can arise with a data set and the importance of properly preparing data prior to a machine learning exercise; and 8. explain the concepts, characteristics, models and benefits, key security and compliance challenges of cloud computing. Course Contents Concepts of human and artificial intelligence; artificial/computational intelligence paradigms; search, logic and learning algorithms. Machine learning and nature-inspired algorithms – examples, their variants and applications to solving engineering problems; understanding natural languages; knowledge representation, knowledge elicitation, mathematical and logic foundations of AI; expert systems, automated reasoning and pattern recognition; distributed systems; data and information security; intelligent web technologies; convergent technologies – definition, significance and engineering applications. Neural networks and deep learning. Introduction to python AI libraries.

The concept of entrepreneurship (entrepreneurship, intrapreneurship/corporate entrepreneurship); theories, rationale and relevance of entrepreneurship (Schumpeterian and other perspectives, risk-taking, necessity and opportunity-based entrepreneurship, and creative destruction); characteristics of entrepreneurs (opportunity seeker, risk-taker, natural and nurtured, problem solver and change agent, innovator and creative thinker); entrepreneurial thinking (critical thinking, reflective thinking and creative thinking). Innovation (The concept of innovation, dimensions of innovation, change and innovation, knowledge and innovation). Enterprise formation, partnership and networking (basics of business plan, forms of business ownership, business registration and alliance formation, and joint ventures). Contemporary entrepreneurship issues (knowledge, skills and technology, intellectual property, virtual office and networking). Entrepreneurship in Nigeria (biography of inspirational entrepreneurs, youth and women entrepreneurship, entrepreneurship support institutions, youth enterprise networks and environmental and cultural barriers to entrepreneurship). Basic principles of e-commerce

A problem, limited and well defined in scope and adapted to the topics of this undergraduate programme, is to be solved by the student on his own responsibility in a definite period of time, under the supervision of an instructor. This provides the opportunity to apply engineering knowledge and methods in a practical way, e.g. measurements in connection with calculations, programming applications. The problem selected should have interdisciplinary aspects. It is recommended that the project work be carried out in an industrial organization or research institute, with the participation and supervision of the university. If nuclear facilities are available, the project work could be performed there. The results should be presented as a written report. One project per student. Minimum Academic Standards Equipment Radiation protection and measurement Radiation Detector (PRD); Gamma Neutron Type Dossimeter; electronic type (EPD) Radioisotope identifier; handheld type Survey meters; general purpose type Radiation portal monitor; pedestrian and luggage type Nuclear Security Laboratory Model 701 series isotope identifier Meters and scanners Physical protection systems analytical tool Nuclear power plant simulation Laboratory Three-key master generic PWR simulation system Dinco software Prism Software MATLAB Staffing Academic Staff The NUC guidelines on staff/student ratio of 1:15 for Engineering and Technology departments shall apply. However, there should be a minimum of six full-time equivalents of Staff in the department. There is need to have a reasonable number of Staff with doctoral degrees as well as sufficient industrial experience. With a minimum load of 15 Units per semester for students and a minimum of six full-time equivalent of staff in each programme, staff should have a maximum of 15 contact hours per week for lectures, tutorials, practical’s and supervision of projects. NUC requirement encourages all academic staff to have PhD degrees; hence appointment of academic staff is preferably to the Lecturer cadre. Only in exceptional cases are candidates with great promise appointed to Graduate Assistant and Assistant Lecturer positions for the purpose of being developed to the Lecturer cadre as registered PhD candidates. Academic Support Personnel Teaching Assistant/Demonstrators to help lecturers in the conduct of tutorials, practical’s and field work. This category of personnel is not expected to be regular staff as they are to be paid on the basis of approved hourly rate. Administrative Support Staff The services of the administrative support staff are indispensable in the proper administration of the departments and faculty offices. It is important to recruit very competent senior staff that are computer literate. Technical Support Personnel The services of technical support staff, which are indispensable in the proper running of laboratories and workshop/studios are required. It is important to recruit very competent senior technical staff to maintain teaching and research equipment. They are also to undergo regular training to keep them abreast of developments in equipment operation and maintenance. The minimum of academic staff to technical staff ratio of 5:1 should be maintained. Minimum Number of Staff Subject to the general standards specified by NUC: 1. there should be a minimum of two PhDs and four M.Eng degree holders full-time academic staff to mount the programme; 2. each workshop or laboratory should have an adequate number of staff with the right mix, such that each unit or section in that workshop or laboratory can run efficiently; and 3. there should be an adequate number of administrative staff of the appropriate caliber for the office of the Head of Department to run. Student/Staff Ratio The minimum staff-to-student ratio should be 1:15 from 200 level to 500 level. Library In addition to the university and faculty libraries, the programme must have a departmental library well equipped with specialised books and journals in both physical collections and e- collections (E-Resources) of various types. Various field and research reports of the programme must also be available in the library for staff, students and researchers. The library must be connected to subscribed repository of: institutions (national and international); open access sources; professional bodies’ e-learning platforms, and relevant international organizations. The library must also have adequate facilities. for reading; provisions for lending, and reservation unit for specialised materials. Classrooms, Laboratory, Workshops, Offices and Clinics Academic and Non-Academic Spaces The NUC recommends the following physical space requirement: Academic m2 Professor’s Office 18.50 Head of Department’s Office 18.50 Tutorial Teaching Staff Space 13.50 Other Teaching Staff Space 7.00 Technical Staff Space 7.00 Science Staff Research Laboratory 16.50 Engineering Staff Research Laboratory 14.50 Seminar Space per student 1.85 Drawing Office Space (A.O. Board) (Per Student) 4.60 Drawing Office Space (A.I. Board) (Per Student) 3.70 Laboratory Space 7.50 Non-Academic Secretarial Space 7.00 Office Accommodation The requirements for office accommodation are: 1. 13 academic offices. 2. 1 professorial type in the department. Size: each of the office is about 13.5 m S/No Office No in Room Facilities 1. HOD 1 Table, chairs, A/C, filing cabinet, bookshelves, computer unit, Secretary and facilities. 2. Professor 1 Table, chairs, A/C, filing cabinet, bookshelves, computer unit, Secretary and facilities. 3. Reader 1 Table, chairs, A/C, filing cabinet, bookshelves, computer unit. 4. Senior 1 Table, chairs, A/C, filing cabinet, bookshelves, Lecturer computer unit. 5. Lecturer I 2 Table, chairs, fan, filing cabinet, bookshelves. 6. Lecturer II 3 Table, chairs, fan, filing cabinet, bookshelves.

Basic concepts, definitions and laws (quantitative relations of Zeroth, first, second and third laws of thermodynamics). Properties of pure substances: the two-property rule (P-V-T behaviour of pure substances and perfect gases); state diagrams. The principle of corresponding state; compressibility relations; reduced pressure; reduced volume; temperature; pseudo-critical constants. The ideal gas: specific heat, polytropic processes. Ideal gas cycles; Carnot; thermodynamic cycles, turbines, steam and gas, refrigeration. The first law of thermodynamics – heat and work, applications to open and closed systems. The steady flow energy equation (Bernoulli’s equation) and application. Second law of thermodynamics, heat cycles and efficiencies.

Fluid properties, hydrostatics - Centre of pressure, buoyancy, stability of floating bodies, fluid dynamics using principles of mass, momentum, and energy conservation from a control volume approach and applications. Flow Measurements in pipes, Dimensional analysis, and Similitude, Hydropower systems. Turbomachinery - types, performance prediction, sizing.