Courses with Gaps

Bioclimatology, agro climatology, climatology and the built environment, climate change and its impact on rural and urban environments, climate change and sustainable development goals, , and climate change and its implications on humans and various human and economic activities. Definition and delimitation of the “Tropics” Rationale for studying the climatology of the tropics. Radiation conditions in the tropics. Temperatures in the tropics. Tropical precipitation. Tropical disturbances: Tropical weather systems. Applied tropical climatology: Tropical agro climatology; tropical bioclimatology, global circulation system and its influence in the tropics., links between the tropical climates and the temperate climates, climate change and climate-premiership.

Workshop - safety precautions; Basic hand tools and bench work practice; Plain and cylindrical generation of smooth surface using power operated machines; Selection and properties of materials used for construction - metallic and non-metallic; Metal joining devices and adhesives in common use; Soldering techniques and wrap joints; Multi-meters and oscilloscopes; Auto-ranging in measuring instruments; A survey of the use of electronics circuit devices e.g.; diodes; transistors including FET; integrated circuits; photocells; Selection; use and care of test instruments; Survey of pick-ups and transducer devices; Basic circuit synthesis and analysis; Pulse circuits; Instrumentation and measuring techniques: impedance matching; Probes - active and input and output impedance using the scope

Introductory chemistry of amino acids; their properties, reactions and biological functions. Classification of amino acids: neutral, basic and acidic; polar and non-polar; essential and non- essential amino acids. Peptides. Introductory chemistry and classification of proteins. Biological functions of proteins. Methods of their isolation, purification and identification. Primary, secondary, tertiary and quaternary structures of proteins. Basic principles of tests for proteins and amino acids. Introductory chemistry of carbohydrates, lipids and nucleic acids. Nomenclature of nucleosides, and nucleotides; effects of acid and alkali on hydrolysis of nucleic acids.

Introductory chemistry of amino acids, their properties, reactions and biological functions. Classification of amino acids: neutral, basic and acidic; polar and non-polar; essential and non- essential amino acids. Peptides. Introductory chemistry and classification of proteins. Biological functions of proteins. Methods of their isolation, purification and identification. Primary, secondary, tertiary and quaternary structures of proteins. Basic principles of tests for proteins and amino acids. Introductory chemistry of carbohydrates, lipids and nucleic acids. Nomenclature of nucleosides and nucleotides, effects of acid and alkali on hydrolysis of nucleic acids.

Introduction to biometrics and digital image processing. Matlab in biometric image/signal processing. Biometric algorithms and systems with emphasis on face, fingerprint, eyes (iris), speech (voice). Automated biometric identification multimodal biometrics. Biometric data: raw data, template data, and data methods. Biometric matching basics: biometric authentication, enrolment, correct user, and incorrect user. Match threshold and matching performance. Setting a threshold. Biometric authentication: matching data, ground truth, calculating errors rates and graphs. Biometric data: Storage of biometric data elements, transactions, errors and quality upgrades. Data security and integrity. Privacy issues and other aspects of biometrics. Applications of biometrics and future trends. Challenging issues: security strength and recognition rates. Alternatives of passwords and smart cards. Lab work: Practical exercise on biometric capture, image processing, matching threshold and performance. Learn the practical aspect of automated biometric identification of multimodal, authentication and calculation of error rates. Work on biometric algorithms, privacy and security of stored biometric data.

The course is planned to equip the undergraduates in all the disciplines of health sciences with the necessary tools and skills for collecting, analysing, interpreting data quantitatively. Topics to be covered include: The central role of statistics in health sciences disciplines, data description, elements of probability. Description of random variables. Applications of the binomial and normal distributions. Estimation and confidence intervals. Contingency tables. Regression and variance analysis. Study design and hypothesis testing for practical purposes. Students are provided with specific data to work on and are also required to develop simple questionnaire protocols for analysis.

Computer vision and image processing are important and fast evolving areas of Mechatronics and Robotics. Student will get familiar with both established and emergent methods, algorithms and architectures. The course will enable students to apply computer vision and image processing techniques to solving various real-world mechatronics and robotics problems, and develop skills for research in the fields. Image formation, image filtering, edge detection and segmentation, morphological processing, registration, object recognition, object detection and tracking 3D vision. The topics may include but are not limited to: 1. Image formation and perception, image representation. 2. Image filtering: space- and frequency- domain filtering, linear and non-linear filters. 3. Morphological image processing. 4. Image geometric transformations, image registration. 5. Edge detection, image segmentation, active contours, and level set methods. 6. Object recognition, template matching, and classification. 7. Object detection and tracking: background modeling, kernel-based tracking, particle filters. 8. Camera models, stereo vision.

Introduction to control system: Concept of feedback and Automatic control, Effects of feedback, Objectives of control system, Definition of linear and nonlinear systems, Elementary concepts of sensitivity and robustness. Types of control systems, Servomechanisms and regulators, examples of feedback control systems. Transfer function concept. Pole and Zeroes of a transfer function. Properties of Transfer function. Mathematical modelling of dynamic systems: Translational systems, Rotational systems, Mechanical coupling, Liquid level systems, Electrical analogy of Spring– MassDashpot system. Block diagram representation of control systems. Block diagram algebra. Signal flow graph. Mason’s gain formula. Control system components: Potentiometer, Synchros, Resolvers, Position encoders. DC and AC tachogenerators. Actuators. Block diagram level description of feedback control systems for position control, speed control of DC motors, temperature control, liquid level control, voltage control of an Alternator. Time domain analysis: Time domain analysis of a standard second order closed loop system. Concept of undamped natural frequency, damping, overshoot, rise time and settling time. Dependence of time domain performance parameters on natural frequency and damping ratio. Step and Impulse response of first and second order systems. Effects of Pole and Zeros on transient response. Stability by pole location. Routh Hurwitz criteria and applications. Error Analysis: Steady state errors in control systems due to step, ramp and parabolic inputs. Concepts of system types and error constants. Stability Analysis: Root locus techniques, construction of Root Loci for simple systems. Effects of gain on the movement of Pole and Zeros. Frequency domain analysis of linear system: Bode plots, Polar plots, Nichol’s chart, Concept of resonance frequency of peak magnification. Nyquist criteria, measure of relative stability, phase and gain margin. Determination of margins in Bode plot. Nichol’s chart. circle and Contours in Nichols chart. Control System performance measures: Improvement of system performance through compensation. Lead, Lag and Lea lag compensation, PI, PD and PID control.

This course acquaints the student with in-depth knowledge of curriculum studies ranging from educational theories to philosophical, psychological, and sociological issues. Also, it is expected to expose the learner to theories and traditions and changes in curriculum. The course also aims at acquainting students with an in-depth knowledge of evaluation and assessment of the curriculum and the teacher accountability and control