Page 34 - M.E. Medical Electronics Curriculum and Syllabus - R2019
P. 34
Department of BME, REC
Types of biomedical waste -Infectious waste, Genotoxic waste, Waste Sharps, Liquid
Biomedical Waste, Radioactive wastes, Metals, Chemicals & drugs.
UNIT V TREATMENT OF BIOMEDICAL WASTE 9
Autoclave, Hydroclave, Microwave, Chemical Disinfection, Solidification and stabilization,
Bioremediation, Thermal Conversion Technologies, accumulation and storage of hazardous
waste, land disposal of hazardous waste, other treatment and disposal method. Common
Hazardous Waste Treatment facilities (TSDF)
TOTAL: 45 PERIODS
OUTCOMES:
On completion of the course the students will be able to
• To develop quality management system in the working environment.
• To implement electrical safety codes and standards in the working environment.
• To apply safety measures while working with radiological equipment.
• To categorize the biomedical wastes.
• To apply different methods to dispose biomedical wastes.
REFERENCES:
1. Bertil Jacobson and Alan Murray, ―Medical Devices use and safety, Reed Elsevier
India Pvt. Ltd, New Delhi, 2001.
2. Steve Webb, ―The Physics of Medical Imaging, Taylor & Francis, New York, 1988.
3. G.D.Kunder, S.Gopinath, A.Katakam, ―Hospital Planning, Design and
Management,Tata McgrawHil publishers, New Delhi, 1998.
4. Tchobanoglous G., Theisen H., Viquel S.A., “Integrated Solid Waste Management:
Engineering, Principles and Management issues”, Tata McGraw Hill Publishing
Company Ltd., New Delhi.
5. V. J. Landrum, Medical Waste Management and disposal, Elsevier, 1991, ISBN: 978-
0-8155-1264-6.
6. https://www.who.int/water_sanitation_health/medicalwaste/en/guidancemanual1.pdf
PROFESSIONAL ELECTIVE III
MX19P25 ADVANCED SOFT COMPUTING L T P C
3 0 0 3
OBJECTIVES
• To learn the basics of artificial intelligence.
• To learn the theory and implementation of neural networks
• To introduce neural computing as an alternative knowledge
acquisition/representation paradigm
• To explain its basic principles and their relationship to neurobiological models
• To introduce fuzzy logic.
UNIT I INTRODUCTION TO ARTIFICIAL INTELLIGENCE 9
Definition, Motivation for computer assisted decision making, Knowledge representation-
Production rules, Frames, Predicate calculus and Semantic nets, Knowledge acquisition,
Reasoning methodologies- Problem representation, Search, Dempster-shafer theory,
Evaluation.
R 2019 - Curriculum and Syllabus/ M.E Medical Electronics Page 34

