Tissue Engineering
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Prof. S. Swaminathan
This course helps the learners to understand thoroughly the key concepts of tissue organization, remodeling and strategies for restoration of tissue function. This will enable them to design tissue regeneration and tissue injury repair strategies. |
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Technical English
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Syllabus
This course aims to train the first year BE/B tech students in basic principles of English language, enabling them to use active and passive vocabulary in different academic and professional contexts, developing their LSRW skills, namely listening, speaking, reading and writing skills thereby improving their proficiency in oral and written communication in technical English. |
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Physics and Applications of Semiconductor Nanostructures (Semiconductor Nanodevices)
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Prof. R. John Bosco Balaguru Dr. B.G. Jeyaprakash
The course will focus on Physics of low dimensional semiconducting materials and their electrical, optical, thermal and mechanical properties, Engineering the semiconducting nanomaterials for nanodevice development and their applications to the budding Science & Engineering students.
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Systems Biology
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Prof. M. Vijayalakshmi
Systems Biology- Fundamentals- gene expression paradigms - genetic switch in Lambda Phage -Noise-based Switches and Amplifiers for Gene Expression -Ecoli chemotaxis - genetic oscillators - Noise in Biochemical Systems-Quorum Sensing - Programmed Population Control by Cell-Cell Communication and Regulated Killing- Drosophila Development - Establishment of Developmental Precision and Proportions in the Early Drosophila embryo -Gene expression networks -Gene regulation at a single cell level-Transcription Networks basic concepts |
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Pattern Recognition
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Prof. K. Vijayarekha
This course gives the importance and usefulness of pattern recognition in modern world. Images can be classified based on their patterns. Clustering which helps in differentiating groups of data is included in this course. Decision functions will be dealt in with. Pattern recognition with the help of the membership of incoming functions will be taught. The extractable measure of an image called features which help us distinguish one image from the other will be studied. Feature extraction and feature selection will also form a part of this course. As application of pattern recognition, Pattern recognition using Fuzzy logic and neural network will be used. |
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