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ICSE Class 9 Biology
ICSE Class 9 Biology introduces students to the fascinating world of living organisms through topics like Cell Structure, Plant and Animal Tissues, Human Anatomy, and Ecology. It helps students develop practical knowledge and a deeper understanding of biological processes, laying the foundation for more advanced studies in the field.
ICSE Class 9 Maths
ICSE Class 9 MathICSE Class 9 Maths introduces essential mathematical concepts like algebra, geometry, and trigonometry. It covers number systems, linear equations, and areas of polygons, helping students build a strong foundation in problem-solving and mathematical reasoning, preparing them for advanced studies in mathematics.s
ICSE Class 10 Biology
ICSE Class 10 Biology helps students explore the living world through topics like Human Evolution, Photosynthesis, and Plant Physiology. It builds curiosity about life processes and shows how our bodies and nature work together.
ICSE Class 11 Biology
ICSE Class 11 Biology explores the study of living organisms, covering topics such as cell structure, genetics, human physiology, plant biology, and ecology. It provides a strong foundation for understanding life sciences and prepares students for advanced studies in biology and medicine.
Manufacturing Process
"Manufacturing Processes" explores the fundamental methods and technologies used in the transformation of raw materials into finished products. The subject provides in-depth knowledge of various manufacturing techniques such as casting, forming, machining, joining, and advanced manufacturing (like additive manufacturing). It emphasizes material behavior, process selection, tool design, and quality control, equipping students with the theoretical and practical understanding required to optimize production systems in mechanical and industrial engineering domains
Design for Testability
This course focuses on designing electronic circuits with testability in mind. Students will explore strategies for enhancing testing efficiency during manufacturing and operation. Topics include built-in self-test (BIST), scan chains, fault modeling, and testability considerations. Through theory, hands-on projects, and industry tools, students will develop skills to create robust, testable electronic systems
AI circuits
This course explores the intersection of artificial intelligence (AI) and integrated circuit design, focusing on principles, techniques, and challenges in creating circuits for AI applications. Topics include neuromorphic computing, hardware accelerators, parallel processing architectures, and AI circuit implementation. Students will gain skills through theory, hands-on projects, and exposure to cutting-edge developments in AI circuit design.
MEMS
This course explores Microelectromechanical Systems (MEMS), a multidisciplinary field combining electrical, mechanical engineering, and material science. Students will study MEMS principles, design methodologies, fabrication techniques, sensing/actuation mechanisms, and modeling. Through theory, hands-on projects, and real-world applications, students will gain skills for developing MEMS devices
Low Power Circuit Designs
This course explores low-power circuit design, focusing on the integration of CMOS and FinFET technologies. Students will study principles, techniques, and challenges in designing energy-efficient circuits. Topics include low-power strategies, optimization, and practical considerations. Students will gain skills in designing circuits with minimal power consumption through theory, simulations, and hands-on projects
Mixed Signal Circuits
This course provides a comprehensive exploration of mixed signal circuits, focusing on integrating analog and digital components within a single system. Topics include ADC, DAC, signal processing, noise analysis, and the coexistence of analog and digital elements. Students will gain practical skills through simulations and hands-on projects.
Embedded Linux
This course teaches embedded Linux system development, focusing on configuring the Linux kernel and writing custom peripheral drivers. Students gain hands-on experience with Linux architecture, building embedded systems, and debugging and profiling applications. Ideal for aspiring embedded engineers, it addresses growing industry demand across consumer, automotive, and industrial applications.
System on Chip
System on Chip This course focuses on designing low-cost, efficient SoCs for IoT using Arm Cortex-M0 processors. Students explore the complete SoC development cycle—specification, design, implementation, and testing—on FPGAs. Emphasis is placed on meeting performance, power, and area constraints using standard hardware description and software programming languages for real-world prototyping.
Computer Architecture
Computer Architecture This course trains students to use tools like Icarus Verilog, GNU Toolchain, and GTKWave for labs. Students write Armv8-A AArch64 assembly, simulate using Arm Education Core, analyze instruction encoding, implement pipeline stages, resolve RAW hazards, handle control hazards, and estimate Power, Performance, and Area metrics effectively.
CAD for VLSI
This course explores CAD algorithms for VLSI circuit design, focusing on automation, optimization, and verification. Students learn logic synthesis, physical design, and verification techniques, gaining hands-on experience with industry tools. Emphasis is on algorithmic efficiency and solving complex design challenges using advanced CAD tools.
Mathematics III (PDE, Probability & Statistics)
This course equips engineering students with essential mathematical tools for modeling and analyzing complex physical systems. It is divided into three core modules: Partial Differential Equations (PDEs): Students learn to formulate and solve first and second-order PDEs, classify them, and apply methods such as D’Alembert’s solution, Duhamel’s principle, and separation of variables. Applications include heat conduction, wave propagation, and vibrational problems in mechanical and thermal systems. Probability Theory: This section introduces foundational concepts like random variables, probability distributions, expectation, and moments. It also covers key discrete and continuous distributions relevant to real-world data modeling. Statistics: Focused on data interpretation, students explore sampling, estimation, hypothesis testing, correlation, and regression analysis—crucial for engineering applications involving uncertainty and decision-making under variability. Together, these modules provide the analytical backbone needed for advanced subjects in fluid dynamics, thermodynamics, signal processing, and beyond.
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