AU-QUASAR at Alliance University
About AU-QUASAR
AU-QUASAR is Alliance University’s future-facing undergraduate initiative built around quantum computing, artificial intelligence, robotics and cyber-physical systems.
AU-QUASAR is Alliance University’s future-facing undergraduate initiative built around quantum computing, artificial intelligence, robotics and cyber-physical systems.
A four-phase progressive curriculum architecture with each phase corresponding to a distinct developmental stage in a student's journey from curious entrant to domain- expert graduate. The framework is designed so that each phase builds upon the previous, creating a coherent, longitudinal learning arc rather than a collection of disconnected courses.
Building foundations for scientific thinking and creator mindset right through semester 1 & 2
Hands-on with cutting-edge technology to explore the fields and understand the capacity through semester 3 & 4
Research pathways in deep tech and advanced systems leading up to product development through semester 5 & 6
Publishing the impact of research and innovation in specialised areas through semester 7 & 8
Curiosity → Scientific Thinking → Creator Mindset
Exploration → Engineering Capability Sampling → Domain Sampling
Deep Engineering → Research → Product Thinking
Mastery → Innovation → Translation to Impact
The program is built on a simple premise: quantum computing cannot be meaningfully understood or engineered through theory and simulation alone. It therefore combines rigorous training in mathematics, physics, and computation with direct interaction with multi-qubit quantum hardware, enabling students to engage with the actual behaviour and limitations of quantum systems.
As students progress, they move from foundational concepts to qubit architectures, quantum algorithms, and error-prone implementations, alongside continuous engagement in structured research. This integration of theory, systems, and experimentation develops the ability to work on real quantum problems rather than only studying established models.
The program is designed to address a central constraint in the field: the shortage of engineers trained to work with quantum systems under real conditions. By integrating theory, hardware access, and sustained research, it prepares students to understand and build quantum systems as they exist in practice.
| High-Impact Roles | Career Pathways | Academic Progression |
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The ǪT programme builds from universal mathematical foundations through hardware-layer engineering to full-stack quantum systems mastery. The credit load is uniformly 21- 24 credits per semester in Semesters 1–6, and 17 to 9 in Semesters 7–8 respectively to allow increasing depth of engagement per course at the frontier level. Total programme credits: 162.
| Semester | Course Title | Category |
|---|---|---|
| Semester 1 | AWAKEN | Calculus - I | BS |
| Physics – I (Electromagnetism and Optics) | BS | |
| Linear Algebra | BS | |
| Materials Science | BS | |
| Cognitive Science for Intelligent Systems | LASS | |
| Technical Communication & Knowledge Design Studio | LASS | |
| AURA Discovery Studio | PBL | |
| Maker Foundations Lab | PBL | |
| Semester 2 | AWAKEN | Calculus - II | BS |
| Probability and Statistics | BS | |
| Biology for Engineers | BS | |
| Physics – II (Quantum Mechanics and Semiconductor Physics) | BS | |
| Digital Storytelling | LASS | |
| Creativity, Imagination & Idea Generation | UWE | |
| Data Structures & Algorithmic Engineering | AE | |
| Semester 3 | UNCOVER | Electronic Devices for Intelligent Systems | AE |
| Signals, Systems & Information Processing | AE | |
| Design Thinking for Intelligent Systems | LASS | |
| Embedded Systems & Real-Time Computing | AE | |
| Microcontrollers, SoC & Edge AI Hardware | AE | |
| Technology, Society & the Future of Work | LASS | |
| Behavioural Science & Human Decision Making | UWE | |
| Semester 4 | UNCOVER | Mathematical Foundations for Quantum Systems | PC |
| Probability & Random Processes for Quantum Info. | PC | |
| Quantum Computing Fundamentals & Qubit Models | PC | |
| Quantum Information Theory | PC | |
| AI for Engineering Systems | AE | |
| Public Policy, Digital Governance & Ethics | LASS | |
| Understanding the Contemporary World | UWE | |
| Semester 5 | REALIZE | Quantum Hardware Architectures & Devices | PC |
| Cryptography & Post-Quantum Security | PC | |
| Quantum Algorithms & Complexity Theory | PC | |
| Quantum Programming & Software Frameworks | PC | |
| Solid State Physics & Quantum Materials | PC | |
| Semester 6 | REALIZE | Quantum Error Correction & Fault-Tolerant Computing | PC |
| Hybrid quantum-classical hardware-aware system design | PC | |
| Quantum Communication & Quantum Networks | PC | |
| Noise, Decoherence & Quantum Control | PC | |
| PE - I | PE | |
| PE - II | PE | |
| Semester 7 | ASCEND | Quantum Optics & Photonic Systems | PC |
| PE - III | PE | |
| PE - IV | PE | |
| PE - V | PE | |
| Internship | PBL | |
| Semester 8 | ASCEND | PE - VI | PE |
| Capstone Project / Venture Creation / Research Project | PBL | |
The Quantum Technology curriculum follows a layered progression from mathematical abstraction to physical realization and finally to full-stack quantum systems engineering.
| Phase | Semester | Pedagogical Logic / Course Sequencing |
|---|---|---|
| AWAKEN | Sem 1 | Establishes core mathematical and physical intuition required for quantum systems. Calculus, Linear Algebra, and Physics build the language of wave mechanics and transformations. Maker and Discovery Studios initiate experiential and exploratory thinking. |
| Sem 2 | Strengthens mathematical rigor and probabilistic reasoning, essential for quantum measurement and uncertainty. Data Structures introduces computational abstraction required for quantum programming environments. | |
| UNCOVER | Sem 3 | Builds classical hardware and signal-level understanding, forming the physical substrate of quantum systems. Embedded systems and microcontrollers establish the control layer required for quantum devices. |
| Sem 4 | Marks the formal entry into quantum programme core. Mathematical Foundations for Quantum Systems and Quantum Information Theory translate prior mathematics into quantum formalism. Quantum Computing Fundamentals introduces qubit models and computational paradigms. | |
| REALIZE | Sem 5 | Deep immersion into quantum systems stack. Algorithms, cryptography, hardware architectures, and quantum materials create a tightly integrated understanding of computation, security, and physical realization. |
| Sem 6 | Focuses on scalability, reliability, and system integration. Error correction, quantum communication, noise and decoherence, and hybrid system design collectively enable practical, fault-tolerant quantum systems. Electives initiate specialization. | |
| ASCEND | Sem 7 | Advances into specialized quantum domains such as photonics and quantum optics, while electives deepen domain expertise. Internship provides real-world exposure to quantum platforms and research environments. |
| Sem 8 | Culminates in independent quantum system design or research. Capstone integrates algorithms, hardware, communication, and control into a unified quantum solution or experimental study. |
Across all four programmes, four consistent pedagogical design principles govern the semester structure:
| Principle | Design Rationale & Implementation |
|---|---|
| Grouped Object Prerequisite Integrity | Quantum courses are strictly gated by mathematical and physical readiness. Linear Algebra, Probability, and Physics are fully completed before quantum formalism begins in Semester 4, ensuring conceptual clarity in state spaces, operators, and measurement theory. |
| Cognitive Load Calibration | Semesters 1–4 maintain high foundational rigor (23–24 credits). Semesters 5–6 sustain intensity but shift toward integration and application (21 credits). Semesters 7–8 reduce load (17 → 9 credits), enabling deep research engagement, electives, and capstone execution in a frontier domain. |
| Theory-to-Experiment Progression | Early semesters emphasise theoretical constructs and mathematical abstraction, while later semesters progressively increase experimental, simulation, and system-level engagement including hybrid quantum-classical labs and hardware-aware design. |
| Continuity Through PBL | Project-Based Learning provides a continuous experiential backbone from Maker Labs (Semester 1) to Field Projects, Internship, and Capstone. In the quantum context, this evolves from guided experimentation to independent research and system prototyping, ensuring demonstrable competency. |
Four years, full-time (eight semesters), including elective capstone projects, design studies, internships, research interpretation.
The rapid expansion of artificial intelligence across industries has created a fundamental shift in how software, data, and decision systems are built. However, most engineering graduates are still trained to treat AI as isolated model-building exercises, without understanding how these models behave once deployed in real, constrained, and continuously evolving environments. This program exists to address that gap by developing engineers who can build AI systems that function reliably in production on a scale.
To achieve this, the program is structured around the idea that true AI engineering requires more than algorithms, it requires mastery over data systems, distributed computation, model behavior under real-world constraints, and end-to-end deployment pipelines. Students are progressively trained through a tightly integrated pathway that begins with mathematical and computational foundations and advances into machine learning systems, deep learning architectures, and generative AI models that operate at industrial scale.
Learning is reinforced through continuous exposure to real infrastructure environments, including high-performance GPU systems, enabling students to work with large-scale models rather than simplified academic simulations. Alongside technical depth, the program emphasizes system thinking, where students learn to design AI solutions that balance accuracy, scalability, latency, cost, and ethical responsibility.
By the end of the program, learners are prepared not only to develop AI models, but to engineer complete AI systems that can be deployed, monitored, and improved in real-world production ecosystems across industries.
| High-Impact Roles | Career Pathways | Academic Progression |
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The AI C ML programme is structured around the full AI engineering lifecycle — from mathematical foundations and data structures through model development, deployment infrastructure, and responsible governance to frontier generative AI systems. The pedagogical architecture ensures that every implementation course follows its theoretical prerequisite by exactly one semester, preventing cognitive overload while maintaining disciplinary momentum. Total programme credits: 162.
| Semester | Course Title | Category |
|---|---|---|
| Semester 1 | AWAKEN | Calculus - I | BS |
| Physics – I (Electromagnetism and Optics) | BS | |
| Linear Algebra | BS | |
| Materials Science | BS | |
| Cognitive Science for Intelligent Systems | LASS | |
| Technical Communication & Knowledge Design Studio | LASS | |
| AURA Discovery Studio | PBL | |
| Maker Foundations Lab | PBL | |
| Semester 2 | AWAKEN | Calculus - II | BS |
| Probability and Statistics | BS | |
| Biology for Engineers | BS | |
| Physics – II (Quantum Mechanics and Semiconductor Physics) | BS | |
| Digital Storytelling | LASS | |
| Creativity, Imagination & Idea Generation | UWE | |
| Data Structures & Algorithmic Engineering | AE | |
| Semester 3 | UNCOVER | Electronic Devices for Intelligent Systems | AE |
| Signals, Systems & Information Processing | AE | |
| Design Thinking for Intelligent Systems | LASS | |
| Embedded Systems & Real-Time Computing | AE | |
| Microcontrollers, SoC & Edge AI Hardware | AE | |
| Technology, Society & the Future of Work | LASS | |
| Behavioural Science & Human Decision Making | UWE | |
| Personal Finance, Wealth & Financial Wellbeing | UWE | |
| Semester 4 | UNCOVER | Programming for Problem Solving | PC |
| OOP with Java | PC | |
| Computer Organization & Architecture | PC | |
| Discrete Mathematics | PC | |
| AI for Engineering Systems | AE | |
| Public Policy, Digital Governance & Ethics | LASS | |
| Understanding the Contemporary World | UWE | |
| Semester 5 | REALIZE | Computer Vision & Image Understanding | PC |
| Machine Learning (Supervised & Unsupervised) | PC | |
| Data Engineering & Large-Scale Data Processing | PC | |
| Computer Networks | PC | |
| Edge & Embedded AI Systems | PC | |
| Responsible, Explainable & Trustworthy AI | PC | |
| Social & Sustainable Engineering Field Project | PBL | |
| Semester 6 | REALIZE | Database Systems for AI Applications | PC |
| Distributed & Cloud Computing for AI | PC | |
| Natural Language Processing | PC | |
| MLOps & Model Deployment Engineering | PC | |
| PE - I | PE | |
| PE - II | PE | |
| Semester 7 | ASCEND | Deep Learning & Neural Networks | PC |
| PE - III | PE | |
| PE - IV | PE | |
| PE - V | PE | |
| Internship | PBL | |
| Semester 8 | ASCEND | PE - VI | PE |
| Capstone Project / Venture Creation / Research Project | PBL |
The AI C ML curriculum applies a pipeline-mirroring sequencing strategy: just as a production AI system flows from data ingestion through model training to deployment and governance, the curriculum sequences Data Structures → ML foundations → MLOps → Responsible AI → Deployment infrastructure → Frontier models. This creates a student experience where each new course is immediately contextualised by prior learning, dramatically reducing cognitive load while accelerating professional readiness.
| Phase | Semester | Pedagogical Logic / Course Sequencing |
|---|---|---|
| AWAKEN | Sem 1 | Establishes mathematical, scientific, and cognitive foundations. Calculus, Linear Algebra, and Physics build analytical thinking, while Cognitive Science introduces intelligence paradigms. Maker and Discovery Labs initiate experiential learning and systems curiosity. |
| Sem 2 | Strengthens mathematical maturity and introduces algorithmic thinking. Probability & Statistics directly enable machine learning reasoning, while Data Structures establishes computational efficiency and abstraction. | |
| UNCOVER | Sem 3 | Introduces systems-level understanding of intelligent machines. Signals, embedded systems, and hardware courses prepare students for Edge AI and real-time systems, bridging physical and computational intelligence. |
| Sem 4 | Marks the transition to core computing and AI readiness. Programming, OOP, Computer Architecture, and Discrete Mathematics establish the formal computational backbone required for AI systems. AI for Engineering Systems provides early applied exposure. | |
| REALIZE | Sem 5 | Begins core AI specialization. Machine Learning, Computer Vision, Data Engineering, Networks, and Edge AI collectively build the data → model → system pipeline. Responsible AI introduces governance and ethics at the point of application. |
| Sem 6 | Completes the AI infrastructure and deployment stack. Databases, Cloud Computing, NLP, and MLOps enable scalable, production-grade AI systems. Electives initiate domain specialization. | |
| ASCEND | Sem 7 | Focus shifts to advanced intelligence modeling and specialization. Deep Learning forms the backbone for advanced electives. Internship ensures real-world immersion and application of full-stack AI knowledge. |
| Sem 8 | Culminates in independent system creation and innovation. Capstone integrates the entire AI lifecycle including data, modeling, deployment, and governance into a deployable solution or research output. |
Across all four programmes, four consistent pedagogical design principles govern the semester structure:
| Principle | Design Rationale & Implementation |
|---|---|
| Prerequisite Integrity | Every Programme Core course is strictly sequenced after its mathematical, computational, or systems prerequisite. The Awaken and Uncover phases act as structured scaffolding layers, ensuring that students enter the Realize phase with full conceptual readiness rather than fragmented exposure. |
| Cognitive Load Management | Semesters 1–4 maintain high but structured academic intensity (23–24 credits) focused on foundational depth. Semesters 5–6 reduce slightly (21 credits) but increase complexity through labs and integration. Semesters 7–8 significantly reduce credit load (17 → 9) to allow deep specialization, internships, and capstone execution. |
| Progressive Lab-to-Theory Shift | Early semesters balance theory and labs, but from Semester 5 onward, practice dominates learning. Courses such as Machine Learning, Computer Vision, MLOps, and Cloud Computing are implementation-heavy, ensuring students transition from understanding concepts to building deployable systems. |
| Longitudinal PBL Integration | Project-Based Learning forms a continuous experiential spine: from Discovery Studio (Semester 1) and Maker Labs to Field Projects, Internship, and Capstone. Each stage increases in complexity and autonomy, ensuring progressive mastery from guided exploration to independent innovation. |
Four years, full-time (eight semesters), including elective capstone projects, design studies, internships, research interpretation.
The field of robotics is undergoing a fundamental shift from pre-programmed industrial automation to intelligent, adaptive systems capable of perception, reasoning, and autonomous action in unstructured environments. This program is designed to prepare engineers who can operate at this intersection of machine intelligence and physical systems engineering.
It focuses on the idea that true robotics expertise cannot be developed through simulation alone but must emerge from direct engagement with real-world robotic systems that integrate sensing, control, computation, and actuation. Students are trained to understand how machines perceive their environment through sensors, how they interpret uncertainty, and how they translate decisions into precise physical movement.
The learning journey progresses from mathematical and computational foundations into robot kinematics, control systems, perception, and reinforcement learning, ultimately culminating in the design and deployment of fully autonomous systems. These systems are tested not only in virtual environments but also on physical robotic platforms such as mobile robots, manipulators, and drones.
By the end of the program, students are capable of building end-to-end autonomous systems that integrate AI-driven perception, decision-making, and real-time control for real-world applications in industry, mobility, healthcare, and defense.
The RCAI programme follows the AWAKEN–UNCOVER–REALIZE–ASCEND progression aligned with the Perception– Planning–Control–Integration architecture. Semesters 1–2 build foundations in mathematics, sciences, and computing; Semesters 3–4 develop core engineering and introduce robotics. Semesters 5–6 deliver the autonomy stack, while Semesters 7–8 emphasize advanced topics, electives, industry immersion, and a capstone. Total programme credits: 162.
| Semester | Course Title | Category |
|---|---|---|
| Semester 1 | AWAKEN | Calculus - I | BS |
| Physics – I (Electromagnetism and Optics) | BS | |
| Linear Algebra | BS | |
| Materials Science | BS | |
| Cognitive Science for Intelligent Systems | LASS | |
| Technical Communication & Knowledge Design Studio | LASS | |
| AURA Discovery Studio | PBL | |
| Maker Foundations Lab | PBL | |
| Semester 2 | AWAKEN | Calculus - II | BS |
| Probability and Statistics | BS | |
| Biology for Engineers | BS | |
| Physics – II (Quantum Mechanics and Semiconductor Physics) | BS | |
| Digital Storytelling | LASS | |
| Creativity, Imagination & Idea Generation | UWE | |
| Data Structures & Algorithmic Engineering | AE | |
| Semester 3 | UNCOVER | Electronic Devices for Intelligent Systems | AE |
| Signals, Systems & Information Processing | AE | |
| Design Thinking for Intelligent Systems | LASS | |
| Embedded Systems & Real-Time Computing | AE | |
| Microcontrollers, SoC & Edge AI Hardware | AE | |
| Technology, Society & the Future of Work | LASS | |
| Behavioural Science & Human Decision Making | UWE | |
| Personal Finance, Wealth & Financial Wellbeing | UWE | |
| Semester 4 | UNCOVER | Robot Kinematics & Dynamics | PC |
| Discrete Mathematics | PC | |
| Programming for Problem Solving | PC | |
| OOP with Java | PC | |
| AI for Engineering Systems | AE | |
| Public Policy, Digital Governance & Ethics | LASS | |
| Understanding the Contemporary World | UWE | |
| Semester 5 | REALIZE | Mechatronics System Design & Integration | PC |
| Embedded Systems for Robotic Platforms | PC | |
| Machine Vision & Image Processing for Robotics | PC | |
| Robot Operating System (ROS) & Middleware | PC | |
| AI & Machine Learning for Robotics | PC | |
| Multi-Sensor Data Fusion | PC | |
| Social & Sustainable Engineering Field Project | PBL | |
| Semester 6 | REALIZE | Advanced Control Systems for Robotics | PC |
| Motion Planning & Trajectory Optimization | PC | |
| AI & Machine Learning for Robotics | PC | |
| Intelligent Autonomous Systems Design | PC | |
| PE - I | PE | |
| PE - II | PE | |
| Semester 7 | ASCEND | Autonomous Navigation, Localization & SLAM | PC |
| PE - III | PE | |
| PE - IV | PE | |
| PE - V | PE | |
| Internship | PBL | |
| Semester 8 | ASCEND | PE - VI | PE |
| Capstone Project / Venture Creation / Research Project | PBL |
The Robotics C AI curriculum is structured along a Perception → Planning → Control → Integration → Autonomy progression, reflecting how real intelligent robotic systems are engineered. The design ensures that mathematical foundations precede kinematics, hardware precedes middleware, and perception precedes autonomy, creating a coherent and industry-aligned learning trajectory.
| Phase | Semester | Pedagogical Logic / Course Sequencing |
|---|---|---|
| AWAKEN | Sem 1 | Establishes mathematical, physical, and cognitive foundations. Calculus and Physics enable understanding of motion and forces, while Linear Algebra introduces transformations essential for robotics. Maker Labs initiate hands-on system intuition. |
| Sem 2 | Strengthens mathematical modelling and computational thinking. Probability supports uncertainty modelling in sensing and localisation, while Data Structures enables efficient algorithm implementation for planning and control. | |
| UNCOVER | Sem 3 | Builds hardware and signal-level foundations for robotics. Embedded systems, microcontrollers, and signals provide the base for sensor interfacing, actuator control, and real-time robotic execution. |
| Sem 4 | Marks entry into the robotics programme core. Robot Kinematics & Dynamics establishes motion modelling, while Programming and OOP provide the software backbone. Discrete Mathematics supports algorithmic reasoning for planning and control. | |
| REALIZE | Sem 5 | Develops the robotics integration layer. Mechatronics, Embedded Robotics, Vision, ROS, Machine Learning, and Sensor Fusion collectively enable perception and system integration. ROS acts as the middleware connecting sensing, control, and computation. |
| Sem 6 | Focuses on autonomy and intelligent decision-making. Advanced Control, Motion Planning, and Autonomous System Design enable robots to act independently. AI/ML deepens perception and decision-making capability. | |
| ASCEND | Sem 7 | Advances into navigation and real-world deployment. SLAM integrates perception and planning for autonomous navigation. Internship ensures exposure to industrial robotics systems and deployment environments. |
| Sem 8 | Culminates in full-stack robotic system realization. Capstone integrates perception, planning, control, and intelligence into a deployable autonomous system or research prototype. |
Across all four programmes, four consistent pedagogical design principles govern the semester structure:
| Principle | Design Rationale & Implementation |
|---|---|
| Prerequisite Integrity | Robotics courses are strictly sequenced after mathematical, physical, and computational readiness. Kinematics appears only after Linear Algebra and Calculus, while autonomy courses are introduced only after perception, control, and middleware foundations are established. |
| Cognitive Load Calibration | Semesters 1–4 maintain high foundational rigor (23–24 credits). Semesters 5–6 reduce slightly (21 credits) but introduce complex integration across perception, control, and AI. Semesters 7–8 reduce load (17 → 9 credits) to enable deep system building, internship, and capstone execution. |
| Physical-to-Autonomy Progression | The curriculum progresses from mechanics and hardware → sensing → control → intelligent autonomy, ensuring students first understand physical systems before designing intelligent behaviour. This mirrors real robotic system engineering practice. |
| Continuity Through PBL | Project-Based Learning provides a continuous build trajectory, from Maker Labs to Field Projects, Internship, and Capstone. Students evolve from assembling components to engineering fully autonomous robotic systems with real-world applicability. |
Four years, full-time (eight semesters), including elective capstone projects, design studies, internships, research interpretation.
Modern industrial and societal systems are increasingly defined by the tight integration of computation, physical processes, and human interaction. This program is designed for engineers who will build and govern these interconnected systems where software decisions directly influence physical outcomes in real time.
Cyber-Physical Systems operate at the intersection of embedded computing, real-time control, sensing, communication networks, and intelligent decision-making. In such environments, system failures are no longer purely digital—they manifest physically across manufacturing lines, transportation systems, energy grids, healthcare devices, and autonomous infrastructure.
This program prepares engineers to design systems that are continuously aware, adaptive, and resilient. Students learn how to model physical processes digitally, simulate real-world environments through digital twins, and deploy intelligent control systems that operate under strict timing, safety, and reliability constraints.
The learning pathway progresses from foundational engineering and computation into embedded systems, real-time operating systems, industrial IoT, and finally into large-scale cyber-physical ecosystems that include collaborative robotics, smart infrastructure, and human-integrated systems such as wearable and biomedical networks.
By the end of the program, graduates are capable of designing and managing Industry 5.0 systems that unify computation, hardware, and human-centric intelligence in real operational environments.
| Career Pathways | Industry Domains | Academic Progression |
|---|---|---|
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The CPS programme sequences its curriculum along the Physical Layer → Control Layer → Connectivity Layer → Intelligence Layer → Security Layer → Virtualisation Layer architecture of real cyber-physical systems. This mirrors industry CPS system-of-systems design practice, enabling students to understand why each course is placed where it is — they are building a coherent engineered system semester by semester. Total programme credits: 162.
| Semester | Course Title | Category |
|---|---|---|
| Semester 1 | AWAKEN | Calculus - I | BS |
| Physics – I (Electromagnetism and Optics) | BS | |
| Linear Algebra | BS | |
| Materials Science | BS | |
| Cognitive Science for Intelligent Systems | LASS | |
| Technical Communication & Knowledge Design Studio | LASS | |
| AURA Discovery Studio | PBL | |
| Maker Foundations Lab | PBL | |
| Semester 2 | AWAKEN | Calculus - II | BS |
| Probability and Statistics | BS | |
| Biology for Engineers | BS | |
| Physics – II (Quantum Mechanics and Semiconductor Physics) | BS | |
| Digital Storytelling | LASS | |
| Creativity, Imagination & Idea Generation | UWE | |
| Data Structures & Algorithmic Engineering | AE | |
| Semester 3 | UNCOVER | Electronic Devices for Intelligent Systems | AE |
| Signals, Systems & Information Processing | AE | |
| Design Thinking for Intelligent Systems | LASS | |
| Embedded Systems & Real-Time Computing | AE | |
| Microcontrollers, SoC & Edge AI Hardware | AE | |
| Technology, Society & the Future of Work | LASS | |
| Behavioural Science & Human Decision Making | UWE | |
| Personal Finance, Wealth & Financial Wellbeing | UWE | |
| Semester 4 | UNCOVER | Cyber-Physical Systems Architecture & Design | PC |
| Real-Time Operating Systems & Scheduling | PC | |
| Programming for Problem Solving | PC | |
| OOP with Java | PC | |
| AI for Engineering Systems | AE | |
| Public Policy, Digital Governance & Ethics | LASS | |
| Understanding the Contemporary World | UWE | |
| Semester 5 | REALIZE | Embedded Systems Design & Firmware Engineering | PC |
| Sensors, Actuators & Instrumentation Engineering | PC | |
| Computer Networks | PC | |
| CPS Communication Protocols | PC | |
| Control Systems for CPS | PC | |
| Wireless Sensor Networks & Edge Devices | PC | |
| Social & Sustainable Engineering Field Project | PBL | |
| Semester 6 | REALIZE | Digital Signal Processing for Sensor Data | PC |
| CPS Security, Safety & Resilience Engineering | PC | |
| Industrial Internet of Things (IIoT) Systems | PC | |
| Digital Twin Modelling & Simulation | PC | |
| Systems Integration & Interoperability | PC | |
| PE - I | PE | |
| PE - II | PE | |
| Semester 7 | ASCEND | Edge / Embedded AI for Smart Systems | PC |
| PE - III | PE | |
| PE - IV | PE | |
| PE - V | PE | |
| Internship | PBL | |
| Semester 8 | ASCEND | PE - VI | PE |
| Capstone Project / Venture Creation / Research Project | PBL |
The Cyber-Physical Systems curriculum follows a layered systems engineering progression, where students incrementally build a complete CPS stack—starting from physical sensing and embedded systems, progressing through control and connectivity, and culminating in intelligent, secure, and integrated cyber-physical environments. Each semester introduces a new architectural layer while reinforcing previously established ones, ensuring strong system coherence and engineering depth.
| Phase | Semester | Pedagogical Logic / Course Sequencing |
|---|---|---|
| AWAKEN | Sem 1 | Establishes fundamental scientific, mathematical, and cognitive foundations. Physics and Materials Science provide understanding of sensing and actuation principles, while Calculus and Linear Algebra enable system modelling. Maker and Discovery Labs initiate hands-on system thinking. |
| Sem 2 | Builds mathematical rigor and computational thinking. Probability supports uncertainty modelling in CPS environments, while Data Structures introduces algorithmic foundations required for scheduling, networking, and system optimisation. | |
| UNCOVER | Sem 3 | Introduces hardware and signal-level foundations. Electronic Devices, Signals, Embedded Systems, and Microcontrollers establish the physical and computational substrate of Cyber-Physical Systems. |
| Sem 4 | Marks the formal entry into CPS core architecture. CPS Architecture and RTOS provide system design and execution environments, while Programming and OOP ensure software readiness for CPS development. | |
| REALIZE | Sem 5 | Builds the complete CPS operational stack. Sensors, Control Systems, Embedded Design, Networks, Protocols, and Wireless Systems collectively enable sensing, actuation, communication, and real-time control. |
| Sem 6 | Focuses on system intelligence, security, and integration. DSP processes sensor data, IIoT enables large-scale connectivity, Digital Twin introduces virtual modelling, and Security ensures resilience. Electives allow specialization. | |
| ASCEND | Sem 7 | Advances to intelligent CPS systems and real-world deployment. Edge AI enables autonomous decision-making in embedded environments. Internship ensures exposure to real industrial CPS ecosystems. |
| Sem 8 | Culminates in full-stack CPS system realization. Capstone integrates sensing, control, communication, intelligence, and security into a deployable system or industrial solution. |
Across all four programmes, four consistent pedagogical design principles govern the semester structure:
| Principle | Design Rationale & Implementation |
|---|---|
| Prerequisite Integrity | All CPS core courses are strictly dependent on prior mathematical, hardware, and computational foundations. The Awaken and Uncover phases ensure readiness before introducing CPS architecture, control systems, and real-time execution environments. |
| Cognitive Load Calibration | Semesters 1–4 maintain high foundational intensity (23–24 credits). Semesters 5–6 reduce slightly (21 credits) but introduce complex system integration and lab-heavy learning. Semesters 7–8 reduce load (17 → 9 credits) to support internship and capstone-driven synthesis. |
| System-to-Integration Progression | Early learning focuses on individual system components such as sensors, embedded systems, and signals, while later semesters emphasize integration across layers, including control, communication, intelligence, and digital twins. This mirrors real-world Cyber-Physical System deployments. |
| Continuity Through PBL | Project-Based Learning ensures a continuous system-building journey, from Maker Labs (Semester 1) to Field Projects, Internship, and Capstone. Students progressively evolve from building isolated subsystems to fully integrated cyber-physical solutions. |
Four years, full-time (eight semesters), including elective capstone projects, design studies, internships, research interpretation.
