Computer Engineering
Overview of Computer Engineering Department
The Department of Artificial Intelligence and Machine Learning was started in 2023 with an intake of 60 students. The department is headed by Dr. Sohan Kumar Gupta who has more than 27 years in teaching in reputed engineering colleges and Industries. The faculty members in the department are well qualified, experienced, and committed to the upliftment of the department, specializing in Object-Oriented System Analysis and Design, Artificial Intelligence, Python Programming, Expert Systems, Multimedia, RDBMS, Machine Learning, Neural Networks, Data Communication, Data Structures and Algorithms, Operating System, and Cyber Security.
The Computer Engineering Department stands as a beacon of innovation and academic excellence, driving forward the frontiers of technology and shaping the future of computing. Nestled within the academic enclave of our institution, our department embodies a dynamic blend of theoretical insights and practical applications, fostering a vibrant learning environment for aspiring engineers and researchers.
At the heart of our department’s mission lies a commitment to excellence in education, research, and service. Through rigorous academic programs, we equip our students with the knowledge, skills, and mindset necessary to tackle the complex challenges of the digital age. Our faculty, comprising distinguished scholars and seasoned practitioners, brings expertise to the classroom, inspiring students to think critically, creatively, and ethically.
The curriculum is designed to provide a comprehensive understanding of computer engineering principles, spanning from fundamental theories to cutting-edge technologies. Students delve into various topics, including digital systems, computer architecture, software engineering, networking, and embedded systems. Through hands-on projects, internships, and research opportunities, students gain practical experience and cultivate the problem-solving prowess essential for success in the field.
Research is a cornerstone of our department’s identity, driving innovation and pushing the boundaries of what is possible in computing. Our faculty members lead pioneering research initiatives in areas such as artificial intelligence, cybersecurity, the Internet of Things (IoT), robotics, and quantum computing. Students are encouraged to participate in research projects, contributing to groundbreaking discoveries and forging connections with industry partners.
Beyond the classroom and the lab, our department is deeply engaged with the broader community. We collaborate with industry partners to address real-world challenges, provide consulting services, and offer professional development opportunities for practicing engineers. Through outreach programs, we inspire the next generation of technologists and promote diversity and inclusion in STEM fields.
As we look to the future, the Computer Engineering Department remains steadfast in its commitment to excellence, innovation, and service. We embrace the ever-evolving nature of technology, adapt to new paradigms, and continue to push the boundaries of knowledge. Together, we strive to shape a future where technology serves humanity and empowers individuals to create positive change in the world.
VISION
To Excel in the emerging areas of Computer Engineering by imparting knowledge, equipping students with latest skills in the field of technology supplemented with practical orientation to face challenges in the fast-morphing modern computing industry and academia for the betterment of the society.
MISSION
- To establish and maintain an effective operational environment and deliver quality, prompt cost effective and reliable technology services to the society as well as compliment the local economic goals.
- Educate students in the best practices of the field as well as integrate the latest research into the curriculum.
- To enhance academic collaborations for National exposure.
Programming Laboratory
A Programming Laboratory is a dedicated space equipped with computers and software for students to practice coding. It provides hands-on experience, fostering understanding of programming concepts and languages through practical application. Labs often offer support from instructors or assistants to aid learners in their coding endeavors.
Software Used:
C & C++ Language (Windows)
Data Structure Laboratory
A Data Structure Laboratory is a specialized facility where students experiment with organizing and manipulating data efficiently. It offers hands-on practice in implementing and analyzing various data structures like arrays, linked lists, trees, and graphs. This experiential learning enhances comprehension and problem-solving skills in managing complex data arrangements.
Software Used:
C++ Language (Windows / Ubuntu)
Database Management System Laboratory
A Database Management System Laboratory is a dedicated space for students to learn and practice managing databases. It provides hands-on experience in designing, querying, and maintaining databases using software like SQL. This practical setting allows learners to understand database concepts and gain proficiency in handling real-world data storage and retrieval tasks.
Software Used:
MySQL, Oracle (Windows)
Operating System Laboratory
An Operating System Laboratory is a specialized environment where students explore the principles and functionality of operating systems. Through hands-on exercises, they gain practical experience in tasks like process management, memory allocation, file systems, and networking. This immersive learning enhances comprehension of how operating systems facilitate computer functionality and resource management.
Software Used:
C Language, Windows and Ubuntu Terminal.
Data Science Laboratory
A Data Science Laboratory is a dedicated space where students gain hands-on experience in applying statistical, mathematical, and computational techniques to analyze and interpret complex data sets. Through practical exercises, learners develop skills in data visualization, machine learning, and data mining, preparing them for real-world data-driven problem-solving tasks.
Software Used:
Python and R Programming Language
Design and Analysis of Algorithms Laboratory
The Design and Analysis of Algorithms focuses on developing efficient solutions to computational problems. Students study algorithmic design paradigms, analyze their correctness and complexity, and evaluate their performance. Through theoretical study and practical implementation, learners gain expertise in solving diverse computational challenges, preparing them for advanced problem-solving in computer science.
Software Used:
C Language Windows Terminal
Data Analytics with Excel Laboratory
In a Data Analytics with Excel Laboratory, students engage in practical exercises to analyze data using Excel’s functions and features. Through hands-on exploration, they learn to manipulate, visualize, and interpret datasets, acquiring skills in data cleansing, pivot tables, statistical analysis, and visualization techniques, preparing them for data-driven decision-making roles.
Software Used:
Microsoft Excel
Machine Learning Laboratory
In a Machine Learning Laboratory, students delve into hands-on experimentation with algorithms and models to analyze and predict patterns in data. Through practical exercises, they gain proficiency in tasks like data preprocessing, model training, evaluation, and optimization, preparing them for real-world applications in various fields reliant on machine learning techniques.
Software Used:
Python Language
Python Programming Laboratory
The Python Programming Laboratory provides hands-on experience in Python language essentials, data structures, algorithms, and problem-solving techniques. Through practical exercises and projects, students develop proficiency in coding, debugging, and software development, fostering a strong foundation for programming in various domains.
Software Used:
PyCharm and Python Language
Mango DB Laboratory
The Mango DB Laboratory focuses on practical applications of MongoDB, a popular NoSQL database system. Students learn database design, querying, indexing, and scaling techniques through hands-on exercises and real-world projects. This laboratory equips learners with valuable skills for managing large volumes of data in modern software development environments.
Software Used:
Mango DB.
Object Oriented with Java Laboratory
The Object Oriented with Java Laboratory immerses students in Java programming, emphasizing object-oriented principles such as inheritance, encapsulation, and polymorphism. Through hands-on exercises and projects, learners develop proficiency in designing and implementing Java applications, gaining essential skills for building robust and scalable software solutions.
Software Used:
C++ Language
Digital Image Processing Laboratory
The Digital Image Processing Laboratory delves into the theory and application of techniques for manipulating and analyzing digital images. Students explore methods such as filtering, segmentation, and feature extraction through hands-on projects. This laboratory equips learners with skills vital for image enhancement, recognition, and computer vision applications.
Software Used:
Photoshop and Lightroom
Mobile Application Development Laboratory
The Mobile Application Development Laboratory focuses on creating applications for mobile platforms like iOS and Android. Students learn about UI/UX design, programming languages (e.g., Swift, Java, Kotlin), and integrating features such as GPS, sensors, and databases. Through hands-on projects, learners gain proficiency in building functional and user-friendly mobile apps.
Software Used:
Xcode.
Value-added courses are supplementary educational programs designed to enhance skills beyond the primary curriculum. They aim to provide practical knowledge and expertise, increasing employability and personal development. These courses often cover specialized topics or emerging fields, offering participants a competitive edge in their chosen career paths.
Block Chain Technology: Blockchain technology revolutionizes data management by creating decentralized, immutable ledgers. Utilizing cryptographic principles, it securely records transactions across a network of computers, eliminating the need for intermediaries. Its transparency, security, and incorruptibility make it invaluable across industries, from finance and healthcare to supply chain management and beyond.
Cyber Security: Cybersecurity safeguards digital systems from unauthorized access, theft, or damage to data, networks, or devices. It encompasses various technologies, processes, and practices, including encryption, firewalls, and intrusion detection systems. As digital threats evolve, cybersecurity professionals continually adapt strategies to protect against malware, phishing attacks, and other cyber threats.
Sustainable Technology: Sustainable technology aims to minimize environmental impact and conserve resources throughout its lifecycle. It encompasses renewable energy sources like solar and wind power, as well as eco-friendly practices in manufacturing, transportation, and waste management. By promoting efficiency and reducing pollution, sustainable technology plays a crucial role in addressing climate change and fostering a greener future.
Metaverse: The metaverse is a virtual reality space where users interact, create, and engage in immersive experiences. It transcends traditional online platforms, offering a persistent, interconnected universe. With its potential for gaming, socializing, commerce, and beyond, the metaverse represents a paradigm shift in how we perceive and engage with digital environments.
Software Development Methodologies: Focuses on agile, lean, and DevOps practices, equipping students with methodologies and tools to efficiently develop, test, and deploy software solutions, enhancing productivity and collaboration in development teams.
Virtual Reality (VR) and Augmented Reality (AR): Explores immersive technologies and their applications in gaming, education, training, and simulation, enabling students to design and develop interactive experiences for diverse industries and audiences.
Data Science: Covers data analysis, visualization, and predictive modeling techniques, preparing students to extract insights and value from large datasets, driving informed decision-making in diverse fields like finance, healthcare, and marketing.
Internet of Things (IoT): Explores the interconnectedness of devices, sensors, and data networks, enabling students to design and implement IoT solutions for smart homes, healthcare, agriculture, and industrial automation.
PROGRAM OUTCOMES (POs)
1 Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
2 Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
3 Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
4 Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
5 Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
6 The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
7 Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
9 Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
10 Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
11 Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
12 Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
COs
