Research

1. Internet of Things Lab

This lab is facilitating the conduction of the Introduction to IoT, Advanced IoT, and IoT Analytics & Security course labs respectively for the academic year 2023-2024 Even Semester. The primary objectives of an IoT lab encompass providing an educational environment for hands-on learning and skill development in IoT technologies. This enables participants to prototype and develop IoT devices and applications, integrating a variety of sensors, communication protocols, and data analytics techniques. Emphasis is placed on understanding and implementing security measures, exploring cloud integration, and investigating networking technologies. Additionally, this lab often encourages collaborative projects, addresses real-world challenges, and promotes an interdisciplinary approach and ethical considerations.

2. Data Visualization Lab

This lab is designed to bridge theoretical concepts from programming paradigms with practical applications in data visualization, empowering the learners to communicate insights effectively through visual representation.

A. Applying Procedural Programming to Data Processing: Utilize procedural programming languages for efficient data processing. Implement algorithms to prepare data for visualization. Emphasize modular code design for data manipulation.
B. Leveraging Object-Oriented Programming in Visualization Tools: Explore the integration of OOP principles in developing visualization tools. Implement classes and objects for data representation. Enhance code flexibility using inheritance and polymorphism.

The Data Visualization Laboratory has 35 computer systems which are equipped with cutting-edge hardware, featuring a Gigabyte H610M H V2 DDR4 motherboard, 16 GB DDR4 RAM, and a 12 th Gen Intel Core i7-12700 processor. This powerful setup ensures that the laboratory is well-suited for computationally intensive tasks in the realm of computer vision. The inclusion of a high-speed 512 GB SSD storage further enhances the efficiency of data access, enabling quick retrieval of datasets and facilitating smooth execution of image processing algorithms. The laboratory's GPU infrastructure, powered by the NVIDIA GeForce GT 730 graphics card, is a key asset for parallel processing capabilities.

3. Cyber Security Lab

This lab aims to offer research facilities focused on digital forensics and cybersecurity. The lab focuses on digital forensics, incident response, audio & video analysis (forensics & tamper detection), network & data security, malware analysis (static and dynamic), ethical hacking, and developing web 3.0-based security applications. This lab is used to conduct research-related experiments by using several cybersecurity tools such as Snort, Metasploit, Wire shark, Nmap, Zeek, Sclapal, Autopsy, Bulk extractor, and OCFA to implement Intrusion detection techniques, and Penetration testing to analyze the traffic in the network and monitor the network. This lab was established at the cost of Rs. 66 lakhs with funds from CHRIST (Deemed to be University), Bangalore, and Rs. 30 Lakhs worth of Hardware and Software from IFF Labs

4. Computer Vision Lab

The objective of the Computer Vision lab is to provide a structured and hands-on learning environment for students to explore, experiment, and advance their understanding of computer vision principles, techniques, and applications. The lab aims to equip students with a solid theoretical foundation and practical skills in computer vision principles, fostering hands-on learning through projects. The Computer Vision Laboratory has 30 computer systems which are equipped with cutting-edge hardware, featuring a Gigabyte H610M H V2 DDR4 motherboard, 16 GB DDR4 RAM, and a 12th Gen Intel Core i7-12700 processor. This powerful setup ensures that the laboratory is well-suited for computationally intensive tasks in the realm of computer vision. The inclusion of a high-speed 512 GB SSD storage further enhances the efficiency of data access, enabling quick retrieval of datasets and facilitating smooth execution of image processing algorithms. The laboratory & GPU infrastructure, powered by the NVIDIA GeForce GT 730 graphics card, is a key asset for parallel processing capabilities.