IIT Jodhpur | हिंदी संस्करण


  1.   AR/VR Lab (Sponsored by Samsung): 
    Overview: To build the world class facility for developing Augmented And Virtual based applications and enhancing the technological advancement to have seamless experience. Samsung India Electronics Private Limited (SIEL) is helping us to set up the lab with all the necessary infrastructure and different types of equipment. The lab is designed to support exploratory, development and research activities in the domain and related areas.
    AR-VR lab at IIT Jodhpur will empower
         1)  Students to develop advanced AR-VR based solutions as part of their curriculum

     2)  Researchers to work with cutting edge devices to enhance the AR-VR experience

     3)  Support start-ups/users from nearby organization(s) by providing them infrastructure for AR-VR based solutions

  2. Hardware Lab:
    Overview: Hardware lab aims to facilitate research activities on various computing architectures, embedded systems, and CAD for VLSI and also to support graduate and undergraduate courses linked to computer organization. The lab is being developed in line with industry demands and to meet the requirements of current AI/ML driven hardware applications. 
    Hardware lab focuses on following areas

     1)  Intelligent Embedded Computing

     2)  Computer Architecture

     3)  CAD for VLSI 

     4)  AI Accelerators

     5)  Edge Computing Devices

   3.  AI and Cognitive Computing Lab:
    This lab focuses on learning techniques that use constantly changing data, do reasoning to make sense of data, and evolve self-correction mechanisms. These technologies are integrated with smart decision support systems that make use of large volumes of data and sophisticated algorithms for better analysis, thus helping in understanding and simulating reasoning and human behaviour. The computing infrastructure comprises the Nvidia DGX-2 System, the world’s most powerful AI System for the most complex AI challenges. The availability of the state of the art computing resources drives the research and development of the next generation of AI Systems, which will transform computers from tools into problem solving partners and enable AI systems to explain their actions and to acquire and understand and reason with common sense knowledge. The goal is to immerse AI in every field, every device in Healthcare, Agriculture, Public Safety, Social good, IOT etc.
   4. Language Technology and Knowledge Management Lab:
    Overview: Research in this lab is broadly focussed towards information extraction, information access, and knowledge management. Thanks to internet technology and social media, there is an enormous amount of data such as images, text, videos, speech, etc available around us. However, most of these data are unstructured and not directly useful to us. In the proposed lab, we aim to harvest knowledge from these unstructured data and use multimodal context in indexing, retrieval, transcription, question-answering, translation, and summarization.
    In this regard, since language provides an excellent interface between AI systems and humans, understanding language is one of the major focuses as well.  The lab focuses on 

     1)  Knowledge harvesting from the multimodal data

     2)  Knowledge-aware Computer Vision

     3)  Language understanding-driven Document Image Analysis

     4)  E-governance through Social Network


   5.  Trusted Visual Intelligence (Trusted.VI) Lab
    Overview: The Trusted.VI lab (Trusted Visual Intelligence Lab) broadly operates at the intersection of Computer Vision and Machine Learning, with applications ranging from biometric analysis, image forensics, object classification, and analysis of medical data captured via different imaging sensors. We work on multiple biometric modalities including face, fingerprint, iris, and multimodal. The lab focuses on a wide range of challenging real-world scenarios such as recognition/analysis in varying spectra (VIS/NIR), varying resolutions, and matching across different domains. Beyond biometrics, the lab also focuses on developing algorithms for core machine learning challenges such as learning with small sample size data and online learning. While solving problems with direct applicability to real-world scenarios, the lab also focuses on making machine learning algorithms adversarially robust and developing dependable and trusted solutions across different domains and applications. 
     Lab website: http://iab-rubric.org/index.html
  6. VANETs and Cyber Security Lab:
    Overview: VANET (Vehicular Ad-hoc NETworks) have become a promising field of research and is an important component of Intelligent Transportation Systems (ITS). Dramatic increase in the number of vehicles equipped with computing technologies and wireless communication devices have given way to new application scenarios that were not feasible before. In this era of modern technology, the Internet of Vehicles (IoV) is an emergent technology. The IoV augments ITS by magnifying the capabilities of VANETs riding on the concept of the Internet of Things (IoT). IoV enabled vehicles exchange information with roadside units (RSU) with minimum or zero human intervention. This autonomous nature of IoV requires strong authentication for each entity to recognize each other, as well as these entities should ensure the integrity of the exchanged information. Otherwise, this autonomy will attract malicious users and malicious activity. Due to the dynamic nature of the IoV network, it is almost impossible to solve the Cyber Security issue with the centralized authentication system.
     This laboratory is equipped with licensed EXata Network Emulator Software, Duckiebots Setup, Implementation of Real Time Vehicular Networks Testbed (using OBU & RSU) and open source software such as ns2/ns3 and OmNet ++ among many others. The students also get hands-on with experiments using Network Hardware (i.e., IoT devices, Raspberry Pi, Routers, Switches, Firewalls, PCs, Servers, Laptops, Sensors, and Arduino) which help to monitor network usage, bandwidth, throughput, delay and security attacks.


Figure 1: A VANET scenario with clustering

Figure 2: IoV Scenario with secure communications

   7.  Vision Application Lab:
    The focus of this lab is on various problems related to visual understanding. These include recognition (detection, categorization and retrieval), biometric and behavioural analysis (face, gesture and body pose), low-level vision, image and video synthesis, vision+language tasks (image captioning, visual question answering and cross-modal retrieval), segmentation, shape analysis, and 3D from multiview and sensors. These problems are addressed in a data-driven manner using various machine learning techniques (both by adapting the existing ones as well as proposing new ones), and are studied in the context of different domains, such as scanned documents, architectural layout plans, natural scenes, activity videos, etc.