Data deduplication or intelligent compression is a process that eliminates redundant copies of data and reduces storage overhead. Data deduplication techniques ensure that only one unique instance of data is retained on storage media, such as disk, flash or tape. Redundant data blocks are replaced with a pointer to the unique data copy. In that way, data deduplication closely aligns with incremental backup, which copies only the data that has changed since the previous backup.

   Researchers Involved:

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   Machine Learning Models for Snow, Glacier Terrain and Hazard Mapping

Glaciers present in the mountain regions constitute a major part of the earth system. It has been observed that mapping and analyzing glacier components provides us with information which is very important in acting as an input parameter for studies related to glacier mass balance, hydrological and climatological modeling and studies related to glacier hazard. Glacier health could be monitored precisely only by a very accurate temporal analysis of the peri-glacial debris (PGD) and supra-glacial debris (SGD) cover.

   Researchers Involved:

1. Rahul Nijhawan

Videos are omnipresent in every area from education, entertainment, surveillance, etc. Having large amount of video data imposes restriction on the time required for consumption. Video summarization is the process of generating an abridged version of the given video which encompasses the summary of the video and is of shorter duration.

   Researchers Involved:

1. VivekRaj

Human activity recognition is the automatic recognition of human activities in videos. It has wide variety of applications in security surveillance, healthcare, sports and human computer interaction. Hence, there is a need to develop automatic recognition systems using computer vision and machine learning based techniques. Theses algorithm are based on features extracted using handcrafted or state-of-the-art deep learning based techniques. The input modalities can be either RGB, depth or IR videos, while the output is class/activity label.

   Unsupervised Learning for Activity Recognition

Over the past couple of years, the field of human activity recognition has made significant progress.The credit of which goes largely to the appearance of bigger datasets (Activity Net etc.) and Deep Learning. The applications of activity recognition are huge, to name a few – video summarization, human behavior analysis, navigation and environmental reconnaissance, elderly health care etc. However, the problem of monocular camera based activity recognition is far from solved, and it is specially challenging to provide unsupervised models for activity recognition. Also, the availability of large number of charge-coupled cameras and unlabeled videos motivates the field of unsupervised activity recognition.

   Researchers Involved:

1. Himanshu Buckchash

Dynamic texture (DT) is an extension of texture to the temporal domain. DT has two types of information: texture and dynamism. that why in DT, recognition involves analyzing the spatio-temporal domain instead of analyzing each frame separately. Therefore feature descriptors should have the caliber to combine robustness, descriptive power and computational efficiency along with motion for efficiently recognizing

   Researchers Involved:

Cloud computing provides cost effective pay-as-you-go services with business continuity. Users are relieved from infrastructure maintenance responsibilities and can accomplish the services directly offered by cloud service providers at reasonable costs. Consequently, new cloud enabled gadgets are evolving nowadays. Regardless of these benefits, users are always concerned about moving their data to the cloud. Hence, new techniques are required to process user's personal images/videos/data without revealing any information and falls in the area of Privacy Preserving Cloud Computing/Encrypted Domain Processing. Cloud computing is an economically and computationally efficient Information Technology paradigm to provide online data processing paid services. The data can be user's personal images/videos or other confidential documents which user wants to process without revealing any information to the cloud service provider. This user's concern of data privacy is addressed by techniques of Privacy Preserving Cloud Computing in which all data processing algorithms are performed over Encrypted form of data.

   Researchers Involved:

1. Vishesh Kumar Tanwar
2. Srishti Yadav

Medical Image Processing is an emerging field and plays a key role in segmentation and classifying region of interest (ROI) from bio-medical images. Medical image processing in itself is a broad area of research which has matured using varous methods of diagnosis/screening which could contain x-rays, computer tomography (CT), positron emission tomography(PET), ultrasound (making use of sound waves), magnetic resonance imgaing (MRI) and much more. With the developement of these softwares, detecting and anlysing disesase to their very basic level has now bwwn made possible. This has helped in clear classification and segmentation of any disease.

   Medical Image Segmentation and Classification

Image segmentation and classification plays an important role in many applications of medical-imaging by automating or facilitating the delineation of the regions of interest and their classification. Our aim is to provide a semi-automated or automated system to delineate and classify the brain stroke lesion from CT/MR scan images. Brain strokes are mainly of two types- hemorrhagic and ischemic. Hemorrhagic stroke occurs when a weakened blood vessel ruptures and iscehmic stroke occurs when there is an interruption of blood supply to the brain. Therefore, for their treatment accurate segmentation and classification system should exist.

   Researchers Involved:

1. Ridhi Arora
2. Rahul Kumar
3. Kishore Babu
4. Anshul Pundhir

Emotion AI or Affective Computing is an interdisciplinary area combining Cognitive Science and Artificial Intelligence. It deals with gathering the data from voices, faces and body language to identify, understand, process and replicate human affects. The aim is to develop computational systems that could respond according to an end user's state of mind, as well as to understand more about human mind. Technologies like microphones, cameras, sensors, motion trackers, EEG, machine learning, deep learning, optimization, big data, etc. are being utilized in this area.

   Researchers Involved:

1. Puneet Kumar
2. Deepak Kumar

Biometrics is the study of anatomical or behavioral features of living beings for the purpose of their automatic identification. A generic biometric system has numerous vulnerabilities and security issues associated with it. From intrinsic failures to identity theft, biometric systems can be subjected to a wide variety of adversarial attacks. As a countermeasure, Biometric Template Protection (BTP) schemes ensure the required guarantees of privacy and security to the end users. The aim of this research problem is to design effective BTP schemes which simultaneously provide robust guarantees of minimum information leakage, unlinkability, revocability, irreversibility and performance.

   Researchers Involved:

1. Debanjan Sadhya

   Visibility Enhancement of Weather Degraded Images and Videos

Weather degradation such as haze, fog, mist, etc. severely reduces the effective range of visual surveillance. This degradation is a spatially varying phenomena, which makes this problem non trivial. Weather effects mitigation is an essential preprocessing stage in applications such as long range imaging, border security, intelligent transportation system, etc.

   Researchers Involved:

1. Rahul Kumar
2. Gyanendra

  Large Scale Reconstruction from multiple unordered images using Structure from Motion algorithms.

The essence of an image is a projection from a 3D scene onto a 2D plane, during which process the depth is lost. The 3D point corresponding to a specific image point is constrained to be on the line of sight. From a single image, it is impossible to determine which point on this line corresponds to the image point. If two images are available, then the position of a 3D point can be found as the intersection of the two projection rays. This process is referred to as triangulation. The key for this process is the relations between multiple views which convey the information that corresponding sets of points must contain some structure and that this structure is related to the poses and the calibration of the camera.

   Researchers Involved:

1. PhoolPreet

  Biped Robot Trajectory Planning

The current research focuses on the following Kinematics, Dynamics and Control aspects of Biped Robot Trajectory Planning: Biped Robot Trajectory Planning for Uneven Ground, Artificial Neural Network based control in Biped Robots, Energy Efficient Gait and Control of Biped Robots, Impedance Control for Biped Robots.

   Researchers Involved:

1. Gaurav Bhardwaj

   Hyperspectral Imaging

Hyperspectral imaging (HSI) is a technique that analyses a wide spectrum of light instead of just assigning primary colours (red, green, blue) to each pixel. The light striking each pixel is broken down into many different spectral bands to provide more information on what is imaged. The complete information is stored in the shape of a hypercube of dimensions of x * y * λ, where x and y are the spatial coordinates and λ is the spectral coordinate. This technique is widely used in remote sensing, seed viability study, biotechnology, environmental monitoring, food, pharmaceuticals, medical diagnose etc.

   Researchers Involved:

1. Nitin Tyagi

   Remote Sensing & GIS

Remote sensing is the science of obtaining the physical properties of an area without being there. It allows users to capture, visualize, and analyze objects and features on the Earth’s surface. By collecting imagery, we can classify it into land cover and other types of analyses.

   Researchers Involved:

1. Pooja Dhayal
2. Amit Kumar Singh

   Reliability and Life Testing, Bayesian Inference

Reliability is usually defined as the probability that a system will perform its intended function for a specified period of time under a set of specified environmental conditions: Generally, reliability requirements are concerned with making a system failure-free. In today's most demanding and competitive business supermarket, It becomes necessary for manufacturers to provide warranty and reliability of their items to the consumers. In order to design a cost-effective warranty, a manufacturer must have sound knowledge about product failure time distributions. To gain this knowledge, life testing and reliability experiments are carried out before (and while) products are put on the market. Some of these areas are engineering design, safety/risk analysis, environmental protection, quality/reliability management, verification, and optimization of maintenance and operation. In the last few years of the twentieth century, there has been remarkable progress in applying reliability principles in industries and government departments in almost all developed and developing countries. Today, reliability has become a catchword in many firms. Many statistical methods have been developed to precisely estimate reliability functions during the last few decades. There are several lifetime distributions that are useful in modeling lifetime data. Some of these are Kumaraswamy, Maxwell failure, and Weibull distributions.

   Researchers Involved:

1. Shubham Saini

   Earthquake Prediction

Earthquake parameters plays vital role to monitor effects of seismic activity and response of ground movements around geotechnical structures. The study involves collaborating various fields such as machine learning, seismic signal processing using deep learning, and computer vision with earthquake engineering. The earthquake parameters are predicted using the above methodologies and their comparison with current state of the arts is performed.

   Researchers Involved:

1. Anushka Joshi

   Geometric Deep Learning

Geometric deep learning is a domain in contemporary research, melding the intricacies of differential geometry with deep learning. It extends the paradigm of deep learning to non-Euclidean spaces, such as manifolds and complex network structures, facilitating the processing and interpretation of data that defies conventional, flat geometric constraints. By leveraging the intrinsic geometric properties inherent in data, this field enables nuanced analysis of high-dimensional data, uncovering latent patterns through topological and differential methodologies. Dynamical systems theory further enriches this landscape, introducing a temporal dimension that models the evolution of complex systems over time. This interdisciplinary approach not only enhances the theoretical framework of neural networks but also propels practical applications forward in areas ranging from quantum mechanics to neuroinformatics, and increasingly, in the complex and dynamic world of finance, embodying a pivotal shift in the paradigm of computational intelligence and data science.

   Researchers Involved:

1. Dr. Pradeep Singh

   Deep Spatial Computing

Deep Spatial Computing is a dynamic field that melds the intricacies of 3D modeling, artificial intelligence, and deep learning to revolutionize our interaction with digital and physical environments. At its core, this domain leverages the robust capabilities of deep neural networks for sophisticated scene reconstruction, object recognition, and predictive analytics. This innovative blend facilitates more natural and intuitive interactions within spatial contexts, significantly impacting various sectors such as healthcare, urban planning, entertainment, and education. It stands at the forefront of technological evolution, offering novel, efficient, and personalized experiences in a digitally interconnected world.

   Researchers Involved:

1. Sandeep Sharma