Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

Crowd-sourcing of information about various aspects of a road is an important mechanism in Intelligent Transportation Systems (ITS). In this work, we consider crowd-sourcing from public transportation commuters, information about the road, traffic, and the specific public transportation unit. Any crowd-sourced information has to be tagged with the information provider's location. Since GPS-based location determination is energy-expensive, we focus on GSM signal based location determination. A specific dimension we explore, not considered in prior work, is the use of GSM signal information from multiple commuters' phones. This consideration is triggered by the observation that a large set of commuters have (almost) the same physical location for large durations of time: when they share the same bus. We present an analysis of data collected from two different Indian cities: Mumbai and Chandigarh. We find that such combination of information can lower the median location error by a factor of 2-10. © 2015 IEEE.

World University root node

2015 7th International Conference on Communication Systems and Networks, COMSNETS 2015 - Proceedings

GSM-based positioning for public transportation commuters

Social network data has been modeled with several approaches, including Sociogram and Sociomatrices, which are popular and comprehensive. Similar to these we have developed here a novel modeling technique based on granular computing theory and fuzzy neighborhood systems, which provides a uniform framework to represent social networks. In this model, a social network is represented with a collection of granules. Fuzzy sets are used for defining the granules. The model is named Fuzzy Granular Social Network (FGSN). Familiar measures of networks viz. degree, betweenness, embeddedness and clustering coefficient are redefined in the context of this new framework. Two measures, namely, entropy of FGSN and energy of granules are defined to quantify the uncertainty involved in FGSN arising from fuzziness in the relationships of actors. Experimental results demonstrate the applicability of the model in two well known problems of social networks, namely, target set selection and community detection with comparative studies. © 2015 Elsevier Inc.

Information Sciences

FGSN: Fuzzy Granular Social Networks - Model and applications

The performance of iris recognition reduces when the images are captured at a distance. However, such images generally contain periocular region which can be utilized for person recognition. In this research, we propose a novel context switching algorithm that dynamically selects the best descriptor for color iris and periocular regions. Using predefined protocols, the performance of the proposed algorithm is evaluated on UBIRIS V2 and FRGC datasets, and the results show improved performance compared to existing algorithms. © 2015 IEEE.

2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015

Person identification at a distance via ocular biometrics

Harnessing Auxiliary Information : New Methods to Improve Person Identification

The mismatch between the training data and the test data distributions is a challenging issue while designing many practical computer vision systems. In this paper, we propose a domain adaptation technique to tackle this issue. We are interested in a domain adaptation scenario where source domain has large amount of labeled examples and the target domain has large amount of unlabeled examples. We align the source domain subspace with the target domain subspace in order to reduce the mismatch between the two distributions. We model the subspace using Locality Preserving Projections (LPP). Unlike previous subspace alignment approaches, we introduce a strategy to effectively utilize the training labels in order to learn discriminative subspaces. We validate our domain adaptation approach by testing it on two different domains, i.e. handwritten and printed digit images. We compare our approach with other existing approaches and show the superiority of our method. © 2015 IEEE.

ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition

Domain adaptation by aligning locality preserving subspaces

Sensor network deployed for the critical infrastructure monitoring requires high degree of reliability in sensory data gathering, in spite of arbitrary node or sink failures. This paper proposes a robust data gathering scheme specially designed to provide guaranteed delivery of the sensory data for applications on the critical infrastructure monitoring. Redundancy in a sensor network, in terms of both the number of deployed sensors and the amount of duplicate data delivery, is explored to design an effective protocol that ensures the reliable data delivery while assuring the timeliness, connectivity and the sensing coverage. A set of active sensors is selected from all the sensors deployed, based on the network connectivity and the sensing coverage criteria that participates in the data forwarding process. Rest of the sensors go to the sleep state, and act as a replacement on the failure of an active sensor. The proposed protocol aims to find out multiple node-disjoint paths to multiple sinks, so that the loss of connectivity in one path due to node failure does not disrupt application services. The effectiveness of the proposed scheme has been analyzed using simulation results, and compared with other protocols proposed in the literature for reliable data delivery. © 2015 Elsevier Ltd. All rights reserved.

Journal of Network and Computer Applications

Fault resilience in sensor networks: Distributed node-disjoint multi-path multi-sink forwarding

In authenticated encryption schemes, there are two techniques for handling long ciphertexts while working within the constraints of a low buffer size: Releasing unverified plaintext (RUP) or Producing intermediate tags (PIT). In this paper, in addition to these two techniques, we propose another way to handle a long ciphertext with a low buffer size by storing and releasing only one (generally, or only few) intermediate state, without releasing or storing any part of an unverified plaintext and without need of generating any intermediate tag. In this paper we explain this generalized technique using our new construction sp-AELM. sp-AELM is a sponge based authenticated encryption scheme that provides support for limited memory devices. We also provide its security proof for privacy and authenticity in an ideal permutation model, using a code based game playing framework. Furthermore, we also present two more variants of sp-AELM that serve the same purpose and are more efficient than sp-AELM. The ongoing CAESAR competition has 9 submissions which are based on the Sponge construction. We apply our generalized technique of storing single intermediate state to all these submissions, to determine their suitability with a devices having limited memory. Our findings show that only ASCON and one of the PRIMATE’s mode(namely GIBBON) satisify the limited memory constraint using this technique, while the remaining 8 schemes (namely, Artemia, ICEPOLE, Ketje, Keyak, NORX, Π-cipher, STRIBOB and two of the PRIMATEs mode: APE & HANUMAN) are not suitable for this scenario directly. © Springer International Publishing Switzerland 2015.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Sp-AELM: Sponge based authenticated encryption scheme for memory constrained devices

Password Hashing is the technique of performing one-way transformation of the password. One of the requirements of password hashing algorithms is to be memory demanding to provide defense against hardware attacks. In practice, most Cryptographic designs are implemented inside a Cryptographic module, as suggested by NIST in a set of standards (FIPS 140). A cryptographic module has a limited memory and this makes it challenging to implement a password hashing scheme (PHS) inside it. In this work, we propose a novel approach to allow a limited memory cryptographic module to be used in the implementation of a high memory password hashing algorithm. We also analyze all the first round entries of the Password Hashing Competition (PHC) to evaluate the suitability of the submitted algorithms to be implemented with a Cryptographic module. We graphically show that the submissions to the PHC can be securely implemented in a crypto-module following our suggestion. To the best of our knowledge, this is the first attempt in the direction of secure implementation of password hashing algorithms. © Springer International Publishing Switzerland 2015.

Cryptographic module based approach for password hashing schemes

Abstract: Empirical stochastic multi-variable models for prediction of treatment efficiency of wetlands are presented in this article. Wetlands of seven different shapes are visualized using tracer studies. Two different variants of experiments are carried out. Numerous flow rate variations are performed keeping surface area of the wetland constant. The experiment is also carried out with a variation in volume of the wetland which helps to study the effect of flow height on the hydrodynamics within the wetland. A multi-variable model for treatment efficiency in terms of change in tracer concentration as a function of shape, volumetric height of water within the wetland, time, and mass flow rate is considered. Further, another set of experiments is performed studying the treatment efficiency in terms of electro-kinetic parameters. This involves measuring the pH, turbidity, temperature, electrical conductivity, total dissolved salts at inlet and outlet and residence time with varying flow rate, and height of water for the seven different wetland models under study. The electro-kinetic parameters changes due to difference in concentration of the tracer dye which simulates impurities. In this case, treatment efficiency is expressed as a function of the above-discussed electro-kinetic variables, time variation, water height, as well as variation in the mass flow rate. The stochastic multi-parameter models, thus, empirically derived in the above two cases have high coefficient of determination. The models thus derived may be used as a tool for quick analysis of treatment efficiency of any shape and size of a three-dimensional wetland. © 2014 Balaban Desalination Publications. All rights reserved.

Department of Mechanical Engineering

Desalination and Water Treatment

Multi-variable approach to determine treatment efficiency of wetland: size effect and electro-kinetic effects

We address the problem of image retrieval using textual queries. In particular, we focus on descriptive queries that can be either in the form of simple captions (e.g., \a brown cat sleeping on a sofa"), or even long descriptions with mul-tiple sentences. We present a probabilistic approach that seamlessly integrates visual and textual information for the task. It relies on linguistically and syntactically motivated mid-level textual patterns (or phrases) that are automati-cally extracted from available descriptions. At the time of retrieval, the given query is decomposed into such phrases, and images are ranked based on their joint relevance with these phrases. Experiments on two popular datasets (UIUC Pascal Sentence and IAPR-TC12 benchmark) demonstrate that our approach effectively retrieves semantically mean-ingful images, and outperforms baseline methods. © 2015 ACM.