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The broad area of faculty research interest, Spatial Computing, includes Computational Geometry, Spatially-Aware Recommender Systems, Location Based Services, Spatial Feature Extraction and Geographical Opportunistic Routing.
In Computational Geometry, research is carried out in the design and analysis of efficient data structures and algorithms for fundamental geometric problems. Algorithms and data structures for various query-retrieval problems defined on geometric objects that come aggregated in disjoint groups are important since such problems arise in diverse applications such as, VLSI layout verification, retrieval of data from spatial/ text/ financial databases, facilities location, sensor networks, and network traffic analysis. Each group may be viewed as consisting of simple geometric objects, where the grouping is dictated by the underlying application. The goal is to pre-process these groups into a data structure so that useful questions about the groups, relative to a query object, can be answered efficiently. Another class of problems we are looking at recently is range-aggregate queries. In a generic instance of such a problem we need to preprocess a set S of geometric objects such that given a query orthogonal range Q, the result of applying an aggregation function on the objects of S in a range Q can be reported efficiently.
Recommender Systems make use of the “wisdom of the crowd” to help users identify useful items from a large search space. A common technique used by many recommender systems is collaborative filtering, which analyses past community opinions to find correlations between similar users and items to suggest personalised items to a query user. Today, recommender systems need to handle increasingly large data sets of items and users. To make collaborative filtering scalable, we have explored various strategies to partition the user and item space. Partitioning strategies include Voronoi diagrams, k-d trees, quadtrees, social graph partitioning and partitioning based on context. Once the data is partitioned, the recommendation algorithm is applied independently in the disjoint partitions. As an insurance against degradation of quality of recommendations, our partitioning strategies adapt to the spatial autocorrelation in the underlying space.
Location Based services (LBS) refer to applications that integrates the knowledge of the geographical location of a mobile device with other information to provide various services. Such applications include navigation, tracking of vehicles, children and elderly, local search, city guides, weather updates, restaurant and ATM finders, location analytics for businesses or location based mobile advertising. Successful provisioning of LBS was hindered in the past due to infrastructure constraints. However a lot of progress on all these fronts over the last few years have resulted in increased interest in location and location based services. Location-dependent queries are not only a fundamental building block of LBS but also probably one of the most challenging issues in supporting them on mobile devices. In this project, we study the problems related to location dependent query processing on road networks.
Spatial feature extraction from images and videos is another area of interest. Lung cancer detection is based on small white spots called lung nodules. Each of them has certain geometric features, upon which the nodules can be classified into cancerous and noncancerous. An automated detection is needed for faster detection of malignant and benign lung nodules based on the shape features of the nodules. Spatial feature extraction from images of faces is used for emotion recognition. Facial expression, facial muscle movements, wrinkles, temporary deformation of facial features (e.g. eyebrow, tightening of lips, etc.) help classifying emotions. Feature extraction from videos of individuals is used for hand gesture recognition. This includes hand region detection and tracking in video and gesture end-point detection for dynamic gestures. Spatial feature extraction from videos of moving vehicles is used for surveillance and security.
Another area of research is in Routing algorithms in Vehicular Ad-Hoc Networks (VANETs). Within this, one of problems of interest is geographical opportunistic routing. In this case, messages are transmitted based on the geographical location (coordinates) of the nodes. This involves communication between vehicles and with infrastructure. A moving vehicle collaborates with selected vehicles in its neighbourhood to communicate with desired vehicles which are further away. Selection of intermediate nodes for the purpose of message transmission based on nearest neighbors is for an example an important problem.
NU is engaged in original, cutting-edge research in communications and networks. The current research focus is on wireless broadband communications, vehicular communications, cognitive radio, communication security, speech signal processing, optics & optoelectronics and mobile health care.
During the last decade, Vehicular Ad-hoc NETworks (VANETs) have emerged with very high potential to make the Vehicle-to-Vehicle and Vehicle-to-Roadside wireless communications a feasible and deployable technology for the current and next-generation automobiles. By offering real-time traffic information, collision-avoidance assistance, and emergency incident notification, V2V communications can help drivers make more intelligent decisions with respect to their surroundings, thereby increasing their overall safety levels and efficiency of the roads and highways. At NU, a dedicated team of researchers is conducting research in the field of vehicular communications The research agenda includes exploration of different physical, contextual and environmental parameters involved in the process of communications in order to provide a reliable communication link, ensure communication security and detect and report spurious messages.
Cognitive Radio Networks (CRN) are expected have the potential for providing mobile broadband service with the expected high bandwidth aggregate traffic, through opportunistic spectrum access of the licensed spectrum band with simpler architectures and greater flexibility than the conventional wireless networks architecture. The research on CRN envisages a comprehensive approach to the design issues related to architectural planning, physical layer alternatives and their impact, medium access protocols and network layer processing including dynamic spectrum coordination, energy efficiency, individual and group mobility support, security in communications and broadband service provisioning on low-power flexible cognitive radio platform.
Mobile and pervasive environments built over wireless infrastructures have introduced new possibilities in the healthcare sector. Future healthcare systems will use fourth generation (4G) mobile broadband technologies like LTE and WiMAX to perform round-the-clock mobile health monitoring, remote tests, physician consultations and diagnosis with high resolution image, audio and video transfer and other complex operations in a mobile environment. At NU, mobile healthcare research opportunities include research and development for health monitoring, security/ privacy of health data and health data management in various mobile platforms.
Wireless sensor networks (WSN) have made inroads to virtually every corner of our life. WSNs have received significant attention due to their widespread application in civilian and military operations. Recent advances in wireless communication and microelectronics have led to the development of low-cost & low-power sensor nodes. At NU, applied research using WSN is conducted for various applications like environmental monitoring, energy management and smart home systems.
Pollution of groundwater and soil is a worldwide problem that has resulted in uptake and accumulation of toxic chemicals in food chains and harmed the flora and fauna of the affected habitats. Persistent organic pollutants (POPs, carcinogenic polycyclic aromatic hydrocarbons (PAHs) and pesticides) and metals are resistant to environmental degradation through chemical, biological, and photolytic processes. The persistence of POPs and metals in the environment leads to bioaccumulation in human and animal tissue and biomagnification in food chains and therefore has potential significant impact on human health and the environment.
The ongoing research at NIIT University (NU) has led to the development of microbiological solutions for the bioremediation of soils contaminated with chlorinated pesticide endosulfan and chlorpyrifos. Further, using molecular tools, the metabolic fate of these chlorinated pesticides has been elucidated and the decontaminated potential of the microbes has been demonstrated.
The increasing heavy metal pollution has posed a major threat to the health of life forms including humans. Metals such as lead, nickel and cadmium are extremely toxic. The intracellular accumulation is desirable for metal removal. However, metal toxicity and cell death have to be avoided and that is to be done by over-expressing metal binding and metal sequestering protein. A microbial method for the treatment of heavy metal containing liquid effluents is proposed wherein metal hyper accumulator bacteria will be generated through genetic and biochemical modifications. To fulfil this, seven bacteria were isolated from metal contaminated site. On the basis of 16S rDNA sequence relatedness, these were identified as Enterococcus sp. These isolates were found to be tolerant to multiple metals like Ni, Zn, Cd, Pb, Al, Fe, Mn, Cu, Co and Hg. Growth profiles showed that these isolates tolerate Ni (50 ppm), Pb (1000 ppm), Cd (70 ppm) and Al (150 ppm).
Biofuels produced from renewable resources are increasingly attracting attention as public concerns about global warming and energy security have been growing worldwide. Among biofuels, butanol and ethanol are currently under the spotlight. Bacteria have been engineered to produce ethanol and butanol from cheap carbon sources like lignocellulose compounds present in agricultural residues. Metabolic pathway of Clostridium sp ATCC 824 produces acetone, butanol and ethanol through ABE fermentation naturally. The five genes responsible for the production of butanol in Clostridium sp ATCC 824 was PCR amplified and cloned into E coli pTZ57R/T. Shuttle vector has been constructed to transfer these genes to the Lactobacillus sp. In Lactobacillus sp. lactate dehydrogenase produces lactic acid from glucose and down regulation of this gene will reduce the production of lactic acid. Moreover, the dairy waste which consists of lactose is being utilised as carbon source for the production of butanol. This project is supported by a research grant from the Department of Biotechnology, Govt. of India. The aims of this project are:
1. Biobutanol – The Fuel of the Future: Expression of Butanol coding genes in Lactobacillus. PI Dr. Sunil Khanna. Department of Biotechnology. INR 67.29 lakhs; 2011-2016.
The Green Chemistry research at NU covers a broad range of subjects and includes both fundamental and applied research. We mainly concentrate on developing new applications and offering practical solutions for existing tedious conventional chemical reaction protocols. This includes upgrading of the processes, both in terms of their environmental performance and efficiency, through the application of catalysis, alternative solvents and reagents.
We also have a special interest in the designing of novel nanomaterial via deposition / immobilization / stabilization of various transition metal nanoparticles (e.g. Fe, Co, Pd, Cu, Ru) on Task-Specific Ionic Liquids (TSILs) as environmentally compatible supports. Unlike conventionally synthesized nanoparticles, supports offer stabilization (additional stability to nanoparticles) and control (controllable particle size and potentially shapes) as well as the improved material reusability required for tedious organic transformation process and biologically active molecule synthesis.
Selective Hydrogenation of CO2 by Task Specific Ionic Liquid Supported Ru on TiO2, Department of Science and Technology; INR 23 lakhs; 2013-16
Research in Nanoscience and Nanotechnology is focused on the following aspects.
Considerable progress has been made in the synthesis of low dimensional semiconductor nanostructures. In majority of the published reports, nanostructures have been functionalised with only one of the desired functions say optical emission, polarisability, mechanical strength, magnetic response, bio sensing etc. Ultra small heterostructures formed by using two or more materials can however be designed for multi-functionality. Such heterostructures offer greater flexibility in the choice of materials parameters and targeted applications in nanodevices. For example, the electronic levels in the constituents can be tailored by their sizes and choice of the components while simultaneously providing for effective surface passivation. One can thus generate families of hybrid nanostructures using organic, inorganic and biological materials. The resulting properties are expected to be combinations of the properties of the constituents, which indeed is enticing for further study. Controlled growth of these complicated structures, however, remains a critical challenge.
Research efforts on the fabrication, characterization and application of some novel hybrid core-shell nanostructures are being directed for possible applications in drug delivery, medical diagnostics, sensing and photovoltaic applications.
Optical nanoantennas are optical counterparts of the well-known microwave antenna. Nanoantennas have aroused global scientific and technological interest as important devices for converting electromagnetic radiation into confined/enhanced fields at nano scale. The recent advances in resonant sub-wavelength optical antennas have now offered researchers a continuum of electromagnetic spectrum—from radio frequencies all the way up to X-rays—to design, analyze and predict new phenomena that were previously unknown. Their applications in sensing, imaging, energy harvesting, and drug delivery, diagnostic and prevention have brought revolutionary improvements. Scientific challenges being addressed include fundamental understanding of the underlying physics, development of the self-assembled nano antenna arrays at relatively low cost and high throughput beyond the diffraction limit.
Small size, large surface area and charge on the surface are associated with toxicity of nanoparticles. Titanium dioxide is among one of the nanoparticles which is receiving increasing attention for a large variety of applications like plastics, paper, toothpastes, dental fillings, photovoltaic cells, beauty products, sunscreens and textiles. The increasing production and use of titanium dioxide nanoparticles (NP-TiO2) has led to concerns about their possible impact on the environment. Bacteria play crucial roles in ecosystem processes and may be subject to the toxicity of these nanoparticles. Laboratory scale study already showed the toxic effect of TiO2 nanoparticles towards some of the bacteria but fate of these nanoparticles and ecotoxicological effects under natural conditions is still not known. Research is targeted to find out the toxic effects of TiO2 nanoparticles towards microorganisms under natural conditions.
While observing earth from above, lot of research has been done in the past decades to investigate the applicability of multispectral data, and to improve the spatial resolutions of optical sensors. However, we need to have accurate and quantitative information of surface parameters to understand the process involved in the terrestrial ecosystems. The advents of high end computers, large storage capacity and enormous data transmission capability have given rise to Hyperspectral Remote Sensing.
As compared to conventional remote sensing, Hyperspectral sensors acquire data in narrow wavelength bands of width of theorder, 10nm. The 'hyper' in Hyperspectral refers to the large number of measured wavelength bands. Hyperspectral images are spectrally over-determined (i.e. there is correlation between adjacent bands), and they provide ample spectral information to identify and distinguish spectrally unique materials (Shippert, 2008). Thus, data produced by the imaging spectrometers is different from that of the multispectral instruments with regard to the number of wave bands in which data is recorded. The success of Hyperspectral remote sensing mainly depends on the understanding of spectroscopy of various targets of interest in the reflective (0.4-2.5) and emissive domain (3-14), therefore to identify ad classify different snow/ice classes it is highly require to model them in both domains (i.e. reflective and emissive).
Hyperspectral Remote Sensing can be used for the identification and mapping of different snow cover characteristics, such as Reflectance/ Albedo, snow grain size, Moisture content in snow, snow mixed objects study and snow surface temperature, Thermal properties of snow etc. For this, there is a need to carry out the following tasks: Generation on spectral library in reflective and emissive domain, identification of suitable bands for snow characteristics, input for development of snow cover monitoring algorithm, Development of snow indices, etc. The proposed research proposal addressing the Development of methodologies for classification/ identification of snow/ ice/ glacier features based on modeled snow properties in reflective and emissive regions.
The major objective of this research:
1. Modelling the snow properties for their classification and identification; PI Dr. Mohd Anul Haq. Department of Science and Technology. INR 37.36 lakhs; 2016-2019.