ScholarSphere: IITJ Research Insights Hub
ScholarSphere: IITJ Research Insights Hub is to preserve and enable easy access to the Intellectual output of its faculty members, such as Journal Papers, Conference Papers, Books, Book Chapters, Reports and Preprints to the research community.
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- PublicationA novel predictor coefficient interpolation algorithm for enhancement of spatial resolution of images(2010)This paper presents a novel algorithm for enhancement of spatial resolution of images. The proposed algorithm estimates a Least square based predictor of lower order and interpolates the coefficients of higher order predictor. We have reduced the predictor order form p to (p-1) that results into a saving of computational power. The proposed algorithm is generic that can be used with most of the LS based interpolation algorithms reported in literature. We have shown that use of interpolated prediction coefficient causes insignificant loss in subjective as well as objective (PSNR) quality of the higher resolution (HR) image as compared with the PSNR obtained by the actual prediction coefficient and there is around 40% to 50% reduction in computational complexity.
- PublicationFeedback fiber delay lines and AWG based optical packet switch architecture(2010-04)All-optical switching has been proposed as a candidate to allow high capacity networking in the future. However, most of the proposed all optical switching architectures have a large physical loss, and hence, their cascadabilty is limited. This paper proposes an optical packet switch (OPS) which has very low loss. The architecture is realized by using components like optical reflectors, tunable wavelength converters (TWCs), arrayed waveguide grating (AWG) and pieces of fiber. This architecture uses the routing pattern of AWG, and its symmetric nature, to simplify switch operation significantly. It is also shown that by using a multi-wavelengths optical reflector, the length of delay lines can be reduced to half of its original value. This reduction in length is useful for larger size packets as their length can grow to more than a few kilometers. The proposed architecture is compared with other approaches that have been published in literature in terms of performance, implementation complexity, number of components used and cost. This paper also proposes a cost model for the cost estimation of the TWCs. The proposed model takes into consideration fiber-to-chip coupling (FCC) and the wavelength speed-up factor (WSU).
- PublicationDark energy and structure formation(2010-01-01)We study the gravitational dynamics of dark energy configurations. We report on the time evolution of the dark energy field configurations as well as the time evolution of the energy density to demonstrate the gravitational collapse of dark energy field configurations. We live in a Universe which is dominated by Dark Energy. According to current estimates about 75% of the Energy Density is in the form of Dark Energy. Thus when we consider gravitational dynamics and Structure Formation we expect Dark Energy to play an important role. The most promising candidate for dark energy is the energy density of fields in curved space-time. It therefore become a pressing need to understand the gravitational dynamics of dark energy field configurations. We develop and describe the formalism to study the gravitational collapse of fields given any general potential for the fields. We apply this formalism to models of dark energy motivated by particle physics considerations. We solve the resulting evolution equations which determine the time evolution of field configurations as well as the dynamics of space-time. Our results show that gravitational collapse of dark energy field configurations occurs and must be considered in any complete picture of our universe.
- PublicationThe novel aggregation function-based neuron models in complex domain(2009-08)The computational power of a neuron lies in the spatial grouping of synapses belonging to any dendrite tree. Attempts to give a mathematical representation to the grouping process of synapses continue to be a fascinating field of work for researchers in the neural network community. In the literature, we generally find neuron models that comprise of summation, radial basis or product aggregation function, as basic unit of feed-forward multilayer neural network. All these models and their corresponding networks have their own merits and demerits. The MLP constructs global approximation to input-output mapping, while a RBF network, using exponentially decaying localized non-linearity, constructs local approximation to input-output mapping. In this paper, we propose two compensatory type novel aggregation functions for artificial neurons. They produce net potential as linear or non-linear composition of basic summation and radial basis operations over a set of input signals. The neuron models based on these aggregation functions ensure faster convergence, better training and prediction accuracy. The learning and generalization capabilities of these neurons have been tested over various classification and functional mapping problems. These neurons have also shown excellent generalization ability over the two-dimensional transformations.
- PublicationMetallurgical aspects of high entropy alloys(2024)Alloying traditionally enhances material properties by adding small amounts of secondary elements to a primary base. Over the past decade and a half, a revolutionary strategy has emerged: combining multiple principal elements in high concentrations to create high-entropy alloys (HEAs). This approach unlocks a vast, largely unexplored compositional space, leading to the discovery of alloys with exceptional properties. This work provides a comprehensive review of recent advancements in HEA research, focusing on metallurgical aspects, highlighting key findings, and identifying future research directions. We reviewed the design philosophy of conventional alloys and its transition to HEAs, emphasizing metallurgical differences and similarities. Key topics include the fundamentals of high-entropy alloys design for variety of HEAs, such as equiatomic and non-equiatomic, eutectic, metastable, refractory, lightweight, spinodal HEAs, and high-entropy superalloys. The review explores microstructural features of HEAs, including as-cast wrought and additively manufactured morphologies, texture development, precipitation, and dispersion-based microstructures. The design of HEAs involves understanding and manipulating these microstructural characteristics to achieve desirable properties. Metallurgical properties of HEAs are summarised, including tensile and compressive properties, fatigue properties, creep, superplastic, corrosion behavior and weldability. Typical deformation mechanisms such as slip, twinning, twinning-induced plasticity, transformation-induced plasticity, and precipitation-assisted deformation, which are found to be mainly active in HEAs, are also discussed. Despite the progress made, the potential of HEAs is far from fully realized. Future directions conceive the concept of high-entropy conventional alloys (HECAs), which merge the high-entropy effect in HEAs with the conventional alloy design approach, thereby introducing high entropy phases in the conventional alloy matrix or vice-versa. Thus, the novel concept of HECA provides a future pathway to materials design and foster enhanced metallurgical properties utilizing the HEA concept for practical applications. To explore the extensive compositional and microstructural possibilities of HEAs and HECAs, high-throughput experimental techniques and computational methods are essential. This review aims to serve as a valuable resource for new researchers and provides insights to guide future investigations in the field of high-entropy alloys and high-entropy conventional alloys, with a strong emphasis on their metallurgical aspects.
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