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Institution

Indian Institute of Technology Bombay

EducationMumbai, India
About: Indian Institute of Technology Bombay is a education organization based out in Mumbai, India. It is known for research contribution in the topics: Population & Thin film. The organization has 16756 authors who have published 33588 publications receiving 570559 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, an approach for the detection of stationary obstacles and moving objects on the road of an autonomous vehicle by evaluation of optical flow fields from image sequences is presented, which can be interpreted to infer information about the three-dimensional environment if the camera is moving on a planar surface.

188 citations

Journal ArticleDOI
TL;DR: In this article, tensile specimens of Type 316L stainless steel having grain sizes in the range 3.1-86.7 μm were deformed to 34% strain at temperatures 24, 400 and 700°C and strain rate 1 × 10−4s−1 to investigate the Hall-Petch (H-P) relationship, the nature of stress-strain curves and the substructure development.
Abstract: Tensile specimens of Type 316L stainless steel having grain sizes in the range 3.1–86.7 μm were deformed to 34% strain at temperatures 24, 400 and 700°C and strain rate 1 × 10−4s−1 to investigate the Hall-Petch (H-P) relationship, the nature of stress-strain curves and the substructure development. Upto ∼5% strain the H-P relationship exhibits bi-linearity whereas the single Hall-Petch relation is exhibited at larger strains. The presence of bi-linearity is explained by the back stress associated with the difference in the dislocation densities in the vicinity of grain boundary and in the grain interior. The log stress (σ)-log strain (e) plots depict three regimes and follow the relationship σ = Ken in each regime, but with varying magnitudes of the strength coefficient (K) and strain-hardening exponent (n).

187 citations

Journal ArticleDOI
TL;DR: Both the magnetic and XRD data support the substitution of Mn3+ ions at the tetrahedral site of γ-Fe2O3 and suggest that the suspension of these particles may be evaluated for magnetic hyperthermia treatment of cancer.
Abstract: Magnetic nanoparticles of γ-MnxFe2–xO3 (0 ≤ x ≤ 1.3) have been synthesized successfully using a single step process wherein the respective inorganic salts were thermally decomposed in ethylene glycol at 200 °C. Single phase formation is evident in the as prepared dried samples without any further treatment. XRD line broadening along with TEM suggest that the particle size is below 30 nm. Both the magnetic and XRD data support the substitution of Mn3+ ions at the tetrahedral site of γ-Fe2O3. Improved magnetization value (78 emu g–1) is obtained for the sample with x = 0.2 compared to one with x = 0 (62 emu g–1) if measured at 20 kOe. Aqueous suspensions of the sample with x = 0.2 were prepared using a polymer, Acrypol 934, with an aim to examine the possible use of these suspensions in the magnetic hyperthermia treatment of cancer. These suspensions were found to be biocompatible at concentration as high as 3.75 mg mL–1 of culture media. Hyperthermia induced by the application of an AC magnetic field in the presence of the above suspension caused HeLa cell death. The cell death was found to be proportional to the quantity of the particles and the duration of application of the AC magnetic field. Following hyperthermia treatment, cells showed varying degrees of membrane blebbing with significant disruption of the actin and tubulin cytoskeletons. The apparent disruption of the actin and microtubule cytoskeletons of cells might be responsible for the death of cells following hyperthermia treatment. These observations suggest that the suspension of these particles may be evaluated for magnetic hyperthermia treatment of cancer.

187 citations

Journal ArticleDOI
01 Jun 1999
TL;DR: A notion of quasi-succinctness is introduced, which allows a quasi-Succinct 2-var constraint to be reduced to two succinct 1-var constraints for pruning, and a query optimizer is proposed that is ccc-optimal, i.e., minimizing the effort incurred w.r.t. constraint checking and support counting.
Abstract: Currently, there is tremendous interest in providing ad-hoc mining capabilities in database management systems. As a first step towards this goal, in [15] we proposed an architecture for supporting constraint-based, human-centered, exploratory mining of various kinds of rules including associations, introduced the notion of constrained frequent set queries (CFQs), and developed effective pruning optimizations for CFQs with 1-variable (1-var) constraints.While 1-var constraints are useful for constraining the antecedent and consequent separately, many natural examples of CFQs illustrate the need for constraining the antecedent and consequent jointly, for which 2-variable (2-var) constraints are indispensable. Developing pruning optimizations for CFQs with 2-var constraints is the subject of this paper. But this is a difficult problem because: (i) in 2-var constraints, both variables keep changing and, unlike 1-var constraints, there is no fixed target for pruning; (ii) as we show, “conventional” monotonicity-based optimization techniques do not apply effectively to 2-var constraints.The contributions are as follows. (1) We introduce a notion of quasi-succinctness, which allows a quasi-succinct 2-var constraint to be reduced to two succinct 1-var constraints for pruning. (2) We characterize the class of 2-var constraints that are quasi-succinct. (3) We develop heuristic techniques for non-quasi-succinct constraints. Experimental results show the effectiveness of all our techniques. (4) We propose a query optimizer for CFQs and show that for a large class of constraints, the computation strategy generated by the optimizer is ccc-optimal, i.e., minimizing the effort incurred w.r.t. constraint checking and support counting.

187 citations


Authors

Showing all 17055 results

NameH-indexPapersCitations
Jovan Milosevic1521433106802
C. N. R. Rao133164686718
Robert R. Edelman11960549475
Claude Andre Pruneau11461045500
Sanjeev Kumar113132554386
Basanta Kumar Nandi11257243331
Shaji Kumar111126553237
Josep M. Guerrero110119760890
R. Varma10949741970
Vijay P. Singh106169955831
Vinayak P. Dravid10381743612
Swagata Mukherjee101104846234
Anil Kumar99212464825
Dhiman Chakraborty9652944459
Michael D. Ward9582336892
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023175
2022433
20213,013
20203,093
20192,760
20182,549