Institution
Stevens Institute of Technology
Education•Hoboken, New Jersey, United States•
About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.
Papers published on a yearly basis
Papers
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TL;DR: In this article, Celgard® microporous hydrophobic hollow fibers and flat membranes have been used for extraction/purification of fermentation-based pharmaceutical products for solvent extraction and back extraction using a pH swing procedure.
77 citations
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TL;DR: This paper presents an efficient and privacy-preserving protocol to construct a Bayesian network from a database vertically partitioned among two parties, in this setting, two parties owning confidential databases wish to learn theBayesian network on the combination of their databases without revealing anything else about their data to each other.
Abstract: Traditionally, many data mining techniques have been designed in the centralized model in which all data is collected and available in one central site. However, as more and more activities are carried out using computers and computer networks, the amount of potentially sensitive data stored by business, governments, and other parties increases. Different parties often wish to benefit from cooperative use of their data, but privacy regulations and other privacy concerns may prevent the parties from sharing their data. Privacy-preserving data mining provides a solution by creating distributed data mining algorithms in which the underlying data need not be revealed. In this paper, we present privacy-preserving protocols for a particular data mining task: learning a Bayesian network from a database vertically partitioned among two parties. In this setting, two parties owning confidential databases wish to learn the Bayesian network on the combination of their databases without revealing anything else about their data to each other. We present an efficient and privacy-preserving protocol to construct a Bayesian network on the parties' joint data
76 citations
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TL;DR: In this article, the applicability and limitations of such a model for a number of membrane-solute-solvent systems were studied and the boundary layer and membrane resistances to solute transport were isolated and simple relations developed for the overall mass transfer coefficient in such systems.
Abstract: Dispersion-free solvent extraction using microporous hydrophobic membranes has been extended to hydrophilic and composite hydrophobic-hydrophilic membranes. Excess phase pressure conditions, if needed for dispersion-free operation, have been identified. Boundary layer and membrane resistances to solute transport have been isolated and simple relations developed for the overall mass transfer coefficient in such systems. A variety of flat microporous membranes have been utilized. Previous investigations by others had interpreted the membrane mass transfer resistance using the notion of unhindered diffusion through tortuous pores of the membrane. We have studied here the applicability and limitations of such a model for a number of membrane-solute-solvent systems.
76 citations
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TL;DR: It is concluded that a common belief, namely, spread spectrum steganography/watermarking, is secure because the low strength, noise-like message carrier is no longer valid within the current context.
Abstract: A mathematical framework for steganalysis is presented in this Paper, with linear steganography being the main focus. A mathematically formal definition of steganalysis is given. Then active steganalysis, defined as the extraction of a hidden message with little or no a priori information, is formulated as a blind system identification problem within this framework. Conditions for identifiability (i.e., successful steganalysis) are derived. A procedure to systematically exploit any available spatial and temporal diversity information for efficient steganalysis is also discussed.
Experimental results are given for steganalysis of Gaussian distributed, spread spectrum image steganography and watermarking. The proposed technique is observed to produce impressive results for a variety of performance measures. Based on the results we conclude that a common belief, namely, spread spectrum steganography/watermarking, is secure because the low strength, noise-like message carrier is no longer valid within the current context. Therefore, new questions regarding steganography security that differ from the standard information theoretic notion are raised and some answers are provided.
76 citations
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TL;DR: In this article, a microporous hydrophobic hollow fiber based membrane extractor has been used to remove a number of priority organic pollutants simultaneously from a synthetic high strengh aqueous waste stream.
Abstract: A recently developed nondispersive microporous membrane-beed solvent extraction technique has been used to remove a number of priority organic pollutants simultaneously from a synthetic high strengh aqueous waste stream. A microporous hydrophobic hollow fiber based membrane extractor having an order of magnitude higher contact area than conventional extraction devices he been used. The pollutants were phenol, 2-chlorophenol, nitrobenzene, toluene, andacrylonitrile. The extracting solvents were methyl isobutyl ketone (MIBK), isopropyl acetate (IPAc), and hexane
76 citations
Authors
Showing all 5536 results
Name | H-index | Papers | Citations |
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Paul M. Thompson | 183 | 2271 | 146736 |
Roger Jones | 138 | 998 | 114061 |
Georgios B. Giannakis | 137 | 1321 | 73517 |
Li-Jun Wan | 113 | 639 | 52128 |
Joel L. Lebowitz | 101 | 754 | 39713 |
David Smith | 100 | 994 | 42271 |
Derong Liu | 77 | 608 | 19399 |
Robert R. Clancy | 77 | 293 | 18882 |
Karl H. Schoenbach | 75 | 494 | 19923 |
Robert M. Gray | 75 | 371 | 39221 |
Jin Yu | 74 | 480 | 32123 |
Sheng Chen | 71 | 688 | 27847 |
Hui Wu | 71 | 347 | 19666 |
Amir H. Gandomi | 67 | 375 | 22192 |
Haibo He | 66 | 482 | 22370 |