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Institution

Stevens Institute of Technology

EducationHoboken, 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
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Paul M. Thompson1832271146736
Roger Jones138998114061
Georgios B. Giannakis137132173517
Li-Jun Wan11363952128
Joel L. Lebowitz10175439713
David Smith10099442271
Derong Liu7760819399
Robert R. Clancy7729318882
Karl H. Schoenbach7549419923
Robert M. Gray7537139221
Jin Yu7448032123
Sheng Chen7168827847
Hui Wu7134719666
Amir H. Gandomi6737522192
Haibo He6648222370
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202342
2022139
2021765
2020820
2019799
2018563