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: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.
Topics: Computer science, Cognitive radio, Communication channel, Wireless network, Artificial neural network
Papers published on a yearly basis
Papers
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22 Aug 2012TL;DR: A peer assisted localization approach is proposed that can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.
Abstract: Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., $6\sim8m$) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m$, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.
430 citations
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TL;DR: The proposed data filter-cleaner includes an on-li ne outlier-resistant estimate of the process model and combines it with a modified Kalman filter to detect and “clean” outliers.
424 citations
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21 Aug 2005TL;DR: The concept of arbitrarily partitioned data is introduced, which is a generalization of both horizontally and vertically partitionedData, and an efficient privacy-preserving protocol for k-means clustering in the setting of arbitrarily partitions data is provided.
Abstract: Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Data mining can extract valuable knowledge from this data, and organizations have realized that they can often obtain better results by pooling their data together. However, the collected data may contain sensitive or private information about the organizations or their customers, and privacy concerns are exacerbated if data is shared between multiple organizations.Distributed data mining is concerned with the computation of models from data that is distributed among multiple participants. Privacy-preserving distributed data mining seeks to allow for the cooperative computation of such models without the cooperating parties revealing any of their individual data items. Our paper makes two contributions in privacy-preserving data mining. First, we introduce the concept of arbitrarily partitioned data, which is a generalization of both horizontally and vertically partitioned data. Second, we provide an efficient privacy-preserving protocol for k-means clustering in the setting of arbitrarily partitioned data.
422 citations
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TL;DR: In this paper, the analytic extension of the Schwarzschild exterior solution is given in a closed form valid throughout empty space-time and possessing no irregularities except that at the origin, and the gravitational field of a spherical point particle is then seen not to be invariant under time reversal for any admissible choice of time coordinate.
Abstract: The analytic extension of the Schwarzschild exterior solution is given in a closed form valid throughout empty space-time and possessing no irregularities except that at the origin. The gravitational field of a spherical point particle is then seen not to be invariant under time reversal for any admissible choice of time coordinate. The Schwarzschild surface $r=2m$ is not a singularity but acts as a perfect unidirectional membrane: causal influences can cross it but only in one direction. The apparent violation of the principle of sufficient reason seems similar to that which is associated with instabilities in other nonlinear phenomena.
421 citations
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TL;DR: The results suggest that project success factors are not universal for all projects, suggesting the need for a more contingent approach in project management theory and practice.
415 citations
Authors
Showing all 5536 results
Name | H-index | Papers | Citations |
---|---|---|---|
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 |