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A simple Cooperative diversity method based on network path selection
- A. Bletsas, A. Khisti, D. Reed, A. Lippman
- Computer ScienceIEEE Journal on Selected Areas in Communications
- 23 October 2005
A novel scheme that first selects the best relay from a set of M available relays and then uses this "best" relay for cooperation between the source and the destination and achieves the same diversity-multiplexing tradeoff as achieved by more complex protocols.
MedRec: Using Blockchain for Medical Data Access and Permission Management
- Asaph Azaria, Ariel Ekblaw, Thiago Vieira, A. Lippman
- Computer Science2nd International Conference on Open and Big Data…
- 1 August 2016
This paper proposes MedRec: a novel, decentralized record management system to handle EMRs, using blockchain technology, and incentivizes medical stakeholders to participate in the network as blockchain “miners”, enabling the emergence of data economics.
Learning Mixture Hierarchies
An extension of the Expectation-Maximization (EM) algorithm that learns mixture hierarchies in a computationally efficient manner that allows a new interpretation of EM that makes clear the relationship with non-parametric kernel-based estimation methods, and provides explicit control over the trade-off between the bias and variance of EM estimates.
A Case Study for Blockchain in Healthcare : “ MedRec ” prototype for electronic health records and medical research data
The purpose of this paper is to expose a working prototype of MedRec, a novel, decentralized record management system to handle EHRs, using blockchain technology, and to analyze and discuss the approach and the potential for blockchain in health IT and research.
Movie-maps: An application of the optical videodisc to computer graphics
- A. Lippman
- Computer ScienceSIGGRAPH '80
- 14 July 1980
An interactive, dynamic map has been built using videodisc technology to engage the user in a simulated “drive” through an unfamiliar space, and to incorporate optical and electronic image processing to provide a more responsive, visually complete representation of an environment.
A probabilistic architecture for content-based image retrieval
- N. Vasconcelos, A. Lippman
- Computer ScienceProceedings IEEE Conference on Computer Vision…
- 13 June 2000
A solution where all the modules strive to optimize the same performance criteria: the probability of retrieval error is presented, which consists of a Bayesian retrieval criteria and an embedded mixture representation over a multiresolution feature space.
Bayesian models for visual information retrieval
A Bayesian architecture is shown to generalize a significant number of previous recognition approaches, solving some of the most challenging problems faced by these: joint modeling of color and texture, objective guidelines for controlling the trade-off between feature transformation and feature representation, and unified support for local and global queries without requiring image segmentation.
Learning from User Feedback in Image Retrieval Systems
A new learning algorithm is presented that relies on belief propagation to account for both positive and negative examples of the user's interests in order to solve two of the most challenging issues in the design of a retrieval system.
Separation of multiple passive RFID signals using Software Defined Radio
- D. Shen, G. Woo, D. Reed, A. Lippman, Junyu Wang
- Computer ScienceIEEE International Conference on RFID
- 27 April 2009
By exploring the differences in amplitudes and phase offsets among signal components, multiple tags can be separated and efficiently decoded using joint decoding and opportunities for improving industrial auto-collision algorithms with multiple-tag decoding capability are summarized.
A unifying view of image similarity
- N. Vasconcelos, A. Lippman
- Computer ScienceProceedings 15th International Conference on…
- 3 September 2000
It is shown that this formulation establishes a common ground for comparing similarity functions, exposes assumptions hidden behind in most commonly used ones, enables a critical analysis of their relative merits, and determines the retrieval scenarios for which each may be most suited.