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- Ameya Agaskar, Yue M. Lu
- IEEE Transactions on Information Theory
- 2013

The spectral theory of graphs provides a bridge between classical signal processing and the nascent field of graph signal processing. In this paper, a spectral graph analogy to Heisenberg's celebrated uncertainty principle is developed. Just as the classical result provides a tradeoff between signal localization in time and frequency, this result provides a… (More)

- Ameya Agaskar, Yue M. Lu
- 2012 IEEE International Conference on Acoustics…
- 2012

The classical uncertainty principle provides a fundamental tradeoff in the localization of a signal in the time and frequency domains. In this paper we describe a similar tradeoff for signals defined on graphs. We describe the notions of “spread” in the graph and spectral domains, using the eigenvectors of the graph Laplacian as a surrogate… (More)

- Ameya Agaskar, Yue M. Lu
- 2011

The classical uncertainty principle provides a fundamental tradeoff in the localization of a function in the time and frequency domains. In this paper we extend this classical result to functions defined on graphs. We justify the use of the graph Laplacian’s eigenbasis as a surrogate for the Fourier basis for graphs, and define the notions of “spread” in… (More)

- Ameya Agaskar, Yue M. Lu
- ArXiv
- 2015

We study the problem of detecting a random walk on a graph from a sequence of noisy measurements at every node. There are two hypotheses: either every observation is just meaningless zero-mean Gaussian noise, or at each time step exactly one node has an elevated mean, with its location following a random walk on the graph over time. We want to exploit… (More)

- Ameya Agaskar, Ting He, Lang Tong
- IEEE Transactions on Signal Processing
- 2010

The problem of detecting multihop information flows subject to communication constraints is considered. In a distributed detection scheme, eavesdroppers are deployed near nodes in a network, each able to measure the transmission timestamps of a single node. The eavesdroppers must then compress the information and transmit it to a fusion center, which then… (More)

- Ameya Agaskar, Chuang Wang, Yue M. Lu
- 2014 IEEE Global Conference on Signal and…
- 2014

The Kaczmarz method, or the algebraic reconstruction technique (ART), is a popular method for solving large-scale overdetermined systems of equations. Recently, Strohmer et al. proposed the randomized Kaczmarz algorithm, an improvement that guarantees exponential convergence to the solution. This has spurred much interest in the algorithm and its… (More)

- Ting He, Ameya Agaskar, Lang Tong
- 2008 IEEE International Conference on Acoustics…
- 2008

The problem of interest is to characterize to what extent nodes independently following certain transmission schedules can be hijacked to relay flows of information packets. Information flows can be embedded in given transmission schedules by properly adding delays and inserting dummy packets. Such hidden flows are usually indicators of network intrusion,… (More)

- Ameya Agaskar, Yue M. Lu
- 2013 IEEE International Conference on Acoustics…
- 2013

We consider the problem of distinguishing between two hypotheses: that a sequence of signals on a large graph consists entirely of noise, or that it contains a realization of a random walk buried in the noise. The problem of computing the error exponent of the optimal detector is simple to formulate, but exhibits deep connections to problems known to be… (More)

- Daniel W. Bliss, Shawn Kraut, Ameya Agaskar
- 2012 IEEE International Conference on Acoustics…
- 2012

In this paper, the problem of informed-transmitter cooperative MIMO communications is addressed. The informed-transmitter link assumes that the distributed transmit nodes have access to channel state information. The channel state information includes the channel between the transmit and receive antenna arrays and a statistical model for interference… (More)

- Ameya Agaskar, Lang Tong, Ting He
- 2008 42nd Annual Conference on Information…
- 2008

Distributed detection of information flows spanning many nodes in a wireless sensor network is considered. In such a system, eavesdroppers are deployed near several nodes in the network. As data may be encrypted or padded, the eavesdroppers can only measure packet timestamps. Each eavesdropper, given a sequence of timestamps, must compress the information… (More)