60 research outputs found
On optimal quantization rules for some problems in sequential decentralized detection
We consider the design of systems for sequential decentralized detection, a
problem that entails several interdependent choices: the choice of a stopping
rule (specifying the sample size), a global decision function (a choice between
two competing hypotheses), and a set of quantization rules (the local decisions
on the basis of which the global decision is made). This paper addresses an
open problem of whether in the Bayesian formulation of sequential decentralized
detection, optimal local decision functions can be found within the class of
stationary rules. We develop an asymptotic approximation to the optimal cost of
stationary quantization rules and exploit this approximation to show that
stationary quantizers are not optimal in a broad class of settings. We also
consider the class of blockwise stationary quantizers, and show that
asymptotically optimal quantizers are likelihood-based threshold rules.Comment: Published as IEEE Transactions on Information Theory, Vol. 54(7),
3285-3295, 200
Submodularity and Optimality of Fusion Rules in Balanced Binary Relay Trees
We study the distributed detection problem in a balanced binary relay tree,
where the leaves of the tree are sensors generating binary messages. The root
of the tree is a fusion center that makes the overall decision. Every other
node in the tree is a fusion node that fuses two binary messages from its child
nodes into a new binary message and sends it to the parent node at the next
level. We assume that the fusion nodes at the same level use the same fusion
rule. We call a string of fusion rules used at different levels a fusion
strategy. We consider the problem of finding a fusion strategy that maximizes
the reduction in the total error probability between the sensors and the fusion
center. We formulate this problem as a deterministic dynamic program and
express the solution in terms of Bellman's equations. We introduce the notion
of stringsubmodularity and show that the reduction in the total error
probability is a stringsubmodular function. Consequentially, we show that the
greedy strategy, which only maximizes the level-wise reduction in the total
error probability, is within a factor of the optimal strategy in terms of
reduction in the total error probability
Compressed Fingerprint Matching and Camera Identification via Random Projections
Sensor imperfections in the form of photo-response nonuniformity (PRNU) patterns are a well-established fingerprinting technique to link pictures to the camera sensors that acquired them. The noise-like characteristics of the PRNU pattern make it a difficult object to compress, thus hindering many interesting applications that would require storage of a large number of fingerprints or transmission over a bandlimited channel for real-time camera matching. In this paper, we propose to use realvalued or binary random projections to effectively compress the fingerprints at a small cost in terms of matching accuracy. The performance of randomly projected fingerprints is analyzed from a theoretical standpoint and experimentally verified on databases of real photographs. Practical issues concerning the complexity of implementing random projections are also addressed by using circulant matrices
Asynchronous Communication: Capacity Bounds and Suboptimality of Training
Several aspects of the problem of asynchronous point-to-point communication
without feedback are developed when the source is highly intermittent. In the
system model of interest, the codeword is transmitted at a random time within a
prescribed window whose length corresponds to the level of asynchronism between
the transmitter and the receiver. The decoder operates sequentially and
communication rate is defined as the ratio between the message size and the
elapsed time between when transmission commences and when the decoder makes a
decision.
For such systems, general upper and lower bounds on capacity as a function of
the level of asynchronism are established, and are shown to coincide in some
nontrivial cases. From these bounds, several properties of this asynchronous
capacity are derived. In addition, the performance of training-based schemes is
investigated. It is shown that such schemes, which implement synchronization
and information transmission on separate degrees of freedom in the encoding,
cannot achieve the asynchronous capacity in general, and that the penalty is
particularly significant in the high-rate regime.Comment: 27 pages, 8 figures, submitted to the IEEE Transactions on
Information Theor
Detection Performance in Balanced Binary Relay Trees with Node and Link Failures
We study the distributed detection problem in the context of a balanced
binary relay tree, where the leaves of the tree correspond to identical and
independent sensors generating binary messages. The root of the tree is a
fusion center making an overall decision. Every other node is a relay node that
aggregates the messages received from its child nodes into a new message and
sends it up toward the fusion center. We derive upper and lower bounds for the
total error probability as explicit functions of in the case where
nodes and links fail with certain probabilities. These characterize the
asymptotic decay rate of the total error probability as goes to infinity.
Naturally, this decay rate is not larger than that in the non-failure case,
which is . However, we derive an explicit necessary and sufficient
condition on the decay rate of the local failure probabilities
(combination of node and link failure probabilities at each level) such that
the decay rate of the total error probability in the failure case is the same
as that of the non-failure case. More precisely, we show that if and only if
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