2,445 research outputs found
Operational Rate-Distortion Performance of Single-source and Distributed Compressed Sensing
We consider correlated and distributed sources without cooperation at the
encoder. For these sources, we derive the best achievable performance in the
rate-distortion sense of any distributed compressed sensing scheme, under the
constraint of high--rate quantization. Moreover, under this model we derive a
closed--form expression of the rate gain achieved by taking into account the
correlation of the sources at the receiver and a closed--form expression of the
average performance of the oracle receiver for independent and joint
reconstruction. Finally, we show experimentally that the exploitation of the
correlation between the sources performs close to optimal and that the only
penalty is due to the missing knowledge of the sparsity support as in (non
distributed) compressed sensing. Even if the derivation is performed in the
large system regime, where signal and system parameters tend to infinity,
numerical results show that the equations match simulations for parameter
values of practical interest.Comment: To appear in IEEE Transactions on Communication
Multidimensional quantum entanglement with large-scale integrated optics
The ability to control multidimensional quantum systems is key for the
investigation of fundamental science and for the development of advanced
quantum technologies. Here we demonstrate a multidimensional integrated quantum
photonic platform able to robustly generate, control and analyze
high-dimensional entanglement. We realize a programmable bipartite entangled
system with dimension up to on a large-scale silicon-photonics
quantum circuit. The device integrates more than 550 photonic components on a
single chip, including 16 identical photon-pair sources. We verify the high
precision, generality and controllability of our multidimensional technology,
and further exploit these abilities to demonstrate key quantum applications
experimentally unexplored before, such as quantum randomness expansion and
self-testing on multidimensional states. Our work provides a prominent
experimental platform for the development of multidimensional quantum
technologies.Comment: Science, (2018
Employing data fusion & diversity in the applications of adaptive signal processing
The paradigm of adaptive signal processing is a simple yet powerful method for the class of system identification problems. The classical approaches consider standard one-dimensional signals whereby the model can be formulated by flat-view matrix/vector framework. Nevertheless, the rapidly increasing availability of large-scale multisensor/multinode measurement technology has render no longer sufficient the traditional way of representing the data. To this end, the author, who from this point onward shall be referred to as `we', `us', and `our' to signify the author myself and other supporting contributors i.e. my supervisor, my colleagues and other overseas academics specializing in the specific pieces of research endeavor throughout this thesis, has applied the adaptive filtering framework to problems that employ the techniques of data diversity and fusion which includes quaternions, tensors and graphs. At the first glance, all these structures share one common important feature: invertible isomorphism. In other words, they are algebraically one-to-one related in real vector space. Furthermore, it is our continual course of research that affords a segue of all these three data types. Firstly, we proposed novel quaternion-valued adaptive algorithms named the n-moment widely linear quaternion least mean squares (WL-QLMS) and c-moment WL-LMS. Both are as fast as the recursive-least-squares method but more numerically robust thanks to the lack of matrix inversion. Secondly, the adaptive filtering method is applied to a more complex task: the online tensor dictionary learning named online multilinear dictionary learning (OMDL). The OMDL is partly inspired by the derivation of the c-moment WL-LMS due to its parsimonious formulae. In addition, the sequential higher-order compressed sensing (HO-CS) is also developed to couple with the OMDL to maximally utilize the learned dictionary for the best possible compression. Lastly, we consider graph random processes which actually are multivariate random processes with spatiotemporal (or vertex-time) relationship. Similar to tensor dictionary, one of the main challenges in graph signal processing is sparsity constraint in the graph topology, a challenging issue for online methods. We introduced a novel splitting gradient projection into this adaptive graph filtering to successfully achieve sparse topology. Extensive experiments were conducted to support the analysis of all the algorithms proposed in this thesis, as well as pointing out potentials, limitations and as-yet-unaddressed issues in these research endeavor.Open Acces
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