WDM monitoring technique using adaptive blind signal separation

We present a cost-effective method of monitoring the performance of wavelength-division-multiplexed (WDM) channels. The method is based on simple optical signal processing in combination with electronic signal processing. The photocurrent of a detected (multi-channel) optical signal is analysed using an adaptive blind signal separation method. A maximum data decorrelation criterion is used to separate the WDM channels. We show experimentally that four WDM channels can be reconstructed accurately by this numerical method

Compressive Source Separation: Theory and Methods for Hyperspectral Imaging

With the development of numbers of high resolution data acquisition systems and the global requirement to lower the energy consumption, the development of efficient sensing techniques becomes critical. Recently, Compressed Sampling (CS) techniques, which exploit the sparsity of signals, have allowed to reconstruct signal and images with less measurements than the traditional Nyquist sensing approach. However, multichannel signals like Hyperspectral images (HSI) have additional structures, like inter-channel correlations, that are not taken into account in the classical CS scheme. In this paper we exploit the linear mixture of sources model, that is the assumption that the multichannel signal is composed of a linear combination of sources, each of them having its own spectral signature, and propose new sampling schemes exploiting this model to considerably decrease the number of measurements needed for the acquisition and source separation. Moreover, we give theoretical lower bounds on the number of measurements required to perform reconstruction of both the multichannel signal and its sources. We also proposed optimization algorithms and extensive experimentation on our target application which is HSI, and show that our approach recovers HSI with far less measurements and computational effort than traditional CS approaches.Comment: 32 page

The multi-frequency angular power spectrum of the epoch of reionization 21 cm signal

Observations of redshifted 21cm radiation from HI at high redshifts is an important future probe of reionization. We consider the Multi-frequency Angular Power Spectrum (MAPS) to quantify the statistics of the HI signal as a joint function of the angular multipole l and frequency separation \Delta\nu. The signal at two different frequencies is expected to get decorrelated as \Delta\nu is increased, and quantifying this decorrelation is particularly important in deciding the frequency resolution for future HI observations. This is also expected to play a very crucial role in extracting the signal from foregrounds as the signal is expected to decorrelate much faster than the foregrounds (which are largely continuum sources) with increasing \Delta\nu. In this paper we develop formulae relating the MAPS to different components of the three dimensional HI power spectrum taking into account HI peculiar velocities. We show that the flat-sky approximation provides a very good representation over the angular scales of interest, and a final expression which is very simple to calculate and interpret. We present results considering two models for the HI distribution, namely, (i) DM: where the HI traces the dark matter and (ii) PR: where the effects of patchy reionization are incorporated through two parameters. We find that while the DM signal is largely featureless, the PR signal peaks at the angular scales of the individual bubbles, and the signal is considerably enhanced for large bubble size. For most cases of interest at l \sim 100 the signal is uncorrelated beyond \Delta\nu \sim 1 MHz or even less, whereas it occurs around \sim 0.1 MHz at l \sim 10^3. The \Delta\nu dependence also carries an imprint of the bubble size and the bias, and is expected to be an important probe of the reionization scenario (abridged).Comment: Accepted for publication in MNRAS. Revised to match the accepted versio

Decorrelation of Neutral Vector Variables: Theory and Applications

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations

Relays for Interference Mitigation in Wireless Networks

Wireless links play an important role in the last mile network connectivity. In contrast to the strictly centralized approach of today's wireless systems, the future promises decentralization of network management. Nodes potentially engage in localized grouping and organization based on their neighborhood to carry out complex goals such as end-to-end communication. The quadratic energy dissipation of the wireless medium necessitates the presence of certain relay nodes in the network. Conventionally, the role of such relays is limited to passing messages in a chain in a point-point hopping architecture. With the decentralization, multiple nodes could potentially interfere with each other. This work proposes a technique to exploit the presence of relays in a way that mitigates interference between the network nodes. Optimal spatial locations and transmission schemes which enhance this gain are identified