Coherence as a feature of real HF signals

Copyright 2005 Society of Photo-Optical Instrumentation Engineers. This paper was published in Noise in Communication Systems, edited by Costas N. Georghiades, Langford B. White, Proc. of SPIE Vol. 5847 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.High-frequency (HF) communications is undergoing a resurgence despite advances in long-range satellite communication systems. Defense agencies are using the HF spectrum for backup communications as well as for spectrum surveillance applications. Spectrum management organizations are monitoring the HF spectrum to control and enforce licensing. These activities usually require systems capable of determining the location of a source of transmissions, separating valid signals from interference and noise, and recognizing signal modulation. Our ultimate aim is to develop robust modulation recognition algorithms for real HF signals that propagate by multiple ionospheric modes. One aspect of modulation recognition is the extraction of signal identifying features. The most common features for modulation recognition are instantaneous phase, amplitude, and frequency. Many papers present results based on synthetic data and unproven assumptions. However, this paper continues our previous work by applying the coherence function to noisy real HF groundwave signals; which removes the need for synthesized data and unrealistic assumptions.James E. Giesbrecht, Russell Clarke, and Derek Abbot

The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming.

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation

Transition in a Laminar Separation Bubble and the Effect of Acoustic Excitation

Flow development within a laminar separation bubble and the effect of controlled acoustic excitation are investigated experimentally for a NACA 0018 airfoil at a chord Reynolds number of 125 000 and an angle of attack of 4°. The investigation is carried out in a series of wind tunnel tests and employs a combination of surface pressure measurements and time-resolved, planar, two-component Particle Image Velocimetry. Both streamwise and spanwise aspects of the flow development are assessed. Three types of acoustic excitation are investigated: (i) harmonic: tonal excitation at the frequency of the most amplified disturbances in the natural flow, (ii) subharmonic: tonal excitation at the subharmonic frequency, and (iii) random: white noise filtered to the naturally unstable frequency band. For all the cases examined, the results show that strongly periodic shear-layer vortices form in the separation bubble due to the amplification of disturbances in the separated shear layer. The formation of these structures is found to occur when velocity fluctuations reach a critical amplitude equal to approximately 6% of the free-stream velocity. These structures feature strong spanwise coherence at roll-up; however, they deform rapidly upstream of the mean reattachment point. Spanwise undulations in the vortex filaments develop in a non-periodic fashion, with the spanwise wavelength shown to be predominantly two and a half times the characteristic streamwise wavelength of the structures. These spanwise deformations lead to regions of local vortex breakup, which expand rapidly as the vortices approach the mean reattachment point. This is accompanied by a rapid decay of the spanwise coherence length, which reaches a value typical of turbulent boundary layers downstream of mean reattachment. Harmonic and random excitations are found to decrease the separation bubble in size, with the effect becoming more pronounced with increasing amplitude. The changes in the mean bubble topology are linked to alterations in the features of the shear layer vortices. Specifically, these types of excitation cause earlier shear layer roll-up, which results in the aft portion of the bubble shifting upstream. Harmonic excitation is shown to organize the shedding process and increase the spanwise coherence of the vortices, while random excitation has the opposite effect. It is proposed that mean reattachment in a separation bubble is a result of momentum exchange between the outer-flow and the near wall region, which can be facilitated by either the coherent roll-up vortices or the smaller scale structures that result from their breakdown, with the latter proving to be more effective. Subharmonic excitation is shown to promote periodic and spanwise non-uniform vortex merging, which otherwise occurs randomly in the unperturbed flow. The results demonstrate that the development of shear-layer vortices plays the fundamental role in defining the separation bubble, and the later stages of transition are directly related to the breakdown of these structures in the aft portion of the bubble