Low-frequency electrostatic waves in the ionospheric E-region: a comparison of rocket observations and numerical simulations

International audienceLow frequency electrostatic waves in the lower parts of the ionosphere are studied by a comparison of observations by instrumented rockets and of results from numerical simulations. Particular attention is given to the spectral properties of the waves. On the basis of a good agreement between the observations and the simulations, it can be argued that the most important nonlinear dynamics can be accounted for in a 2-D numerical model, referring to a plane perpendicular to a locally homogeneous magnetic field. It does not seem necessary to take into account turbulent fluctuations or motions in the neutral gas component. The numerical simulations explain the observed strongly intermittent nature of the fluctuations: secondary instabilities develop on the large scale gradients of the largest amplitude waves, and the small scale dynamics is strongly influenced by these secondary instabilities. We compare potential variations obtained at a single position in the numerical simulations with two point potential-difference signals, where the latter is the adequate representation for the data obtained by instrumented rockets. We can demonstrate a significant reduction in the amount of information concerning the plasma turbulence when the latter signal is used for analysis. In particular we show that the bicoherence estimate is strongly affected. The conclusions have implications for studies of low frequency ionospheric fluctuations in the E and F regions by instrumented rockets, and also for other methods relying on difference measurements, using two probes with large separation. The analysis also resolves a long standing controversy concerning the supersonic phase velocities of these cross-field instabilities being observed in laboratory experiments

Pattern classification based multiuser detectors for CDMA communication systems

Master'sMASTER OF ENGINEERIN

Population Dynamics In A Model Closed Ecosystem

For almost any species in any environment, it is nearly impossible to predict its fitness from molecular knowledge. If fitness is not to be a mere tautology, reproducible measurements of the survival and reproduction of populations are needed over many generations. Laboratory microbial ecosystems afford the short time and length scales required for such measurements. Their conventional implementations, batch cultures with period refreshment of growth medium or chemostats with continuous refreshment, have a number of disadvantages, such as the introduction of additional frequencies, selection for surface growth and the distortion of chemical interactions. In closed ecosystems free energy is instead supplied as light, allowing for simpler, replicable protocols and a consistent interpretation of interactions, independent of their mode or timescale. Here, I describe a model closed ecosystem consisting of three singlecelled microbes, Escherichia coli, Chlamydomonas reinhardtii and Tetrahymena thermophila and show that these species can coexist for hundreds of days under closure. Using a custom built in situ fluorescence microscopy set up, the densities of these three species can be measured automatically and noninvasively over months with low classification error and large dynamical range. When kept under identical boundary conditions, these ecosystems reproducibly diverge in composition, with characteristic divergence times of ~20 days for T. thermophila, ~40 days for the other two species, and an approximately linear increase of an aggregate divergence measure over the first ~60 days. For two ecosystems, densities were measured continuously under constant conditions and their dynamics shown to be nonstationary for all three species \u3e100 days after closure. As a consequence, conventional time series methods assuming stationarity are inadequate and wavelet analysis is proposed as an alternative. Species-species interactions are further investigated using oscillations in illumination intensity. Densities of C. reinhardtii and, surprisingly, E. coli respond to modest perturbations of light intensity. Variation of the modulation frequency strongly implicates the circadian clock of C. reinhardtii in its response. The nonlinearity of the E. coli response suggests that it depends on C. reinhardtii density or spatial distribution rather than directly responds to the modulation of illumination. Further improvements in the detection of interactions are proposed

Experimental Investigation Of Ultrawideband Wireless Systems: Waveform Generation, Propagation Estimation, And Dispersion Compensation

Ultrawideband (UWB) is an emerging technology for the future high-speed wireless communication systems. Although this technology offers several unique advantages like robustness to fading, large channel capacity and strong anti-jamming ability, there are a number of practical challenges which are topics of current research. One key challenge is the increased multipath dispersion which results because of the fine temporal resolution. The received response consists of different components, which have certain delays and attenuations due to the paths they took in their propagation from the transmitter to the receiver. Although such challenges have been investigated to some extent, they have not been fully explored in connection with sophisticated transmit beamforming techniques in realistic multipath environments. The work presented here spans three main aspects of UWB systems including waveform generation, propagation estimation, and dispersion compensation. We assess the accuracy of the measured impulse responses extracted from the spread spectrum channel sounding over a frequency band spanning 2-12 GHz. Based on the measured responses, different transmit beamforming techniques are investigated to achieve high-speed data transmission in rich multipath channels. We extend our work to multiple antenna systems and implement the first experimental test-bed to investigate practical challenges such as imperfect channel estimation or coherency between the multiple transmitters over the full UWB band. Finally, we introduce a new microwave photonic arbitrary waveform generation technique to demonstrate the first optical-wireless transmitter system for both characterizing channel dispersion and generating predistorted waveforms to achieve spatio-temporal focusing through the multipath channels

Identification of Dendritic Processing in Spiking Neural Circuits

A large body of experimental evidence points to sophisticated signal processing taking place at the level of dendritic trees and dendritic branches of neurons. This evidence suggests that, in addition to inferring the connectivity between neurons, identifying analog dendritic processing in individual cells is fundamentally important to understanding the underlying principles of neural computation. In this thesis, we develop a novel theoretical framework for the identification of dendritic processing directly from spike times produced by spiking neurons. The problem setting of spiking neurons is necessary since such neurons make up the majority of electrically excitable cells in most nervous systems and it is often hard or even impossible to directly monitor the activity within dendrites. Thus, action potentials produced by neurons often constitute the only causal and observable correlate of dendritic processing. In order to remain true to the underlying biophysics of electrically excitable cells, we employ well-established mechanistic models of action potential generation to describe the nonlinear mapping of the aggregate current produced by the tree into an asynchronous sequence of spikes. Specific models of spike generation considered include conductance-based models such as Hodgkin-Huxley, Morris-Lecar, Fitzhugh-Nagumo, as well as simpler models of the integrate-and-fire and threshold-and-fire type. The aggregate time-varying current driving the spike generator is taken to be produced by a dendritic stimulus processor, which is a nonlinear dynamical system capable of describing arbitrary linear and nonlinear transformations performed on one or more input stimuli. In the case of multiple stimuli, it can also describe the cross-coupling, or interaction, between various stimulus features. The behavior of the dendritic stimulus processor is fully captured by one or more kernels, which provide a characterization of the signal processing that is consistent with the broader cable theory description of dendritic trees. We prove that the neural identification problem, stated in terms of identifying the kernels of the dendritic stimulus processor, is mathematically dual to the neural population encoding problem. Specifically, we show that the collection of spikes produced by a single neuron in multiple experimental trials can be treated as a single multidimensional spike train of a population of neurons encoding the parameters of the dendritic stimulus processor. Using the theory of sampling in reproducing kernel Hilbert spaces, we then derive precise results demonstrating that, during any experiment, the entire neural circuit is projected onto the space of input stimuli and parameters of this projection are faithfully encoded in the spike train. Spike times are shown to correspond to generalized samples, or measurements, of this projection in a system of coordinates that is not fixed but is both neuron- and stimulus-dependent. We examine the theoretical conditions under which it may be possible to reconstruct the dendritic stimulus processor from these samples and derive corresponding experimental conditions for the minimum number of spikes and stimuli that need to be used. We also provide explicit algorithms for reconstructing the kernel projection and demonstrate that, under natural conditions, this projection converges to the true kernel. The developed methodology is quite general and can be applied to a number of neural circuits. In particular, the methods discussed span all sensory modalities, including vision, audition and olfaction, in which external stimuli are typically continuous functions of time and space. The results can also be applied to circuits in higher brain centers that receive multi-dimensional spike trains as input stimuli instead of continuous signals. In addition, the modularity of the approach allows one to extend it to mixed-signal circuits processing both continuous and spiking stimuli, to circuits with extensive lateral connections and feedback, as well as to multisensory circuits concurrently processing multiple stimuli of different dimensions, such as audio and video. Another important extension of the approach can be used to estimate the phase response curves of a neuron. All of the theoretical results are accompanied by detailed examples demonstrating the performance of the proposed identification algorithms. We employ both synthetic and naturalistic stimuli such as natural video and audio to highlight the power of the approach. Finally, we consider the implication of our work on problems pertaining to neural encoding and decoding and discuss promising directions for future research

Perceptual models in speech quality assessment and coding

The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as inputs to a second model. This model simulates the information centre in the brain which performs the speech quality assessment. [Continues.