Beta-frequency (15-35 Hz) electroencephalogram activities elicited by toluene and electrical stimulation in the behaving rat

Bursts of beta-frequency (15-35 Hz) electroencephalogram activity occur in the olfactory system during odour sampling, but their mode of propagation within the olfactory system and potential contribution to the mechanisms of learning and memory are unclear. We have elicited large-amplitude beta activity in the rat olfactory system by applying noxious olfactory stimuli (toluene), and have monitored the bursts via chronically-implanted electrodes. Following exposure to toluene, coherent bursts with a peak frequency of 19.8 +/- 0.9 Hz were observed in the olfactory bulb, piriform cortex, entorhinal cortex and dentate gyrus. The timing of the bursts and the phases of electroencephalogram cross-spectra indicate that beta bursts propagate in a caudal direction from the olfactory bulb to the entorhinal cortex. The time delays between peaks of bursts in these structures were similar to latency differences for field potentials evoked by olfactory bulb or piriform cortex test-pulses. Peaks of burst cycles in the dentate region, however, were observed just prior to those in the entorhinal cortex. Surprisingly, power in toluene-induced beta-frequency oscillations was not increased following long-term potentiation induced by tetanic stimulation of the olfactory bulb, piriform cortex and entorhinal cortex. The activity of local inhibitory mechanisms may therefore counteract the effects of synaptic enhancements in afferent pathways during beta bursts. Low-frequency electrical stimulation of the piriform cortex was most effective in inducing coherent oscillatory responses in the entorhinal cortex and dentate gyrus at stimulation frequencies between 12 and 16 Hz. The results show that repetitive polysynaptic volleys at frequencies in the beta band induced by either toluene or electrical stimulation are transmitted readily within the olfactory system. The propagation of neural activity within this frequency range may therefore contribute to the transmission of olfactory signals to the hippocampal formation, particularly for those odours which induce high-amplitude bursts of beta activity.     

Optimal synthesis of an RC active filter

For a given network structure, the problem considered is that of finding the optimum set of nominal element values, so that the network not only realises a prescribed pair of complex-conjugate poles exactly, but also minimises the multi-parameter sensitivity of the network.     

Radar clutter suppression schemes

In the letter the spatial correlation between radar clutter returns from adjacent range rings is exploited to construct two new adaptive clutter suppression schemes. The schemes are tested by using actual radar data, including weather clutter as well as ground clutter. Experimental results show that these schemes are superior to the adaptive lattice prediction error filter.     

Synchronisation-burst spectra for the switching satellite

Synchronisation of Earth stations to a switching communications satellite is achieved by transmitting synchronisation-burst signals. When more than one synchronisation-burst passes through the synchronisation window, mutual interference is generated by the frequency sidelobes. This interference can be reduced, however, by amplitude modulation of the synchronisation-burst signals.     

AR-based growler detection in sea clutter

A technique that performs better than conventional, noncoherent marine radars in detecting small pieces of glacial ice (growlers) in the sea is reported. A coherent detector based on an autoregressive (AR) parameterization of received radar echoes that distinguishes growler returns from the background sea clutter returns is considered. A conventional noncoherent detector is also evaluated as a basis for comparison. The AR-based detector is shown to offer significant improvements in detecting growlers. The results reported are based on real X-band radar data     

A dynamic channel assignment policy through Q-learning

One of the fundamental issues in the operation of a mobile communication system is the assignment of channels to cells and to calls. This paper presents a novel approach to solving the dynamic channel assignment (DCA) problem by using a form of real-time reinforcement learning known as Q-learning in conjunction with neural network representation. Instead of relying on a known teacher the system is designed to learn an optimal channel assignment policy by directly interacting with the mobile communication environment. The performance of the Q-learning based DCA was examined by extensive simulation studies on a 49-cell mobile communication system under various conditions. Comparative studies with the fixed channel assignment (FCA) scheme and one of the best dynamic channel assignment strategies, MAXAVAIL, have revealed that the proposed approach is able to perform better than the FCA in various situations and capable of achieving a performance similar to that achieved by the MAXAVAIL, but with a significantly reduced computational complexity.     

Multiple window time-frequency distribution and coherence of EEGusing Slepian sequences and Hermite functions

Multiple window (MW) time-frequency analysis (TFA) is a newly developed technique to estimate a time-varying spectrum for random nonstationary signals with low bias and variance. In this paper, we describe the application of MW-TFA techniques to electroencephalogram (EEG) and compare the results with those of the conventional spectrogram. We find that the MW-TFA provide us with not only low bias and variance time-frequency (TF) distribution for EEG but also TF coherence estimation between a single realization of EEG recorded from two sites. We also compare the performance of the MW-TFA using two sets of windows, Slepian sequences, and Hermite functions. If care is taken in matching the two windows, we find no noticeable difference in the resulting TF representations.     

Optimum nonlinear filtering

This paper is composed of two parts. The first part surveys the literature regarding optimum nonlinear filtering from the (continuous-time) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discrete-time context) to nonlinear filtering. In particular, the results obtained by using a regularized form of radial basis function (RBF) networks are presented in fair detail     

T-BLAST for wireless communications: first experimental results

In earlier papers , we described a novel multitransmit, multireceive (MTMR) antenna system for wireless communications. This new system, turbo Bell-Labs layered space-time (T-BLAST) architecture, combines the benefits of layered space-time coding concepts and turbo principles in the multitransmit, multireceive antenna system design. In particular, the random layered space-time codes designed by using a set of block convolutional codes and random space-time interleavers and the space-time turbo-like decoding operation allow T-BLAST to realize the benefits of MTMR systems in a computationally feasible manner. The goal of this paper is to present experimental results of T-BLAST based on real-life data collected using the Bell-Labs experimental multiple antenna system with eight transmit and five and six receive antennas. The experimental results show the practical virtues of T-BLAST.     

Performance limits of the innovations-based detection algorithm

A theoretical evaluation of the performance limits of the innovations-based detection algorithm, which provides an adaptive algorithm for discriminating between two hypotheses parameterized as autoregressive (AR) models, is presented. Two particular cases are considered. One pertains to a situation in which the variance of the interference (modeled as an AR process) acting alone is known a priori. The other pertains to a situation in which no such knowledge is available. Detailed computer simulations are carried out to confirm the practical validity of the theory