Single Frequency Bin Research Articles

A novel blind deconvolution algorithm using single frequency bin

Former frequency-domain blind devolution algorithms need to consider a large number of frequency bins and recover the sources in different orders and with different amplitudes in each frequency bin, so they suffer from permutation and amplitude indeterminacy troubles. Based on sliding discrete Fourier transform, the presented deconvolution algorithm can directly recover time-domain sources from frequency-domain convolutive model using single frequency bin. It only needs to execute blind separation of instantaneous mixture once there are no permutation and amplitude indeterminacy troubles. Compared with former algorithms, the algorithm greatly reduces the computation cost as only one frequency bin is considered. Its good and robust performance is demonstrated by simulations when the signal-to-noise-ratio is high.

EEG During Anesthesia Is Not a Linear Random Process

In Response: As Hagihira et al. mention, before calculating the bispectrum and its normalized version, bicoherence, the investigated time series must be proven to be stationary (i.e., its statistical properties must be time-invariant). Because of this important constraint in spectral and bispectral analysis, every artifact-free electroencephalogram (EEG) epoch (8 s, 210 data points) in our studies was first submitted to a commonly used test of stationary status, the so called “run-test.” The estimation of the bispectrum and bicoherence was then performed only for artifact-free EEG epochs statistically proved to be stationary (1–3). In contrast, Hagihira et al. estimated the bispectrum and bicoherence from artifact-free EEG epochs (2 s, 28 data points) by averaging 360 values over time periods (i.e., ≥3 min) that were assumed to be stationary because of a SEF90 of 11 ± 0.18 in 20 patients (4) and 12 ± 1.9 in 30 patients (5) during general anesthesia combined with epidural anesthesia. As this parameter of the EEG power spectrum by itself need to be computed from stationary EEG epochs, it can not serve as surrogate for stationary status in the same time series. Like the power spectrum, the bispectrum and the bicoherence are determined from EEG epochs of finite length. The missing EEG information before and after the EEG epoch translates into a statistical error of estimation of the power spectrum and all higher order spectra, such as the bispectrum. The error of estimation of a single frequency bin of the power spectrum is, for instance, approximately 100% of its value (6). As the bicoherence depends on the bispectrum and the power spectrum, its error of estimation is much larger than 100%. If one would like to determine whether a given EEG time series is a result of a linear random process, one has to test the hypothesis that the bicoherence is a constant (7). In our investigations, this hypothesis could be rejected with the Hinich test in only 6.2%, 9.1%, and 10.62% of the investigated EEG epochs during general anesthesia with isoflurane/alfentanil (3), Isoflurane/N2O (2) and propofol/alfentanil (1), respectively. In their investigations (4,5,8), Hagihira et al. made no statistical estimation of the frequency of nontrivial bicoherence as an indicator of nonlinearity in the analyzed EEG. From a practical point of view, stationary EEG data segments of at least 3 min, as they have been proposed for the accurate computation of one bicoherence value (4), may be difficult to obtain, especially during intensive electrosurgical cautery after skin incision or before skin suture. If one takes into consideration, that the transition between brain states as a result of anesthesia seems to lie within milliseconds (9), than the applicability of the bicoherence as an indicator of the consciousness (5) or analgesic state (8) of a patient during general anesthesia seems to be limited. With respect to these important methodological aspects, the remarks from Hagihira et al. do not change the conclusions from our investigations: 1) the EEG during anesthesia can be considered in more than 90% as a linear random process; and 2) the additional benefit of bispectral analysis for monitoring clinical anesthesia remains to be evaluated. Christian Jeleazcov, MD Jörg Fechner, MD Helmut Schwilden, MD, PhD Klinik für Anästhesiologie der Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen, Germany [email protected]

Efficient matched processing for localisation of a moving acoustic source

In recent years matched processing techniques have emerged as a solution for the problem of target motion analysis. A target's motion parameters are estimated by determining which of a bank of replica signals best correlates with the signal received from that target. Matched processing methods have been used in low SNR environments such as the underwater acoustic environment because they outperform pseudomeasurement-based target motion analysis. However, matched processing is notoriously computationally demanding, requiring evaluation of complex equations for replica generation, and batch processing via an exhaustive search of a multidimensional parameter space. This study presents a matched processing method for localisation of a slow-moving low-frequency acoustic source by matching Fourier coefficients from a single frequency bin. A simple range-dependent propagation model is assumed, and the Doppler effect is carefully modelled to produce realistic replicas. An efficient matching algorithm is presented which computes cost values in a recursive fashion, discarding poorly scoring replicas before they are fully evaluated. Simulated examples demonstrate the ability of the technique to resolve the locus of low-Doppler targets at extremely low SNR with a fraction of the cost associated with exhaustive search batch processing.

Transforming echoes into pseudo-action potentials for classifying plants.

Animals perceive their environment by converting sensory stimuli into action potentials, or temporal point processes, that are interpreted by the brain. This paper investigates the information content of point processes extracted from echoes from in situ plants in an effort to understand how bats recognize landmarks in the field. A mobile sonar converts echoes into biologically similar temporal point processes. termed pseudo-action potentials (PAPs), whose inter-PAP interval relates to echo amplitude. The sonar forms a sector scan of an object to produce a spatial-temporal PAP field. Classifier neurons apply delays and coincidence detection to the PAP field to identify three distinct echo types, glints, blobs, and fuzz, which characterize plant features. Glints are large amplitude echoes exhibiting coherence over successive echoes in the sector scan, typically produced by favorably oriented isolated specular reflectors. Blobs are large echoes lacking coherence, typically bordering glints or formed by collections of interfering reflectors. Fuzz represents weak echoes, typically produced by collection of weak scatterers or by reflectors on the beam periphery. A small mirror reflector models a flat leaf surface and motivates the glint criteria. Classifiers are applied to experimental data from two types of tree trunks, a glint-producing sycamore (Platanus occidenatalis) and a glint-absent Norway maple (Acer platanoides) and two plants, a glint-producing rhododendron (Rhododendron maximus) and a glint-absent yew (Taxus media). We speculate that our narrow-band sonar models the activity of a single frequency bin in the frequency-modulated (FM) sweep emitted by bats, and that one function of the frequency bins in the FM sweep is to form a sector scan of the environment.

Code diversity transmission in a slow-frequency-hopped spread spectrum multiple-access communication system

At every hop in a conventional frequency-hopped spread spectrum multiple-access (FH-SSMA) system, each transmitter sends its data using a single frequency bin. In this paper, a scheme in which several frequency bins are simultaneously used by a transmitter is studied. It is found that such a code diversity scheme can yield a significant reduction in symbol error rate over that of a conventional FH-SSMA system. A priority system can also be implemented by assigning different diversity degrees to the transmitters.