Distributed Detection of a Signal in Generalized Gaussian Noise

The problem of distributed detection of a signal in incompletely specified noise is considered. The noise assumed belongs to the generalized Gaussian family and the sensors in the distributed network employ the Wilcoxon test. The sensors pass the test statistics to a fusion center, where a hypothesis testing results in a decision regarding the presence or the absence of a signal. Three monotone and admissible fusion center tests are formulated. Restricted numerical evaluation over a certain parameter range of the noise distribution and the range of signal level indicates that these tests yield performances at comparable levels

Performance Study of Maximum-Likelihood Receivers and Transversal Filters for the Detection of Direct-Sequence Spread-Spectrum Signal in Narrowband Interference

Linear least squares estimation techniques can be used to enhance suppression of narrowband interference in direct-sequence spread-spectrum systems. Nonlinear techniques for this purpose have also been investigated recently. Here, we derive maximum-likelihood receivers for direct-sequence signal in Gaussian interference with known second order characteristics. It is shown that if the receiver uses samples from outside the bit interval, then the receiver structure (called ML II)is nonlinear. The bit error rate performances of these ML receivers are compared to those of linear receivers employing one-sided and two-sided least squares estimation filters, for the case of Gaussian autoregressive interference, It is shown that the ML II receiver outperforms the matched filter, the one sided and the two sided transversal filters

Application of Expectation-Maximization Algorithm to the Detection of Direct-Sequence Signal in Pulsed Noise Jamming

We consider the detection of direct-sequence spread spectrum signal received in pulsed noise jamming enviromment. The Expectation- Maximization Algorithm is used to estimate the unknown jammer parameters and hence obtain a decision on the binary signal based on the estimated IikeIihood functions. The probability of eerror performance of the algorithm is simulated for a repeat code and a (7,4) block code. Simulation results show that at low signal to thermal noise ratio and high jammer power, the EM detector performs significantly better than the hard limiter and somewhat better than the soft limiter. Also, at low SNR, there is little degradauon as compared to the maximum-likelihood detector with true jammer parameters. At high SNR, the soft limiter outperforms the EM detector

On SNR as a Measure of Performance for Narrowband Interference Rejection in Direct Sequence Spread Spectrum Systems

We simulate a nonlinearized Kalman [5], Kalman and a modified Kalman (linear) filter for suppressing a narrowband Gaussian interference in direct sequence spread spectrum receiver and examine the suitability of Signal-to-Noise Ratio (SNR) of the test statistic as a measure of performance of the receiver. We consider Gaussian autoregressive interference with a peaked spectrum and the three cases: small processing gain (PG) and short pseudonoise (PN) sequence, small PG and long PN sequence, and moderate PG and PN sequence. Based on the simulations, we conclude that for the two cases corresponding to small processing gain, if the thermal noise variance is small and the interference is strong, the Gaussian approximation to the test statistic does not yield the correct BER for any of the receivers. For small PG and short PN sequence, even though the SNR corresponding to nonlinear filter is significantly higher than the SNRs of the two linear filters, the BER of the non-linear is higher than that of the linear receivers. SNR is not a useful measure in these situations

On SNR as a Measure of Performance for Narrowband Interference Rejection in Direct Sequence Spread Spectrum Systems

The usefulness of SNR as a figure of merit to quantify the narrowband interference rejection capability of a DS receiver is examined. The interference considered is a peaked autoregressive Gaussian process. The probability of error and SNR estimates of a Kalman, a modified Kalman, and a nonlinear filter proposed in [2] are obtained by simulation. Based on this simulation study and the available theoretical error rate analysis of transversal filters, we can conclude that SNR is a useful measure if the processing gain, PG, of the DS system is moderately large. When the PG is small, such as 7, and if thermal noise is negligible compared to the signal, the SNR is not a reliable measure of performance

Application of Expectation-Maximization Algorithm to the Detection of a Direct-Sequence Signal in Pulsed Noise Jamming

We consider the detection of a direct-sequence spread-spectrum signal received in a pulsed noise jamming environment. The expectation-maximization algorithm is used to estimate the unknown jammer parameters and hence obtain a decision on the binary signal based on the estimated likelihood functions. The probability of error performance of the algorithm is simulated for a repeat code and a (7,4) block code. Simulation results show that at low signal-to-thermal noise ratio and high jammer power, the EM detector performs significantly better than the hard limiter and somewhat better than the soft limiter. Also, at low SNR, there is little degradation as compared to the maximum-likelihood detector with true jammer parameters. At high SNR, the soft limiter outperforms the EM detector

Statistics of Gravitational Microlensing Magnification. I. Two-Dimensional Lens Distribution

(Abridged) In this paper we refine the theory of microlensing for a planar distribution of point masses. We derive the macroimage magnification distribution P(A) at high magnification (A-1 >> tau^2) for a low optical depth (tau << 1) lens distribution by modeling the illumination pattern as a superposition of the patterns due to individual ``point mass plus weak shear'' lenses. We show that a point mass plus weak shear lens produces an astroid- shaped caustic and that the magnification cross-section obeys a simple scaling property. By convolving this cross-section with the shear distribution, we obtain a caustic-induced feature in P(A) which also exhibits a simple scaling property. This feature results in a 20% enhancement in P(A) at A approx 2/tau. In the low magnification (A-1 << 1) limit, the macroimage consists of a bright primary image and a large number of faint secondary images formed close to each of the point masses. Taking into account the correlations between the primary and secondary images, we derive P(A) for low A. The low-A distribution has a peak of amplitude ~ 1/tau^2 at A-1 ~ tau^2 and matches smoothly to the high-A distribution. We combine the high- and low-A results and obtain a practical semi-analytic expression for P(A). This semi-analytic distribution is in qualitative agreement with previous numerical results, but the latter show stronger caustic-induced features at moderate A for tau as small as 0.1. We resolve this discrepancy by re-examining the criterion for low optical depth. A simple argument shows that the fraction of caustics of individual lenses that merge with those of their neighbors is approx 1-exp(-8 tau). For tau=0.1, the fraction is surprisingly high: approx 55%. For the purpose of computing P(A) in the manner we did, low optical depth corresponds to tau << 1/8.Comment: 35 pages, including 6 figures; uses AASTeX v4.0 macros; submitted to Ap

Hyperforin: A lead for antidepressants

Depression is a complex but treatable disorder if diagnosed appropriately. However, despite the advances in the understanding of the molecular basis of this disorder and the vast range of medication, psychotherapy and electroconvulsive therapy, very safe and effective drug to treat this disease is still being sought. Several studies suggest that St.John’s wort (Hypericum perforatum L.) has phloroglucinol derivative, hyperforin, exhibiting antidepressant activity. This bioactive component can be exploited to create a major shift in the safer treatment of depression. Keywords: Hypericum perforatum L., St. John's wort, Antidepressant, Hyperfori

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.Comment: 13 page

Combined Economic and Emission Dispatch Incorporating Renewable Energy Sources and Plug-In Hybrid Electric Vehicles

Conventional transportation and electricity industries are considered as two major sources of greenhouse gases (GHGs) emission. Improvement of vehicle’s operational efficiency can be a partial solution but it is necessary to employ Plug-In Hybrid Electric Vehicles (PHEVs) and Renewable Energy Sources (RESs) in the network to slow the increasing rate of the GHGs emission. However, it is crucial to investigate the effectiveness of each solution. In this paper, a combination of generation cost and GHGs emission of the two mentioned industries, as economic and environmental aspects of using PHEVs and RESs will be analyzed. The effectiveness of five different scenarios of utilizing the mentioned elements is studied on a test system. To have a realistic evaluation, an extended cost function model of wind farm is employed in optimal power dispatch calculations. Particle Swarm Optimization (PSO) algorithm is applied to the combined economic and emission dispatch (CEED) non- linear problem