Identifications of some highly-ionized iron and nickel lines in the 200–400 Å region of the solar spectrum

A number of previously unclassified multiplets of Fexiv, xiii, xii, and xi produced by transitions of the type 3s ²3p ⁿ -3s3p ⁿ⁺¹ are identified in the XUV spectrum of the Sun. The iron lines account for most of the previously unidentified strong lines between 330 and 370 Å. Solar observations of especial value for the investigation of the 300–400 Å region were the slitless spectroheliograms of September 22, 1968 (Purcell and Tousey, 1969) and November 4, 1969 (Tousey, 1971) — on which the image of a flare was recorded. Other solar identifications in the same spectral region include the resonance lines of Nixvii and Nixviii, and one 3p-3d multiplet of Fexiii. The solar blend at 417 Å involving the Fexv inter-combination line and Sxiv is resolved.     

The Local Bubble and Interstellar Material Near the Sun

The properties of interstellar matter at the Sun are regulated by our location with respect to a void in the local matter distribution, known as the Local Bubble. The Local Bubble (LB) is bounded by associations of massive stars and fossil supernovae that have disrupted dense interstellar matter (ISM), driving low density intermediate velocity ISM into the void. The Sun appears to be located in one of these flows of low density material. This nearby interstellar matter, dubbed the Local Fluff, has a bulk velocity of ∼19 km s⁻¹ in the local standard of rest. The flow is coming from the direction of the gas and dust ring formed where the Loop I supernova remnant merges into the LB. Optical polarization data suggest that the local interstellar magnetic field lines are draped over the heliosphere. A longstanding discrepancy between the high thermal pressure of plasma filling the LB and low thermal pressures in the embedded Local Fluff cloudlets is partially mitigated when the ram pressure component parallel to the cloudlet flow direction is included.     

Ll-CFAR: a flexible and robust alternative

A new family of constant false alarm rate (CFAR) processors is introduced. An Ll-CFAR forms its noise power estimate by linearly filtering ranked samples from the reference set; the weights of this combination, however, depend not only on the rank, but also on the relative proximity of the sample to the cell under test. From the class of Ll-CFARs may be chosen members which effectively censor spurious targets; members which exhibit impressive control of false alarm in the presence of a clutter edge; and members which are robust against both such inhomogeneities. While the design of such schemes is involved, their implementation is not significantly more burdensome than that of plain ordered statistic CFAR (OS-CFAR). After a discussion of the stochastic training of Ll-CFAR, the performance is thoroughly assessed under the most commonly encountered instances of environmental conditions, and compared with those of classical CFAR techniques     

Steady-state analysis of the constant-frequency clamped seriesresonant converter

A constant-frequency diode-clamped series resonant converter (CFCSRC) is proposed as a solution to problems associated with frequency-controlled resonant converters. This converter has two resonant frequencies, and control is achieved by varying the relative time spent at each switching frequency. Two zero-current-switching (ZCS) modes are examined and plotted in the output plane. An equation is given for the boundary between the two ZCS modes, as well as an expression for the boundary between ZCS and non-ZCS operation; both are plotted in the output plane. The output equation for the main mode is shown to be hyperbolic. Converter peak voltages limited to the input voltages, and peak currents are less than those of the frequency-controlled clamped series resonant converter over a large operating range. Data from a prototype converter are compared with theoretical data and are shown to be in good agreement with the theoretical model     

Investigation of corona initiation voltage at reduced pressures

The results of an experimental study of the 60 Hz ac corona inception voltage at reduced pressures for a cylindrical conductor and a parallel ground plane are presented. Several spacings and bare and insulated conductors of several diameters are considered. Empirical expressions from which a reasonably accurate prediction of the corona inception voltage (CIV) may be made have been inferred from experimental data     

Speech Data Rate Reduction Part II: Applicability of Sensitivity and Error Analysis

This paper deals with the application of modern estimation techniques to the problem of speech data rate reduction. It is desirable to adaptively identify and quantitize the parameters of the speech model. These paramaters cannot be identified and quantized exactly; the performance of the predictor is thereby degraded and this could prevent data reduction. In many cases it is desirable to emply a suboptimal predictor in order to simplify the algorithms, and predictor performance is again degraded. This paper develops sensitivity and error analysis as a potential method for determining quantitatively how speech data reduction system performance is degraded by imprecise parameter knowledge or suboptimal filtering. An intended use of the sensitivity and error analysis algorithms is to determine parameter identification and model structure requirements of configuration concepts for adaptive speech digitizers. First, sensitivity and error analysis algorithms are presented that form the basis for the remainder of the work. The algorithms are then used to determine how imprecise knowledge of vocal tract parameters degrades predictor performance in speech. Transversal filters have previously been proposed for this application. The sensitivity analysis algorithms are then used to determine when and by how much the transverse filter is suboptimal to the Kalman filter. In particular, the question of how effectively a higher order of all-pole model approximates a system with zeros is answered, as this question is of considerable importance in speech. Finally, the physical significance of the innovations process in speech data rate reduction is studied.     

Robust adaptive beamforming for HF surface wave over-the-horizon radar

Adaptive beamforming is used to enhance the detection of target echoes received by high frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars in the presence of spatially structured interference. External interference from natural and man-made sources typically masks the entire range-Doppler search space and is characterized by a spatial covariance matrix that is time-varying or nonstationary over the coherent processing interval (CPI). Adaptive beamformers that update the spatial filtering weight vector within the CPI are likely to suppress such interference most effectively, but the intra-CPI antenna pattern fluctuations result in temporal decorrelation of the clutter which severely degrades subclutter visibility after Doppler processing. A robust adaptive beamformer that effectively suppresses spatially nonstationary interference without degrading subclutter visibility is proposed here. The proposed algorithm is computationally efficient and suitable for practical implementation. Its operational performance is evaluated using experimental data recorded by the Iluka HFSW OTH radar, located near Darwin in far north Australia.     

Langmuir Turbulence and Solar Radio Bursts

Langmuir waves and turbulence resulting from an electron beam-plasma instability play a fundamental role in the generation of solar radio bursts. We report recent theoretical advances in nonlinear dynamics of Langmuir waves. First, starting from the generalized Zakharov equations, we study the parametric excitation of solar radio bursts at the fundamental plasma frequency driven by a pair of oppositely propagating Langmuir waves with different wave amplitudes. Next, we briefly discuss the emergence of chaos in the Zakharov equations. We point out that chaos can lead to turbulence in the source regions of solar radio emissions. This revised version was published online in August 2006 with corrections to the Cover Date.     

Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.