Unusually low coronal radio emission at the solar minimum

We present two-dimensional observations of the quiet Sun at 73.8, 50.0, and 38.5 MHz obtained with the Clark Lake Radioheliograph during the sunspot minimum period of September 1986. The observed peak brightness temperatures during the entire period of sunspot minimum are found to be extremely low, lying in the range (0.6 × 10⁵ K – 2.5 × 10⁵ K). It is shown that these low values cannot be explained by the generally adopted models for N _e and T _e in a homogeneous corona. The effect of scattering by random density fluctuations is introduced in order to decrease the values of predicted T _b . The value of peak T _b is computed as a function of relative r.m.s. density fluctuations = <N _e >/N _e ; and it is found that should be in the range from 0.07 to 0.19, 0.1 to 0.25, and 0.15 to 0.35, respectively, at 38.5, 50.0, and 73.8 MHz, respectively, to explain the observed low brightness temperatures.On leave from Indian Institute of Astrophysics, Bangalore, India.     

The impulsive phase of a large solar limb flare of June 20, 1989

On 1989 June 20, we observed in H the impulsive phase of a 3B/X1.6 limb flare with high temporal resolution. Line profiles have been acquired every 2.3 s with an imaging spectrograph. During the eruption of a filament we observed in H a moving plasma blob from what we believe to be a second loop which correlated spatially and temporally with a microwave source at 1.4 GHz observed by VLA. A magnetodynamic model is used to understand the development of the moving plasma blob.     

Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps

Evaluation of maps generated from different conceptual models or data processing approaches at spatial level has importance in many geoenvironmental applications. This paper addresses the spatial comparison of different landslide susceptibility zonation (LSZ) raster maps of the same area derived from various procedures.     

Automated Target Detection and Discrimination Using Constrained Kurtosis Maximization

Exploiting hyperspectral imagery without prior information is a challenge. Under this circumstance, unsupervised target detection becomes an anomaly detection problem. We propose an effective algorithm for target detection and discrimination based on the normalized fourth central moment named kurtosis, which can measure the flatness of a distribution. Small targets in hyperspectral imagery contribute to the tail of a distribution, thus making it heavier. The Gaussian distribution is completely determined by the first two order statistics and has zero kurtosis. Consequently, kurtosis measures the deviation of a distribution from the background and is suitable for anomaly/target detection. When imposing appropriate inequality constraints on the kurtosis to be maximized, the resulting constrained kurtosis maximization (CKM) algorithm will be able to quickly detect small targets with several projections. Compared to the widely used unconstrained kurtosis maximization algorithm, i.e., fast independent component analysis, the CKM algorithm may detect small targets with fewer projections and yield a slightly higher detection rate.     

Population growth and the development of a central place system

This paper describes the spatial and functional evolution of a central place system as market conditions change with population growth. Utilizing a partial equilibrium optimization model, we examine the spatial response of two economic sectors to increases in market populations resulting from natural increase and migration. Response in both sectors is conditioned by threshold demand, with factor prices also affecting one of the sectors. As the central place system evolves it exhibits spatial and functional characteristics that are initially consistent with a Löschian landscape, then a Christallerian landscape at higher populations, while at even larger populations Krugman’s landscape emerges.     

Using a Grid-Based Filter to Solve InSAR Phase Unwrapping

This letter presents a phase-unwrapping (PU) algorithm for synthetic aperture radar interferometry based on a grid-based filter. The proposed PU algorithm, which is based on state-space techniques, simultaneously performs noise filtering and PU. The formulation of this technique provides independence from noise statistics and is not constrained by the nonlinearity of the problem. Results obtained with synthetic data show a significant improvement with respect to other conventional PU algorithms in some situations.