Fusion of enhanced and synthetic vision system images for runway and horizon detection

Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications

Locus Interactions Underlie Seed Yield In Soybeans Resistant to Heterodera glycines

In soybean (Glycine max L. Merr.) combining resistance to cyst nematode (SCN; Heterodera glycines I.) with high seed yieldremains problematic. Molecular markers linked to quantitative trait loci (QTL) have not provided a solution. Sets of markers describing a collection of favorable alleles (linkats) may assist plant breeders seeking to combine both traits. The objective of this analysis was to identify linkats in genomic regions underlying seed yield and root SCN resistance QTL. Used were groups of cultivars selected from a single recombinant inbred (RIL) population derived from \u27Essex\u27 by \u27Forrest\u27 (ExF). The yield was measured at four locations. SCN resistance was determined in greenhouse assays. The mean seed yield was used to define 3 groups (each n = 30), high, medium and low. SCN resistance formed 2 groups (SCN resistant (n = 21) and SCN susceptible (n = 69)). Microsatellite markers (213) alleles were compared with seed yield and root SCN (Hetrodera glycines) resistance using mean analysis. The number, size and position of potential linkats were determined. Loci, genomic regions and linkats associated with seed yield were identified on linkage group (LG) K and with root resistance to SCN e on LG E, G, and D1b+W. A method to identify co-localized genomic regions is presented

Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives

This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided

Real-time hospital bed occupancy and requirements forecasting

To ensure better utilization and availability of the healthcare resources healthcare managers, planners and hospital staff need to develop policies. The hospital length of stay (LOS) of patients and therefore the resource requirements depend on many factors such as the covariates that represent the characteristics of the patients. Here we have used the discharge dataset of Mater Dei Hospital, Malta to model the LOS and admissions. Phase type survival tree is used to cluster patients into homogeneous groups with respect to the LOS and admissions.peer-reviewe

Look-Alike Drugs-‘AWonder or a Blunder’?

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Permutation Matrix Representation Quantum Monte Carlo

We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its off-diagonal terms and is both parameter-free and Trotter error-free. In our approach, the quantum dimension consists of products of elements of a permutation group. As such, it allows for the study of a very wide variety of models on an equal footing. To demonstrate the utility of our technique, we use it to clarify the emergence of the sign problem in the simulations of non-stoquastic physical models. We showcase the flexibility of our algorithm and the advantages it offers over existing state-of-the-art by simulating transverse-field Ising model Hamiltonians and comparing the performance of our technique against that of the stochastic series expansion algorithm. We also study a transverse-field Ising model augmented with randomly chosen two-body transverse-field interactions.Comment: 15 pages, 6 figure