Underwater sensor networks (UWSNs) have been investigated in a variety of applications such as sea resources reconnaissance, pollution monitoring and tactical monitoring. In 3D underwater environments, it is a key topic to judge the localizability of sensor nodes given known locations of a small set of anchor nodes. In this paper, a novel localizability judgment method for UWSNs is proposed based on rigidity theory. A UWSN is modelled as an undirected graph based on acoustic connectivity. The graph is then reduced to a subgraph with global rigidity, called skeleton, from which the set of localizable sensors can determined. Furthermore, the Analytic Hierarchy Process (AHP) is used to evaluate the localization confidence of localizable sensors. Extensive simulations demonstrate that the proposed localizability judgment method can achieve low false negative rate and high efficiency networks of different sensor numbers and sensor densities. It is also shown to perform well in dynamic networks with relatively low waterflow speed.

Acoustic noise has been reported as a major problem in Switched Reluctance Machines (SRMs) for noise sensitive applications. This issue has drawn significant attention over the past decades. In this paper, a numerical method used to predict vibration with high computational efficiency is introduced. The accuracy of this method is demonstrated by the comparison of the calculated results and the multiphysics Finite Element Analysis (FEA) simulation. Finally, based on this method, a fast process for the optimal design of the stator frame is proposed to reduce the vibration and acoustic noise in SRMs.

Sphere detection (SD) can significantly reduce the complexity of multiple-input multiple-output (MIMO) maximum-likelihood (ML) detection, but the complexity of SD is still too high to be applied to the practical system. In order to reduce the complexity of SD, two novel reordering methods based minimum mean square error (MMSE) detection are presented in this paper. Rule-R is based on the reliability of the MMSE detector's decision variable which depends on the magnitude and the SINR of the decision variable. The other method, Rule-M, is a simplified version of Rule-R and only depends on the magnitude of the decision variable. We show that both of them can significantly reduce the complexity with few additional computations. For original SD, both the rules cause no performance degradation. While if some other suboptimal SD were used, the proposed algorithms can both provide computational efficiency and performance improvement.

Tree pruning can significantly reduce the complexity of sphere decoding (SD). How to determine the pruning rule is an open problem of tree pruning. In this paper, we propose a pruning strategy for SD based on path metric. Because only the nearest lattice point is concerned, if the ratio of the metric to the minimum metric is larger than a threshold, the path whose metric is large enough can be pruned. We analyze the influence of the choice of the thresholds on the performance and the complexity. Through analysis and the simulations, we can show that the complexity reduction is significant while maintaining the negligible performance degradation when proper thresholds are chosen. Besides, tradeoff between complexity and performance can be easily achieved by adjusting the thresholds.

BACKGROUND: the decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing. RESULTS: we have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested. CONCLUSIONS: VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline.

An approach to automatic prediction and detection of ovulation is described. It is based on the application of image processing techniques to the cervical mucus fern test, a popular clinical diagnostic method. The sequence of histogram equalization, filtering, edge detection, binarization, labeling, thinning, Hough transform, and automatic pattern recognition in a feature space is applied to microscopic images of the ferning patterns. This method permits decisions to be made based on quantitative data instead of the subjective evaluations that are presently used.>