Multiple feature points representation in target localization of wireless visual sensor networks

This paper discusses the target localization problem in wireless visual sensor networks. Additive noises and measurement errors will affect the accuracy of target localization when the visual nodes are equipped with low-resolution cameras. In the goal of improving the accuracy of target localization without prior knowledge of the target, each node extracts multiple feature points from images to represent the target at the sensor node level. A statistical method is presented to match the most correlated feature point pair for merging the position information of different sensor nodes at the base station. Besides, in the case that more than one target exists in the field of interest, a scheme for locating multiple targets is provided. Simulation results show that, our proposed method has desirable performance in improving the accuracy of locating single target or multiple targets. Results also show that the proposed method has a better trade-off between camera node usage and localization accuracy.

Feature issue introduction: light, energy and the environment, 2014

This feature issue highlights contributions from authors who presented their research at the OSA Light, Energy and the Environment Congress, held in Canberra, Australia from 2-5 December, 2014.

Ectopic expression of the minimal whiE polyketide synthase generates a library of aromatic polyketides of diverse sizes and shapes

The single recombinant expressing the Streptomyces coelicolor minimal whiE (spore pigment) polyketide synthase (PKS) is uniquely capable of generating a large array of well more than 30 polyketides, many of which, so far, are novel to this recombinant. The characterized polyketides represent a diverse set of molecules that differ in size (chain length) and shape (cyclization pattern). This combinatorial biosynthetic library is, by far, the largest and most complex of its kind described to date and indicates that the minimal whiE PKS does not independently control polyketide chain length nor dictate the first cyclization event. Rather, the minimal PKS enzyme complex must rely on the stabilizing effects of additional subunits (i.e., the cyclase whiE-ORFVI) to ensure that the chain reaches the full 24 carbons and cyclizes correctly. This dramatic loss of control implies that the growing polyketide chain does not remain enzyme bound, resulting in the spontaneous cyclization of the methyl terminus. Among the six characterized dodecaketides, four different first-ring cyclization regiochemistries are represented, including C7/C12, C8/C13, C10/C15, and C13/C15. The dodecaketide TW93h possesses a unique 2,4-dioxaadamantane ring system and represents a new structural class of polyketides with no related structures isolated from natural or engineered organisms, thus supporting the claim that engineered biosynthesis is capable of producing novel chemotypes.

Feature integration in pattern perception

The human visual system is able to effortlessly integrate local features to form our rich perception of patterns, despite the fact that visual information is discretely sampled by the retina and cortex. By using a novel perturbation technique, we show that the mechanisms by which features are integrated into coherent percepts are scale-invariant and nonlinear (phase and contrast polarity independent). They appear to operate by assigning position labels or “place tags” to each feature. Specifically, in the first series of experiments, we show that the positional tolerance of these place tags in foveal, and peripheral vision is about half the separation of the features, suggesting that the neural mechanisms that bind features into forms are quite robust to topographical jitter. In the second series of experiment, we asked how many stimulus samples are required for pattern identification by human and ideal observers. In human foveal vision, only about half the features are needed for reliable pattern interpolation. In this regard, human vision is quite efficient (ratio of ideal to real ≈ 0.75). Peripheral vision, on the other hand is rather inefficient, requiring more features, suggesting that the stimulus may be relatively underrepresented at the stage of feature integration.

The central structural feature of the membrane fusion protein subunit from the Ebola virus glycoprotein is a long triple-stranded coiled coil

The ectodomain of the Ebola virus Gp2 glycoprotein was solubilized with a trimeric, isoleucine zipper derived from GCN4 (pIIGCN4) in place of the hydrophobic fusion peptide at the N terminus. This chimeric molecule forms a trimeric, highly α-helical, and very thermostable molecule, as determined by chemical crosslinking and circular dichroism. Electron microscopy indicates that Gp2 folds into a rod-like structure like influenza HA2 and HIV-1 gp41, providing further evidence that viral fusion proteins from diverse families such as Orthomyxoviridae (Influenza), Retroviridae (HIV-1), and Filoviridae (Ebola) share common structural features, and suggesting a common membrane fusion mechanism.

Formation of temporal-feature maps by axonal propagation of synaptic learning

Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 μs. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre- and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.

Seeing properties of an invisible object: Feature inheritance and shine-through

We characterize a class of spatio-temporal illusions with two complementary properties. Firstly, if a vernier stimulus is flashed for a short time on a monitor and is followed immediately by a grating, the latter can express features of the vernier, such as its offset, its orientation, or its motion (feature inheritance). Yet the vernier stimulus itself remains perceptually invisible. Secondly, the vernier can be rendered visible by presenting gratings with a larger number of elements (shine-through). Under these conditions, subjects perceive two independent “objects” each carrying their own features. Transition between these two domains can be effected by subtle changes in the spatio-temporal layout of the grating. This should allow psychophysicists and electrophysiologists to investigate feature binding in a precise and quantitative manner.

Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach

A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source–sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.

Phosphorylation of ubiquitin-activating enzyme in cultured cells.

Ubiquitin-activating enzyme, E1, is the first enzyme in the pathway leading to formation of ubiquitin-protein conjugates. E1 exists as two isoforms in human cells which are separable by electrophoresis. These isoforms migrate with apparent molecular sizes of 110 kDa and 117 kDa in SDS/polyacrylamide gels. Immunoprecipitation of E1 from lysates of HeLa cells metabolically labeled with [32P]phosphate indicated the presence of a phosphorylated form of E1 which migrates at 117 kDa. Phospho amino acid analysis identified serine as the phosphorylated residue in E1. Phosphorylated E1 was also detected in normal and transformed cells from another human cell line. Phosphatase-catalyzed dephosphorylation of E1 in vitro did not eliminate the 117-kDa E1 isoform detected by Coomassie staining after SDS/polyacrylamide gel electrophoresis, thereby demonstrating that phosphorylation is not the sole structural feature differentiating the isoforms of E1. These observations suggest new hypotheses concerning mechanisms of metabolic regulation of the ubiquitin conjugation pathway.

A novel information theory method for filter feature selection

In this paper, we propose a novel filter for feature selection. Such filter relies on the estimation of the mutual information between features and classes. We bypass the estimation of the probability density function with the aid of the entropic-graphs approximation of Rényi entropy, and the subsequent approximation of the Shannon one. The complexity of such bypassing process does not depend on the number of dimensions but on the number of patterns/samples, and thus the curse of dimensionality is circumvented. We show that it is then possible to outperform a greedy algorithm based on the maximal relevance and minimal redundancy criterion. We successfully test our method both in the contexts of image classification and microarray data classification.

Exploring automatic feature selection for transition-based dependency parsing

En este artículo se investigan técnicas automáticas para encontrar un modelo óptimo de características en el caso de un analizador de dependencias basado en transiciones. Mostramos un estudio comparativo entre algoritmos de búsqueda, sistemas de validación y reglas de decisión demostrando al mismo tiempo que usando nuestros métodos es posible conseguir modelos complejos que proporcionan mejores resultados que los modelos que siguen configuraciones por defecto.

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.

3D Feature Extraction and Modelling for SLAM

Paper submitted to the 39th International Symposium on Robotics ISR 2008, Seoul, South Korea, October 15-17, 2008.

Discussing the physical meaning of the absorption feature at 2.1 keV in 4U 1538–52

High resolution X-ray spectroscopy is a powerful tool for studying the nature of the matter surrounding the neutron star in X-ray binaries and its interaction between the stellar wind and the compact object. In particular, absorption features in their spectra could reveal the presence of atmospheres of the neutron star or their magnetic field strength. Here we present an investigation of the absorption feature at 2.1 keV in the X-ray spectrum of the high mass X-ray binary 4U 1538–52 based on our previous analysis of the XMM-Newton data. We study various possible origins and discuss the different physical scenarios in order to explain this feature. A likely interpretation is that the feature is associated with atomic transitions in an O/Ne neutron star atmosphere or of hydrogen and helium like Fe or Si ions formed in the stellar wind of the donor.