A hierarachical data structure representation for fusing multisensor information

A major problem with MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the data element, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Data-element fusion has problems with coregistration. Attempts to fuse information using the features of segmented data relies on a Presumed similarity between the segmentation characteristics of each data stream. Symbolic-level fusion requires too much advance processing (including object identification) to be useful. MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Data Structure (HDS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The HDS is an intermediate representation between the raw or smoothed data stream and symbolic interpretation of the data. it represents the structural organization of the data. Fused HDSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based region interpretation

Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph-toplogies

Deep Learning methods, specifically convolutional neural networks (CNNs), have seen a lot of success in the domain of image-based data, where the data offers a clearly structured topology in the regular lattice of pixels. This 4-neighbourhood topological simplicity makes the application of convolutional masks straightforward for time series data, such as video applications, but many high-dimensional time series data are not organised in regular lattices, and instead values may have adjacency relationships with non-trivial topologies, such as small-world networks or trees. In our application case, human kinematics, it is currently unclear how to generalise convolutional kernels in a principled manner. Therefore we define and implement here a framework for general graph-structured CNNs for time series analysis. Our algorithm automatically builds convolutional layers using the specified adjacency matrix of the data dimensions and convolutional masks that scale with the hop distance. In the limit of a lattice-topology our method produces the well-known image convolutional masks. We test our method first on synthetic data of arbitrarily-connected graphs and human hand motion capture data, where the hand is represented by a tree capturing the mechanical dependencies of the joints. We are able to demonstrate, amongst other things, that inclusion of the graph structure of the data dimensions improves model prediction significantly, when compared against a benchmark CNN model with only time convolution layers

Altered Histofluorescent Pattern of Noradrenergic Innervation of the Cerebellum of the Mutant Mouse Purkinje Cell Degeneration

The Purkinje target cells for noradrenergic fibers originating in the locus coeruleus are considered to be of importance in the regulation of noradrenergic input to the cerebellum. The availability of a mouse mutant, Purkinje cell degeneration provides a non-surgical means for studying cellular regulation of innervation. Using a glyoxylic acid histofluorescent method for visualizing noradrenergic fibers, the observations have been made that the density of green histofluorescent neuntes is markedly increased in both the granule and molecular layers of the cerebellum of Purkinje cell degeneration mice, following spontaneous degeneration of the Purkinje cells. However, because of tissue shrinkage, tissue concentration of norepinephrine also increases, but total tissue content of norepinephrine is unchanged in whole cerebellum and outer cerebellar cortex. These findings indicate that the relative number of noradrenergic afferents to the molecular layer of the cerebellum is not reduced following spontaneous degeneration of Purkinje cells. Therefore, Purkinje target cells do not appear to be essential for maintenance of afferent inputs in mature cerebellum

Opiate-Enhanced Toxicity and Noradrenergic Sprouting in Rats Treated With 6-Hydroxydopa

Because endorphin receptor activation alters the function of the central noradrenergic system, opiates may change the regenerative sprouting of neurons in response to adrenergic neurotoxins. To test this hypothesis, newborn rats were treated with several opioids and 6-hydroxydopa (6-OHDOPA) and the development of the noradrenergic system was evaluated. In combination with 6-OHDOPA morphine and naloxone potentiated the development of norepinephrine (NE) levels in the pons-medulla and cerebellum by four weeks of age. β-Endorphin, Leu- and Met-enkephalin and d-Ala2-enkephalinamide produced a similar effect in the pons-medulla. The effect of morphine was partially attenuated by naloxone. Increased cerebellar noradrenergic histofluorescent staining was observed with the morphine + 6-OHDOPA and naloxone + 6-OHDOPA treatments. Both naloxone and morphine decreased NE levels in the pons-medulla of adult rats treated with 6-OHDOPA. These results suggest that opiates and endorphins may enhance sprouting of noradrenergic neurons following neonatal treatment with 6-OHDOPA, by increasing the toxicity of this neurotoxin

Developmental Localization of Noradrenergic Innervation to the Rat Cerebellum Following Neonatal 6-Hydroxydopa and Morphine Treatment

In order to demonstrate the influence of morphine on the developmental localization of regenerated noradrenergic fibers in rat cerebellum, a glyoxylic histofluorescent method and radiometric assay for norepinephrine (NE) were utilized. An initial reduction of NE in the cerebellum after 6-hydroxydopa [6-OHDOPA; 60 µg/g intraperitoneally (i.p.)] was followed by a return to control levels at 3 days, and an elevation above control levels at 7 days. The initial rates of recovery of NE in the cerebellum of the 6-OHDOPA group of rats and the group receiving morphine (20 µg/g i.p.) in combination with 6-OHDOPA were identical up to 7 days. However, by 14 days NE content was further elevated in the cerebellum of the morphine+6-OHDOPA group. Histofluorescent microscopic observations of the cerebellar cortex correlated with the biochemical findings. A reduction in cerebellar NE content at 3 days was associated with a reduction in the number of visible histofluorescent fibers in the cerebellar cortex. By 7 days the relative number of fibers in the 6-OHDOPA groups was similar to that seen in the control group, but by 9 days the relative number of fluorescent fibers in the cerebellar cortex was increased above control. By 13 days there was a further increase in the relative number of fluorescent fibers in the cerebellar cortex of the morphine+6-OHDOPA group, as compared to the group treated with 6-OHDOPA alone. These findings provide an anatomic correlate for recovery of noradrenergic fibers after 6-OHDOPA, and demonstrate an action of morphine in enhancing regenerative sprouting

LAND USE CHANGE DETECTION FROM SATELLITE IMAGERY

One of the more important recent technological advances for scientific research has been the development of a satellite specifically designed to gather information about the earth's resources. Two satellites launched by the National Aeronautics and Space Administration and known as LANDSAT 1 and 2, currently orbit the earth in a systematic fashion every nine days gathering data for virtually every point on the globe. This new source of data has important implications for research on natural resource use