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AbstractÐA class of highly scalable interconnect topologies called the Scalable Optical Crossbar-Connected Interconnection Networks (SOCNs) is proposed. This proposed class of networks combines the use of tunable Vertical Cavity Surface Emitting Lasers (VCSEL's), Wavelength Division Multiplexing (WDM) and a scalable, hierarchical network architecture to implement large-scale optical crossbar based networks. A free-space and optical waveguide-based crossbar interconnect utilizing tunable VCSEL arrays is proposed for interconnecting processor elements within a local cluster. A similar WDM optical crossbar using optical fibers is proposed for implementing intercluster crossbar links. The combination of the two technologies produces large-scale optical fan-out switches that could be used to implement relatively low cost, large scale, high bandwidth, low latency, fully connected crossbar clusters supporting up to hundreds of processors. An extension of the crossbar network architecture is also proposed that implements a hybrid network architecture that is much more scalable. This could be used to connect thousands of processors in a multiprocessor configuration while maintaining a low latency and high bandwidth. Such an architecture could be very suitable for constructing relatively inexpensive, highly scalable, high bandwidth, and fault-tolerant interconnects for large-scale, massively parallel computer systems. This paper presents a thorough analysis of two example topologies, including a comparison of the two topologies to other popular networks. In addition, an overview of a proposed optical implementation and power budget is presented, along with analysis of proposed media access control protocols and corresponding optical implementation.

Structural segmentation of 3-D point-cloud data is an important step in the acquisition, recognition and visual representation of objects from point data. Associating groups of points that are consistent with structural surface elements allows decision making based on object components that are much more meaningful that the points alone. Processing begins by filtering the 3-D point-cloud data to smooth surfaces and remove noise. Filtering is essential for accurate surface-normal estimation. Our point filtering algorithm steps a 3-D box through the data, using an efficient search algorithm that employs priority queues for sequential sorting in x, y, and z. Filtering is based on the computation of a best planar fit at each box location. After filtering, processing continues by again stepping through the data and computing local surface normals at each filtered point. We then compute a Minimum Spanning Tree (MST) with nodes consisting of the filtered points, edges established by proximity, and edge weights set as the Euclidean distance between local surface normals. A modified range tree that is computed on the fly from unsorted point data is used in implementing the MST. We then employ a novel procedure to determine the edges that should be broken, leaving subgraphs that represent structural surfaces. These surfaces have been used for visual display of 3-D LADAR data, extraction of surfaces for automatic detection of buildings, and differentiation between man-made and natural objects. BACKGROUNDThree-dimensional point-cloud data such as that provided by newly available LADAR sensors presents opportunities and challenges for automated exploitation. Point clouds contain shape information that affords new possibilities for recognition of remotely sensed objects. Multiple-view combination and range-gating approaches can be used to capture signatures of objects hidden beneath trees or semi-permeable camouflage. Point-cloud data sets can, however, be very large, and often do not directly support discrete, spatially relevant indexing schemes. As a result, fundamental operations, such as 3-D searching and sorting, are much more difficult to perform efficiently than their 2-D counterparts. Approaches to higher-level information extraction are also complicated by the increased dimensionality of the data.We present effective point-cloud processing strategies, using graph data structures that capture information and relationships between spatially localized neighborhoods of points. Graphs provide a framework for extracting structurally meaningful components of complex objects in a manner that is computationally efficient. We divide our presentation into two broad classes of algorithms. First, in Section 2, we present algorithms that work on raw point data grouped into spatially localized neighborhoods. Next, in Section 3, we present graph algorithms for segmentation and region growing of point-cloud data. These algorithms build on those in Section 2 by using associations and characterizations of point groups that are extracted...

A design for an all-optical crossbar network utilizing wavelength-tunable vertical-cavity surface-emitting laser ͑VCSEL͒ technology and a combination of free-space optics and compact optical waveguides is presented. Polymer waveguides route the optical signals from a spatially distributed array of processors to a central free-space optical crossbar, producing a passive, all-optical, fully connected crossbar network directly from processor to processor. The analyzed network could, relatively inexpensively, connect local clusters of tightly integrated processors. In addition, it is also believed that such a network could be extended, with wavelength reuse, to connect much larger numbers of processors in a multicluster network.

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