We propose a federated edge-computing architecture for management of data. Our vision is to enable a service provider model for "data-services", where a user can enter into economic agreements with an infrastructure maintainer to provide storage and communication of data, without necessarily trusting the infrastructure provider. Toward this vision, we present cryptographically hardened cohesive collections of data items called DataCapsules, and an overview of the underlying federated architecture, called Global Data Plane.

Previous studies examined and tested a number of statistics-based registration-free transforms to find targets amid cluttered backgrounds. These transforms temporally evolve spectral target signatures under global, varying conditions using collected imagery of regions of similar objects and content distribution from datasets gathered at two different times. The transformed target signature is then inserted into the matched filter to search for targets. Although critical for transforming spectral target signatures, finding two suitable candidate regions is often difficult, computationally intensive, and may require the aid of an image analyst. This is the first study to examine a metric to help identify suitable areas for spectral target transformation. Specifically, this study examines and finds that the average correlation coefficient between the corrected histograms of the multispectral image cube collected at two times can help assess the similarity of the areas and indicate the target-to-clutter ratio, a metric shown to predict target detection performance in matched filter searches for targets.

The authors have designed and implemented a ring array processor, RAP, for fast implementation of layered neural network algorithms. The RAP is a multi-DSP system targeted at continuous speech recognition using connectionist algorithms. Four boards, each with four Texas Instruments, TMS 320C30 DSPs, serve as an array processor for a 68020-based host running a real-time operating system. The overall system is controlled from a Sun workstation via the Ethernet. Each board includes 16 MB of dynamic memory (expandable to 64 MB) and 1 MB of fast static RAM. Theoretical peak performance is 128 MFLOPS/board, and test runs with the first working board show a sustained throughput of roughly one-third to one-half of this for algorithms of interest. Software development is aided by a Sun workstation-based command interpreter, tools from the standard C environment and a library of matrix and vector routines.>

A ring array processor (RAP) designed for fast simulation of artificial neural network algorithms is described. The RAP is a multiprocessor system which is particularly targeted in the training of feedforward networks for the recognition of continuous speech. The overall system consists of several four-processor boards serving together as an array processor for a 68020-based host running a real-time operating system. The prototype design includes 64 Mbytes of dynamic memory (expandable to 256) and 4 Mbytes of fast static RAM distributed between 16 processors on four boards. Theoretical peak performance is 512 MFLOPS, and simulations have indicated a sustained throughput of roughly half of this for algorithms of current interest, including communication overhead of roughly 10-30%. Initial tests with the new hardware have confirmed the major assumptions of the estimates from the simulations.>

In this paper we examine four performance enhancements to a database management system: dynamic compilation, microcoded routines, a special-purpose file system, and a special-purpose operating system. All were examined in the context of the INGRES database management system. Benchmark timings that are included suggest the attractiveness of dynamic compilation and a special-purpose file system. Microcode and a special-purpose operating system are analyzed and appear to be of more limited utility in the INGRES context.