S+Net: extending functional coordination with extra-functional semantics

This technical report introduces S+Net, a compositional coordination language for streaming networks with extra-functional semantics. Compositionality simplifies the specification of complex parallel and distributed applications; extra-functional semantics allow the application designer to reason about and control resource usage, performance and fault handling. The key feature of S+Net is that functional and extra-functional semantics are defined orthogonally from each other. S+Net can be seen as a simultaneous simplification and extension of the existing coordination language S-Net, that gives control of extra-functional behavior to the S-Net programmer. S+Net can also be seen as a transitional research step between S-Net and AstraKahn, another coordination language currently being designed at the University of Hertfordshire. In contrast with AstraKahn which constitutes a re-design from the ground up, S+Net preserves the basic operational semantics of S-Net and thus provides an incremental introduction of extra-functional control in an existing language.Comment: 34 pages, 11 figures, 3 table

High Performance Software Reconfiguration in the Context of Distributed Systems and Interconnection Networks.

Designed algorithms that are useful for developing protocols and supporting tools for fault tolerance, dynamic load balancing, and distributing monitoring in loosely coupled multi-processor systems. Four efficient algorithms are developed to learn network topology and reconfigure distributed application programs in execution using the available tools for replication and process migration. The first algorithm provides techniques for transparent software reconfiguration based on process migration in the context of quadtree embeddings in Hypercubes. Our novel approach provides efficient reconfiguration for some classes of faults that may be identified easily. We provide a theoretical characterization to use graph matching, quadratic assignment, and a variety of branch and bound techniques to recover from general faults at run-time and maintain load balance. The second algorithm provides distributed recognition of articulation points, biconnected components, and bridges. Since the removal of an articulation point disconnects the network, knowledge about it may be used for selective replication. We have obtained the most efficient distributed algorithms with linear message complexity for the recognition of these properties. The third algorithm is an optimal linear message complexity distributed solution for recognizing graph planarity which is one of the most celebrated problems in graph theory and algorithm design. Recently, efficient shortest path algorithms are developed for planar graphs whose efficient recognition itself was left open. Our algorithm also leads to designing efficient distributed algorithm to recognize outer-planar graphs with applications in Hamiltonian path, shortest path routing and graph coloring. It is shown that efficient routing of information and distributing the stack needed for for planarity testing permit local computations leading to an efficient distributed algorithm. The fourth algorithm provides software redundancy techniques to provide fault tolerance to program structures. We consider the problem of mapping replicated program structures to provide efficient communication between modules in multiple replicas. We have obtained an optimal mapping of 2-replicated binary trees into hypercubes. For replication numbers greater than two, we provide efficient heuristic simulation results to provide efficient support for both \u27N-version programming\u27 and \u27Recovery block\u27 approaches for software replication

Planning assistance for the NASA 30/20 GHz program. Network control architecture study.

Network Control Architecture for a 30/20 GHz flight experiment system operating in the Time Division Multiple Access (TDMA) was studied. Architecture development, identification of processing functions, and performance requirements for the Master Control Station (MCS), diversity trunking stations, and Customer Premises Service (CPS) stations are covered. Preliminary hardware and software processing requirements as well as budgetary cost estimates for the network control system are given. For the trunking system control, areas covered include on board SS-TDMA switch organization, frame structure, acquisition and synchronization, channel assignment, fade detection and adaptive power control, on board oscillator control, and terrestrial network timing. For the CPS control, they include on board processing and adaptive forward error correction control

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision