Overview of database projects

The use of entity and object oriented data modeling techniques for managing Computer Aided Design (CAD) is explored

Programming models for sensor networks: a survey

Sensor networks have a significant potential in diverse applications some of which are already beginning to be deployed in areas such as environmental monitoring. As the application logic becomes more complex, programming difficulties are becoming a barrier to adoption of these networks. The difficulty in programming sensor networks is not only due to their inherently distributed nature but also the need for mechanisms to address their harsh operating conditions such as unreliable communications, faulty nodes, and extremely constrained resources. Researchers have proposed different programming models to overcome these difficulties with the ultimate goal of making programming easy while making full use of available resources. In this article, we first explore the requirements for programming models for sensor networks. Then we present a taxonomy of the programming models, classified according to the level of abstractions they provide. We present an evaluation of various programming models for their responsiveness to the requirements. Our results point to promising efforts in the area and a discussion of the future directions of research in this area.

Scalable Emulation of Heterogeneous Systems

The breakdown of Dennard's transistor scaling has driven computing systems toward application-specific accelerators, which can provide orders-of-magnitude improvements in performance and energy efficiency over general-purpose processors. To enable the radical departures from conventional approaches that heterogeneous systems entail, research infrastructure must be able to model processors, memory and accelerators, as well as system-level changes---such as operating system or instruction set architecture (ISA) innovations---that might be needed to realize the accelerators' potential. Unfortunately, existing simulation tools that can support such system-level research are limited by the lack of fast, scalable machine emulators to drive execution. To fill this need, in this dissertation we first present a novel machine emulator design based on dynamic binary translation that makes the following improvements over the state of the art: it scales on multicore hosts while remaining memory efficient, correctly handles cross-ISA differences in atomic instruction semantics, leverages the host floating point (FP) unit to speed up FP emulation without sacrificing correctness, and can be efficiently instrumented to---among other possible uses---drive the execution of a full-system, cross-ISA simulator with support for accelerators. We then demonstrate the utility of machine emulation for studying heterogeneous systems by leveraging it to make two additional contributions. First, we quantify the trade-offs in different coupling models for on-chip accelerators. Second, we present a technique to reuse the private memories of on-chip accelerators when they are otherwise inactive to expand the system's last-level cache, thereby reducing the opportunity cost of the accelerators' integration

Applications of reprogrammability in algorithm acceleration

This doctoral thesis consists of an introductory part and eight appended publications, which deal with hardware-based reprogrammability in algorithm acceleration with a specific emphasis on the possibilities offered by modern large-scale Field Programmable Gate Arrays (FPGAs) in computationally demanding applications. The historical evolution of both the theoretical and technological paths culminating in the introduction of reprogrammable logic devices is first outlined. This is followed by defining the commonly used terms in the thesis. The reprogrammable logic market is surveyed, and the architectural structures and the technological reasonings behind them are described in detail. As reprogrammable logic lies between Application Specific Integrated Circuits (ASICs) and general-purpose microprocessors in the implementation spectrum of electronics systems, special attention has been paid to differentiate these three implementation approaches. This has been done to emphasize, that reprogrammable logic offers much more than just a low-volume replacement for ASICs. Design systems for reprogrammable logic are investigated, as the learning curve associated with them is the main hurdle for software-oriented designers for using reprogrammable logic devices. The theoretically important topic of partial reprogrammability is described in detail, but it is concluded, that the practical problems in designing viable development platforms for partially reprogrammable systems will hinder its wide-spread adoption. The main technical, design-oriented, and economic applicability factors of reprogrammable logic are laid out. The main advantages of reprogrammable logic are their suitability for fine-grained bit-level parallelizable computing with a short time-to-market and low upfront costs. It is also concluded, that the main opportunities for reprogrammable logic lie in the potential of high-level design systems, and the ever-growing ASIC design gap. On the other hand, most power-conscious mass-market portable products do not seem to offer major new market potential for reprogrammable logic. The appended publications are examined and compared to contemporaneous research at other research institutions. The conclusion is that for relatively wide classes of well-defined computation problems, reprogrammable logic offers a more efficient solution than a software-centered approach, with a much shorter production cycle than is the case with ASICs.reviewe

Reconfigurable middleware architectures for large scale sensor networks

Wireless sensor networks, in an effort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task reconfiguration and high-level object recomposition. Through the layered approach of SENSIX, the software developer creates a domain-specific sensing architecture by defining a customized task specification and utilizing object inheritance. In addition, SENSIX performs better at large scales (on the order of 1000 nodes or more) than other sensor network middleware which do not include such unified facilities for vertical integration

Advancement in robot programming with specific reference to graphical methods

This research study is concerned with the derivation of advanced robot programming methods. The methods include the use of proprietary simulation modelling and design software tools for the off-line programming of industrial robots. The study has involved the generation of integration software to facilitate the co-operative operation of these software tools. The three major researcli'themes7of "ease of usage", calibration and the integration of product design data have been followed to advance robot programming. The "ease of usage" is concerned with enhancements in the man-machine interface for robo t simulation systems in terms of computer assisted solid modelling and computer assisted task generation. Robot simulation models represent an idealised situation, and any off-line robot programs generated from'them may contain'discrepancies which could seriously effect thq programs' performance; Calibration techniques have therefore been investigated as 'a method of overcoming discrepancies between the simulation model and the real world. At the present time, most computer aided design systems operate as isolated islands of computer technology, whereas their product databases should be used to support decision making processes and ultimately facilitate the generation of machine programs. Thus the integration of product design data has been studied as an important step towards truly computer integrated manufacturing. The functionality of the three areas of study have been generalised and form the basis for recommended enhancements to future robot programming systems

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures