A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Grey-Box Modeling for Photo-Voltaic Power Systems Using Dynamic Neural-Networks

There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities

Observation of Intensity-Intensity Correlation Speckle Patterns with Thermal Light

In traditional Hanbury Brown and Twiss (HBT) schemes, the thermal intensity-intensity correlations are phase insensitive. Here we propose a modified HBT scheme with phase conjugation to demonstrate the phase-sensitive and nonfactorizable features for thermal intensity-intensity correlation speckle. Our scheme leads to results that are similar to those of the two-photon speckle. We discuss the possibility of the experimental realization. The results provide us a deeper insight of the thermal correlations and may lead to more significant applications in imaging and speckle technologies.Comment: 5 pages, 5 figure

Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms