A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

Image Compression Using Cascaded Neural Networks

Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented

Image Compression Using Cascaded Neural Networks

Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented

XML-VM: An XML-Based Grid Computing Middleware

This paper describes a novel distributing computing middleware named XML-VM. Its architecture is inspired by the \u2018Grid Computing\u2019 paradigm. The proposed system improves many characteristics of previous Grid systems, in particular the description of the distributed computation, the distribution of the code and the execution times. XML is a markup language commonly used to interchange arbitrary data over the Internet. The idea behind this work is to use XML to describe algorithms; XML documents are distributed by means of XML-RPC, interpreted and executed using virtual machines. XML-VM is an assembly-like language, coded in XML. Parsing of XML-VM programs is performed with a fast SAX parser for JAVA. XML-VM interpreter is coded in JAVA. Several algorithms are written in XML-VM and executed in a distributed environment. Representative experimental results are reported

Quantum correlations in nanophotonics: from long-range dipole-dipole interactions to fundamental efficiency limits of coherent energy transfer

Quantum properties like coherence and entanglement can lead to enhanced performance characteristics in a wide range of applications including quantum computation, quantum memory storage, optical sensing, and energy harvesting. Entanglement is very sensitive to static and dynamical disorder. Similarly, the generation of highly-entangled states requires strong coupling or strong driving fields. Satisfying all of these requirements is generally quite difficult. In the first part of this thesis, we present an approach to overcome these limitations through the use of exotic light-matter states in hyperbolic media which provide a new approach to control quantum correlations and interatomic interactions. We reveal a class of excited-state, long-range interactions, referred to as Super-Coulombic interactions that are singular along a material-dependent resonance angle. In practical systems, the Super-Coulombic interaction achieves dipole-dipole coupling that is orders of magnitude larger than conventional approaches, while also occurring across a large frequency bandwidth making it robust to static energy-level disorder. This unique hyperbolic response is not only naturally occurring, found in materials like h-BN, BiTe2, BiSe2, and mono-layered black phosphorus, but can also be designed with artificial nanostructured materials (metamaterials) to create the desired hyperbolic dispersion across different parts of the electromagnetic spectrum. Our theoretical prediction motivated an intense search for the effect and was confirmed by an experimental demonstration at room temperature. To obtain agreement with experimental results, we present a rigorous theoretical framework that takes into account ensemble effects, finite-sized effects, and dimensional effects that arise from confined geometries ultimately modifying the Super-Coulombic spatial scaling law. In the second part of this thesis, we solve an outstanding theoretical problem dealing with the control of resonance energy transfer in nanophotonic environments in both the incoherent and coherent coupling limits. Resonance energy transfer is a fundamental process that is the subject of intense research across all sciences. For example, in chemistry for drug delivery and chemical monitoring, in engineering for photovoltatic and up-conversion devices, and in biology for exciton transport within photosynthetic complexes. First, we consider the disordered and weak coupling limit of resonance energy transfer often encountered in chemistry. We propose new design principles for enhancing and suppressing the energy transfer rate and efficiency quantitatively captured by a simple image dipole model. Our theory explains a wide range of experimental results which have been the subject of an ongoing debate for the past 15 years. Second, we present our recent result aimed at understanding the fundamental role of entanglement and quantum coherence in resonance energy transfer. To uncover the role of these effects, we develop a unified theory of energy transfer valid from the incoherent to quantum coherent coupling regimes. Ultimately, our theory reveals a fundamental bound ηmax = γa for energy transfer efficiency arising from γd+γa the spontaneous emission rates γd and γa of the donor and acceptor. This bound provides an upper limit to the efficiency of energy transfer regardless of quantum coherence or entanglement, suggesting new design principles for achieving near-unity energy transfer efficiency in coherent systems. The result has important implications for the two-chromophore model found in photosynthetic complexes and paves the way for nanophotonic analogues of efficiency-enhancing environments mimicking biological photosynthetic systems