Calculation of imaging errors of AWG

This paper reports on the modeling of arrayed-waveguide gratings (AWGs) by means of Fourier optics where the coupling between the grating waveguides and defocus of the star couplers is taken into account as well. Special interest is drawn to the geometrical modeling of AWGs. The paraxial approximation for the free-space propagation within the star couplers, the exact calculation of the coupling coefficients for the star couplers by an orthonormal basis (ONB) expansion, and the drawbacks due to the nonunitary transfer matrix provided by the Green's functions approach for the coupling between the grating waveguides, as well as possible improvements thereof, are identified as crucial points. An efficient orthonormalization procedure by means of an eigenvector decomposition is presented. A unitary approximation of the transfer matrix, which is in itself approximate but in general not unitary, introduced within the impulse response method, is obtained. The presented simulation technique allows for ab initio simulations, which do not fall back on any experimental input, for the spectral response of AWGs, which are found in good agreement with experimental results.     

Glimmer: Multilevel MDS on the GPU

We present Glimmer, a new multilevel algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hardware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm makes local minima less likely while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We also show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm.     

Cerebral: visualizing multiple experimental conditions on a graph with biological context

Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists obser ve the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs ser ve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incor porates experimental data directly into the graph display. Small multiple views of different experimental conditions and a data-driven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaboratorsweconcludethat Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model.      

Smashing peacocks further: drawing quasi-trees from biconnected components

We extend the popular force-directed approach to network (or graph) layout to allow separation constraints, which enforce a minimum horizontal or vertical separation between selected pairs of nodes. This simple class of linear constraints is expressive enough to satisfy a wide variety of application-specific layout requirements, including: layout of directed graphs to better show flow; layout with non-overlapping node labels; and layout of graphs with grouped nodes (called clusters). In the stress majorization force-directed layout process, separation constraints can be treated as a quadratic programming problem. We give an incremental algorithm based on gradient projection for efficiently solving this problem. The algorithm is considerably faster than using generic constraint optimization techniques and is comparable in speed to unconstrained stress majorization. We demonstrate the utility of our technique with sample data from a number of practical applications including gene-activation networks, terrorist networks and visualization of high-dimensional data     

Composite rectilinear deformation for stretch and squish navigation

We present the first scalable algorithm that supports the composition of successive rectilinear deformations. Earlier systems that provided stretch and squish navigation could only handle small datasets. More recent work featuring rubber sheet navigation for large datasets has focused on rendering and on application-specific issues. However, no algorithm has yet been presented for carrying out such navigation methods; our paper addresses this problem. For maximum flexibility with large datasets, a stretch and squish navigation algorithm should allow for millions of potentially deformable regions. However, typical usage only changes the extents of a small subset k of these n regions at a time. The challenge is to avoid computations that are linear in n, because a single deformation can affect the absolute screen-space location of every deformable region. We provide an O(klogn) algorithm that supports any application that can lay out a dataset on a generic grid, and show an implementation that allows navigation of trees and gene sequences with millions of items in sub-millisecond time.     

Ocupado: Visualizing Location-Based Counts Over Time Across Buildings

Understanding how spaces in buildings are being used is vital for optimizing space utilization, for improving resource allocation, and for the design of new facilities. We present a multi-year design study that resulted in Ocupado, a set of visual decision-support tools centered around occupancy data for stakeholders in facilities management and planning. Ocupado uses WiFi devices as a proxy for human presence, capturing location-based counts that preserve privacy without trajectories. We contribute data and task abstractions for studying space utilization for combinations of data granularities in both space and time. In addition, we contribute generalizable design choices for visualizing location-based counts relating to indoor environments. We provide evidence of Ocupado's utility through multiple analysis scenarios with real-world data refined through extensive stakeholder feedback, and discussion of its take-up by our industry partner.