50,424 research outputs found
Octree-based production of near net shape components
Near net shape (NNS) manufacturing refers to the production of products that require a finishing operation of some kind. NNS manufacturing is important because it enables a significant reduction in: machining work, raw material usage, production time, and energy consumption. This paper presents an integrated system for the production of near net shape components based on the Octree decomposition of 3-D models. The Octree representation is used to automatically decompose and approximate the 3-D models, and to generate the robot instructions required to create assemblies of blocks secured by adhesive. Not only is the system capable of producing shapes of variable precision and complexity (including overhanging or reentrant shapes) from a variety of materials, but it also requires no production tooling (e.g., molds, dies, jigs, or fixtures). This paper details how a number of well-known Octree algorithms for subdivision, neighbor findings, and tree traversal have been modified to support this novel application. This paper ends by reporting the construction of two mechanical components in the prototype cell, and discussing the overall feasibility of the system
Optimization of Massive Full-Dimensional MIMO for Positioning and Communication
Massive Full-Dimensional multiple-input multiple-output (FD-MIMO) base
stations (BSs) have the potential to bring multiplexing and coverage gains by
means of three-dimensional (3D) beamforming. Key technical challenges for their
deployment include the presence of limited-resolution front ends and the
acquisition of channel state information (CSI) at the BSs. This paper
investigates the use of FD-MIMO BSs to provide simultaneously high-rate data
communication and mobile 3D positioning in the downlink. The analysis
concentrates on the problem of beamforming design by accounting for imperfect
CSI acquisition via Time Division Duplex (TDD)-based training and for the
finite resolution of analog-to-digital converter (ADC) and digital-to-analog
converter (DAC) at the BSs. Both \textit{unstructured beamforming} and a
low-complexity \textit{Kronecker beamforming} solution are considered, where
for the latter the beamforming vectors are decomposed into separate azimuth and
elevation components. The proposed algorithmic solutions are based on Bussgang
theorem, rank-relaxation and successive convex approximation (SCA) methods.
Comprehensive numerical results demonstrate that the proposed schemes can
effectively cater to both data communication and positioning services,
providing only minor performance degradations as compared to the more
conventional cases in which either function is implemented. Moreover, the
proposed low-complexity Kronecker beamforming solutions are seen to guarantee a
limited performance loss in the presence of a large number of BS antennas.Comment: 30 pages, 6 figure
Generating optimized Fourier interpolation routines for density function theory using SPIRAL
© 2015 IEEE.Upsampling of a multi-dimensional data-set is an operation with wide application in image processing and quantum mechanical calculations using density functional theory. For small up sampling factors as seen in the quantum chemistry code ONETEP, a time-shift based implementation that shifts samples by a fraction of the original grid spacing to fill in the intermediate values using a frequency domain Fourier property can be a good choice. Readily available highly optimized multidimensional FFT implementations are leveraged at the expense of extra passes through the entire working set. In this paper we present an optimized variant of the time-shift based up sampling. Since ONETEP handles threading, we address the memory hierarchy and SIMD vectorization, and focus on problem dimensions relevant for ONETEP. We present a formalization of this operation within the SPIRAL framework and demonstrate auto-generated and auto-tuned interpolation libraries. We compare the performance of our generated code against the previous best implementations using highly optimized FFT libraries (FFTW and MKL). We demonstrate speed-ups in isolation averaging 3x and within ONETEP of up to 15%
Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation
Increasingly, business projects are ephemeral. New Business Intelligence
tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds
are a popular community-driven visualization technique. Hence, we investigate
tag-cloud views with support for OLAP operations such as roll-ups, slices,
dices, clustering, and drill-downs. As a case study, we implemented an
application where users can upload data and immediately navigate through its ad
hoc dimensions. To support social networking, views can be easily shared and
embedded in other Web sites. Algorithmically, our tag-cloud views are
approximate range top-k queries over spontaneous data cubes. We present
experimental evidence that iceberg cuboids provide adequate online
approximations. We benchmark several browser-oblivious tag-cloud layout
optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou
Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods
In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem
SAT-Based Synthesis Methods for Safety Specs
Automatic synthesis of hardware components from declarative specifications is
an ambitious endeavor in computer aided design. Existing synthesis algorithms
are often implemented with Binary Decision Diagrams (BDDs), inheriting their
scalability limitations. Instead of BDDs, we propose several new methods to
synthesize finite-state systems from safety specifications using decision
procedures for the satisfiability of quantified and unquantified Boolean
formulas (SAT-, QBF- and EPR-solvers). The presented approaches are based on
computational learning, templates, or reduction to first-order logic. We also
present an efficient parallelization, and optimizations to utilize reachability
information and incremental solving. Finally, we compare all methods in an
extensive case study. Our new methods outperform BDDs and other existing work
on some classes of benchmarks, and our parallelization achieves a super-linear
speedup. This is an extended version of [5], featuring an additional appendix.Comment: Extended version of a paper at VMCAI'1
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