Characterizing the Conductivity and Enhancing the Piezoresistivity of Carbon Nanotube-Polymeric Thin Films.

The concept of lightweight design is widely employed for designing and constructing aerospace structures that can sustain extreme loads while also being fuel-efficient. Popular lightweight materials such as aluminum alloy and fiber-reinforced polymers (FRPs) possess outstanding mechanical properties, but their structural integrity requires constant assessment to ensure structural safety. Next-generation structural health monitoring systems for aerospace structures should be lightweight and integrated with the structure itself. In this study, a multi-walled carbon nanotube (MWCNT)-based polymer paint was developed to detect distributed damage in lightweight structures. The thin film's electromechanical properties were characterized via cyclic loading tests. Moreover, the thin film's bulk conductivity was characterized by finite element modeling

Inexpensive lightweight mirror

Aluminized Mylar film is bonded to polyurethane foam mold; Mylar is then removed, leaving highly reflective coating of aluminum on foam. Mold may be used repeatedly to make mirrors for several optical instruments. Large mirrors of almost any shape may be made singularly or in quantity

Lightweight Call-Graph Construction for Multilingual Software Analysis

Analysis of multilingual codebases is a topic of increasing importance. In prior work, we have proposed the MLSA (MultiLingual Software Analysis) architecture, an approach to the lightweight analysis of multilingual codebases, and have shown how it can be used to address the challenge of constructing a single call graph from multilingual software with mutual calls. This paper addresses the challenge of constructing monolingual call graphs in a lightweight manner (consistent with the objective of MLSA) which nonetheless yields sufficient information for resolving language interoperability calls. A novel approach is proposed which leverages information from a compiler-generated AST to provide the quality of call graph necessary, while the program itself is written using an Island Grammar that parses the AST providing the lightweight aspect necessary. Performance results are presented for a C/C++ implementation of the approach, PAIGE (Parsing AST using Island Grammar Call Graph Emitter) showing that despite its lightweight nature, it outperforms Doxgen, is robust to changes in the (Clang) AST, and is not restricted to C/C++.Comment: 10 page

Metal matrix composite structural panel construction

Lightweight capped honeycomb stiffeners for use in fabricating metal or metal/matrix exterior structural panels on aerospace type vehicles and the process for fabricating same are disclosed. The stiffener stringers are formed in sheets, cut to the desired width and length and brazed in spaced relationship to a skin with the honeycomb material serving directly as the required lightweight stiffeners and not requiring separate metal encasement for the exposed honeycomb cells

LIKWID: Lightweight Performance Tools

Exploiting the performance of today's microprocessors requires intimate knowledge of the microarchitecture as well as an awareness of the ever-growing complexity in thread and cache topology. LIKWID is a set of command line utilities that addresses four key problems: Probing the thread and cache topology of a shared-memory node, enforcing thread-core affinity on a program, measuring performance counter metrics, and microbenchmarking for reliable upper performance bounds. Moreover, it includes a mpirun wrapper allowing for portable thread-core affinity in MPI and hybrid MPI/threaded applications. To demonstrate the capabilities of the tool set we show the influence of thread affinity on performance using the well-known OpenMP STREAM triad benchmark, use hardware counter tools to study the performance of a stencil code, and finally show how to detect bandwidth problems on ccNUMA-based compute nodes.Comment: 12 page

WATER WITHIN LIGHTWEIGHT AGGREGATE CONCRETE AND ITS RELATION TO AUTOGENOUS SHRINKAGE

Abstract Autogenous shrinkage of lightweight aggregate concrete (LAC) has been investigated with the aims of studying if water within LAC is effective in preventing autogenous shrinkage as suggested by Bentz’s model. By calculating ratios of water supplied by lightweight aggregate (LA) at various degrees of saturation to water required for maximum hydration and plotting these against ultimate values of autogenous shrinkage, it seems that only when the ratio is high (above 3.5) then the water within LAC supplied from LA is immediately ready to fill the empty pores and in turn, reducing autogenous shrinkage. The case is also confirmed when ratios of total void (porosity) of concrete to total volume of water within concrete are plotted against autogenous shrinkage. Keywords: autogenous shrinkage, Bentz’s model, lightweight aggregate, porosity

High impact pressure regulator Patent

High impact pressure regulator having minimum number of lightweight movable element

Lightweight Probabilistic Deep Networks

Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical CNN architectures in computer vision only compound this problem. Uncertainty in neural networks has thus been largely ignored in practice, despite the fact that it may provide important information about the reliability of predictions and the inner workings of the network. In this paper, we introduce two lightweight approaches to making supervised learning with probabilistic deep networks practical: First, we suggest probabilistic output layers for classification and regression that require only minimal changes to existing networks. Second, we employ assumed density filtering and show that activation uncertainties can be propagated in a practical fashion through the entire network, again with minor changes. Both probabilistic networks retain the predictive power of the deterministic counterpart, but yield uncertainties that correlate well with the empirical error induced by their predictions. Moreover, the robustness to adversarial examples is significantly increased.Comment: To appear at CVPR 201