Idioms for µ-charts

This paper presents an idiomatic construct for µ-charts which reflects the high-level specification construct of synchronization between activities. This, amongst others, has emerged as a common and useful idea during our use of µ-charts to design and specify commonly-occurring reactive systems. The purpose of this example, apart from any inherent interest in being able to use synchronization in a specification, is to show how the very simple language of µ-charts can used as a basis for a more expressive language built by definitional extension

Recent Progress in Plasmonic Colour Filters for Image Sensor and Multispectral Applications

Using nanostructured thin metal films as colour filters offers several important advantages, in particular high tunability across the entire visible spectrum and some of the infrared region, and also compatibility with conventional CMOS processes. Since 2003, the field of plasmonic colour filters has evolved rapidly and several different designs and materials, or combination of materials, have been proposed and studied. In this paper we present a simulation study for a single- step lithographically patterned multilayer structure able to provide competitive transmission efficiencies above 40% and contemporary FWHM of the order of 30 nm across the visible spectrum. The total thickness of the proposed filters is less than 200 nm and is constant for every wavelength, unlike e.g. resonant cavity-based filters such as Fabry-Perot that require a variable stack of several layers according to the working frequency, and their passband characteristics are entirely controlled by changing the lithographic pattern. It will also be shown that a key to obtaining narrow-band optical response lies in the dielectric environment of a nanostructure and that it is not necessary to have a symmetric structure to ensure good coupling between the SPPs at the top and bottom interfaces. Moreover, an analytical method to evaluate the periodicity, given a specific structure and a desirable working wavelength, will be proposed and its accuracy demonstrated. This method conveniently eliminate the need to optimize the design of a filter numerically, i.e. by running several time-consuming simulations with different periodicities. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

New laser optical fiber for laser surgery

Nd:YAG laser systems, coupled to silica fibers, have shown great benefits as surgical tools. Using the laser system with a bare silica fiber, laser surgeons can photocoagulate tissue to depths of 4 to 5 mm in a non-contact mode. In a contact mode, incision and cauterization of the nearby tissue can be achieved. Although these two capabilities provide powerful tools for hemostatic procedures, research performed at Iowa State University has shown that the silica fiber tips undergo extensive damage when in contact with tissue. Chemical and thermal degradation of the silica glass surface plays a key role. Damaged fibers do not transmit a significant fraction of the laser light launched down them. Instead, essentially all of the laser energy is converted to heat at the contact point. The tip can then be used only to incise tissue. We report here on the development and characterization of a new optical fiber that offers improved chemical resistance and also high temperature resistance. The new fibers were pulled from glass rods with a composition of 92.5 wt.% SiO2 and 7.5 wt.% TiO2 and then cladded with a fluorinated hard polymer. The new fibers effectively deliver energy even after the fiber comes into contact with tissue while the silica fiber tips undergo catastrophic damage. Also, preliminary clinical testing of the new fibers has demonstrated the stability of the fibers in contact with tissue during gynecological surgical procedures

\u27Texas Maroon’ Bluebonnet

The Texas state flower, the bluebonnet, encompasses all six of the Lupinus species native to Texas. The most widespread and popular bluebonnet, Lupinus texensis Hook., is a winter annual that produces violet-blue [violet-blue group 96A, Royal Horticultural Society (RHS), 1982] racemes in early to midspring and is predominately self-pollinating. The Texas Dept. of Transportation uses this species widely for floral displays along roadsides throughout much of the state (Andrews, 1986). Rare white and even rarer pink variants exist in native populations, and a breeding project was initiated in 1985 to develop bluebonnets with novel flower colors for use as bedding plants. ‘Abbott Pink’ was the first seed-propagated cultivar to be developed from this program (Parsons and Davis, 1993). The second cultivar, ‘Barbara Bush’ with novel lavender shade flowers, was developed more recently (Parsons et al., 1994). As with the cultivars previously developed, we used recurrent phenotypic selection to develop ‘Texas Maroon’. This cultivar is intended for use as a bedding plant for maroon flower color

Bird Species Categorization Using Pose Normalized Deep Convolutional Nets

We propose an architecture for fine-grained visual categorization that approaches expert human performance in the classification of bird species. Our architecture first computes an estimate of the object's pose; this is used to compute local image features which are, in turn, used for classification. The features are computed by applying deep convolutional nets to image patches that are located and normalized by the pose. We perform an empirical study of a number of pose normalization schemes, including an investigation of higher order geometric warping functions. We propose a novel graph-based clustering algorithm for learning a compact pose normalization space. We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification. We observe that a model that integrates lower-level feature layers with pose-normalized extraction routines and higher-level feature layers with unaligned image features works best. Our experiments advance state-of-the-art performance on bird species recognition, with a large improvement of correct classification rates over previous methods (75% vs. 55-65%)