Design and standalone characterisation of a capacitively coupled HV-CMOS sensor chip for the CLIC vertex detector

The concept of capacitive coupling between sensors and readout chips is under study for the vertex detector at the proposed high-energy CLIC electron positron collider. The CLICpix Capacitively Coupled Pixel Detector (C3PD) is an active High-Voltage CMOS sensor, designed to be capacitively coupled to the CLICpix2 readout chip. The chip is implemented in a commercial $180$ nm HV-CMOS process and contains a matrix of $128\times128$ square pixels with $25$ $\mu$m pitch. First prototypes have been produced with a standard resistivity of $\sim20$ $\Omega$cm for the substrate and tested in standalone mode. The results show a rise time of $\sim20$ ns, charge gain of $190$ mV/ke$^{-}$ and $\sim40$ e$^{-}$ RMS noise for a power consumption of $4.8$ $\mu$W/pixel. The main design aspects, as well as standalone measurement results, are presented.Comment: 13 pages, 13 figures, 2 tables. Work carried out in the framework of the CLICdp collaboratio

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Medium-term temporal stability of the helminth component community structure in bank voles (Clethrionomys glareolus) from the Mazury Lake District region of Poland

The structure of helminth communities in wild rodents is subject to seasonal variation, and is dependent on host age within years. Although between-year variation has been monitored, seldom has it been assessed rigorously by appropriate multifactorial analysis with potentially confounding factors taken into account. In this study we tested the null hypothesis that despite seasonal, host age and sex effects, helminth communities should show relative stability between years. Over a period of 3 years (1998–2000) we sampled bank vole (Clethrionomys glareolus) populations (total n=250) at 2 points in the year: in spring, at the start of the breeding season, and in autumn, after the cessation of breeding. In spite of seasonal differences and strong age effects, the between-year effects were surprisingly small. Measures of component community structure (Berger- Parker dominance index, the dominant species, S. petrusewiczi) did not vary, or varied only slightly from year to year. The majority of measures of infracommunity structure [Brillouin’s index of diversity, prevalence of all helminths combined, prevalence and abundance of H. mixtum (the most prevalent helminth), mean species richness] did not differ significantly between years when other factors such as age, sex and seasonal variation had been taken into account. Some between-year variations were found (at the component community level, Simpson’s index of diversity ; at the infracommunity level, prevalence and abundance of S. petrusewiczi and abundance of all helminths combined), but even these were modest in comparison to seasonal and age differences, and were primarily attributable to S. petrusewiczi. We conclude that despite dynamic within-year fluctuations, helminth communities in bank voles in this region of Poland show relative stability across years. The sporadic occurrence of individual platyhelminths at low prevalence, makes little difference to the overall structure, which is largely maintained by the key roles played by the dominant intestinal nematodes of bank voles and the rarer species collectively

In-Space Propulsion, Logistics Reduction, and Evaluation of Steam Reformer Kinetics: Problems and Prospects

Human space missions generate waste materials. A 70-kg crewmember creates a waste stream of 1 kg per day, and a four-person crew on a deep space habitat for a 400+ day mission would create over 1600 kg of waste. Converted into methane, the carbon could be used as a fuel for propulsion or power. The NASA Advanced Exploration Systems (AES) Logistics Reduction and Repurposing (LRR) project is investing in space resource utilization with an emphasis on repurposing logistics materials for useful purposes and has selected steam reforming among many different competitive processes as the preferred method for repurposing organic waste into methane. Already demonstrated at the relevant processing rate of 5.4 kg of waste per day, high temperature oxygenated steam consumes waste and produces carbon dioxide, carbon monoxide, and hydrogen which can then be converted into methane catalytically. However, the steam reforming process has not been studied in microgravity. Data are critically needed to understand the mechanisms that allow use of steam reforming in a reduced gravity environment. This paper reviews the relevant literature, identifies gravity-dependent mechanisms within the steam gasification process, and describes an innovative experiment to acquire the crucial kinetic information in a small-scale reactor specifically designed to operate within the requirements of a reduced gravity aircraft flight. The experiment will determine if the steam reformer process is mass-transport limited, and if so, what level of forced convection will be needed to obtain performance comparable to that in 1-g

Nanoscale Metal Oxide Semiconductors for Gas Sensing

A report describes the fabrication and testing of nanoscale metal oxide semiconductors (MOSs) for gas and chemical sensing. This document examines the relationship between processing approaches and resulting sensor behavior. This is a core question related to a range of applications of nanotechnology and a number of different synthesis methods are discussed: thermal evaporation- condensation (TEC), controlled oxidation, and electrospinning. Advantages and limitations of each technique are listed, providing a processing overview to developers of nanotechnology- based systems. The results of a significant amount of testing and comparison are also described. A comparison is made between SnO2, ZnO, and TiO2 single-crystal nanowires and SnO2 polycrystalline nanofibers for gas sensing. The TECsynthesized single-crystal nanowires offer uniform crystal surfaces, resistance to sintering, and their synthesis may be done apart from the substrate. The TECproduced nanowire response is very low, even at the operating temperature of 200 C. In contrast, the electrospun polycrystalline nanofiber response is high, suggesting that junction potentials are superior to a continuous surface depletion layer as a transduction mechanism for chemisorption. Using a catalyst deposited upon the surface in the form of nanoparticles yields dramatic gains in sensitivity for both nanostructured, one-dimensional forms. For the nanowire materials, the response magnitude and response rate uniformly increase with increasing operating temperature. Such changes are interpreted in terms of accelerated surface diffusional processes, yielding greater access to chemisorbed oxygen species and faster dissociative chemisorption, respectively. Regardless of operating temperature, sensitivity of the nanofibers is a factor of 10 to 100 greater than that of nanowires with the same catalyst for the same test condition. In summary, nanostructure appears critical to governing the reactivity, as measured by electrical resistance of these SnO2 nanomaterials towards reducing gases. With regard to the sensitivity of the different nascent nanostructures, the electrospun nanofibers appear preferabl

Chemical Sensors Based on Metal Oxide Nanostructures

This paper is an overview of sensor development based on metal oxide nanostructures. While nanostructures such as nanorods show significan t potential as enabling materials for chemical sensors, a number of s ignificant technical challenges remain. The major issues addressed in this work revolve around the ability to make workable sensors. This paper discusses efforts to address three technical barriers related t o the application of nanostructures into sensor systems: 1) Improving contact of the nanostructured materials with electrodes in a microse nsor structure; 2) Controling nanostructure crystallinity to allow co ntrol of the detection mechanism; and 3) Widening the range of gases that can be detected by using different nanostructured materials. It is concluded that while this work demonstrates useful tools for furt her development, these are just the beginning steps towards realizati on of repeatable, controlled sensor systems using oxide based nanostr uctures

The Development of Metal Oxide Chemical Sensing Nanostructures

This paper discusses sensor development based on metal oxide nanostructures and microsystems technology. While nanostructures such as nanowires show significant potential as enabling materials for chemical sensors, a number of significant technical challenges remain. This paper discusses development to address each of these technical barriers: 1) Improved contact and integration of the nanostructured materials with microsystems in a sensor structure; 2) Control of nanostructure crystallinity to allow control of the detection mechanism; and 3) Widening the range of gases that can be detected by fabricating multiple nanostructured materials. A sensor structure composed of three nanostructured oxides aligned on a single microsensor has been fabricated and tested. Results of this testing are discussed and future development approaches are suggested. It is concluded that while this work lays the foundation for further development, these are the beginning steps towards realization of repeatable, controlled sensor systems using oxide based nanostructures

Processing of Nanostructured Devices Using Microfabrication Techniques

Systems and methods that incorporate nanostructures into microdevices are discussed herein. These systems and methods can allow for standard microfabrication techniques to be extended to the field of nanotechnology. Sensors incorporating nanostructures can be fabricated as described herein, and can be used to reliably detect a range of gases with high response

Extraction and Characterization of Lipids from Salicornia virginica and Salicornia europaea

The lipid content from Salicornia virginica and Salicornia europaea is investigated. The plants are leafless halophytes with seeds contained in terminal nodes. The lipids, in the form of cell membranes and oil bodies that come directly from the node cells, are observed using fluorescence microscopy. Two extraction methods as well as the results of extracting from the seeds and from the entire nodes are described. Characterization of the fatty acid components of the lipids using Gas Chromatography in tandem with Mass Spectroscopy is also described. Comparisons are made between the two methods and between the two plant materials as lipid sources

Robust Trajectory Planning for Autonomous Parafoils under Wind Uncertainty

A key challenge facing modern airborne delivery systems, such as parafoils, is the ability to accurately and consistently deliver supplies into di cult, complex terrain. Robustness is a primary concern, given that environmental wind disturbances are often highly uncertain and time-varying, coupled with under-actuated dynamics and potentially narrow drop zones. This paper presents a new on-line trajectory planning algorithm that enables a large, autonomous parafoil to robustly execute collision avoidance and precision landing on mapped terrain, even with signi cant wind uncertainties. This algorithm is designed to handle arbitrary initial altitudes, approach geometries, and terrain surfaces, and is robust to wind disturbances which may be highly dynamic throughout the terminal approach. Explicit, real-time wind modeling and classi cation is used to anticipate future disturbances, while a novel uncertainty-sampling technique ensures that robustness to possible future variation is e ciently maintained. The designed cost-to-go function enables selection of partial paths which intelligently trade o between current and reachable future states. Simulation results demonstrate that the proposed algorithm reduces the worst-case impact of wind disturbances relative to state-of-the-art approaches.Charles Stark Draper Laborator