4,794 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    LASSO – an observatorium for the dynamic selection, analysis and comparison of software

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    Mining software repositories at the scale of 'big code' (i.e., big data) is a challenging activity. As well as finding a suitable software corpus and making it programmatically accessible through an index or database, researchers and practitioners have to establish an efficient analysis infrastructure and precisely define the metrics and data extraction approaches to be applied. Moreover, for analysis results to be generalisable, these tasks have to be applied at a large enough scale to have statistical significance, and if they are to be repeatable, the artefacts need to be carefully maintained and curated over time. Today, however, a lot of this work is still performed by human beings on a case-by-case basis, with the level of effort involved often having a significant negative impact on the generalisability and repeatability of studies, and thus on their overall scientific value. The general purpose, 'code mining' repositories and infrastructures that have emerged in recent years represent a significant step forward because they automate many software mining tasks at an ultra-large scale and allow researchers and practitioners to focus on defining the questions they would like to explore at an abstract level. However, they are currently limited to static analysis and data extraction techniques, and thus cannot support (i.e., help automate) any studies which involve the execution of software systems. This includes experimental validations of techniques and tools that hypothesise about the behaviour (i.e., semantics) of software, or data analysis and extraction techniques that aim to measure dynamic properties of software. In this thesis a platform called LASSO (Large-Scale Software Observatorium) is introduced that overcomes this limitation by automating the collection of dynamic (i.e., execution-based) information about software alongside static information. It features a single, ultra-large scale corpus of executable software systems created by amalgamating existing Open Source software repositories and a dedicated DSL for defining abstract selection and analysis pipelines. Its key innovations are integrated capabilities for searching for selecting software systems based on their exhibited behaviour and an 'arena' that allows their responses to software tests to be compared in a purely data-driven way. We call the platform a 'software observatorium' since it is a place where the behaviour of large numbers of software systems can be observed, analysed and compared

    Challenge and Research Trends of Solar Concentrators

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    Primary and secondary solar concentrators are of vital importance for advanced solar energy and solar laser researches. Some of the most recent developments in primary and secondary solar concentrators were firstly presented. A novel three-dimensional elliptical-shaped Fresnel lens analytical model was put forward to maximize the solar concentration ratio of Fresnel-lens-based solar concentrators. By combining a Fresnel lens with a modified parabolic mirror, significant improvement in solar laser efficiency was numerically calculated. A fixed fiber light guide system using concave outlet concentrators was proposed. The absence of a solar tracking structure highlights this research. By shaping a luminescent solar concentrators in the form of an elliptic array, its emission losses was drastically reduced. Simple conical secondary concentrator was effective for thermal applications. New progresses in solar-pumped lasers by NOVA University of Lisbon were presented. By adopting a rectangular fused silica light guide, 40 W maximum solar laser power was emitted from a single Ce:Nd:YAG rod. An aspheric fused silica secondary concentrator and a small diameter Ce:Nd:YAG rod were essential for attaining 4.5 % record solar-to-laser power conversion efficiency. A novel solar concentrator design for the efficient production of doughnut-shaped and top-hat solar laser beams were also reported. More importantly, a novel solar concentrator approach for the emission of 5 kW-class TEM00 mode solar laser beams from one megawatt solar furnace was put forward at the end of this book, revealing promising future for solar-pumped lasers

    Intelligent computing : the latest advances, challenges and future

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    Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

    Computational Geometry Contributions Applied to Additive Manufacturing

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    This Doctoral Thesis develops novel articulations of Computation Geometry for applications on Additive Manufacturing, as follows: (1) Shape Optimization in Lattice Structures. Implementation and sensitivity analysis of the SIMP (Solid Isotropic Material with Penalization) topology optimization strategy. Implementation of a method to transform density maps, resulting from topology optimization, into surface lattice structures. Procedure to integrate material homogenization and Design of Experiments (DOE) to estimate the stress/strain response of large surface lattice domains. (2) Simulation of Laser Metal Deposition. Finite Element Method implementation of a 2D nonlinear thermal model of the Laser Metal Deposition (LMD) process considering temperaturedependent material properties, phase change and radiation. Finite Element Method implementation of a 2D linear transient thermal model for a metal substrate that is heated by the action of a laser. (3) Process Planning for Laser Metal Deposition. Implementation of a 2.5D path planning method for Laser Metal Deposition. Conceptualization of a workflow for the synthesis of the Reeb Graph for a solid region in â„ť" denoted by its Boundary Representation (B-Rep). Implementation of a voxel-based geometric simulator for LMD process. Conceptualization, implementation, and validation of a tool for the minimization of the material over-deposition at corners in LMD. Implementation of a 3D (non-planar) slicing and path planning method for the LMD-manufacturing of overhanging features in revolute workpieces. The aforementioned contributions have been screened by the international scientific community via Journal and Conference submissions and publications

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Improving the Manufacture by Flexographic Printing of RFID Aerials for Intelligent Packaging

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    Flexography is a well-established high-volume roll-to-roll industrial printing process that has shown promise for the manufacture of printed electronics for smart and intelligent packaging, particularly on to flexible substrates. Understanding is required of the relationship between print process parameters, including ink rheology, and performance of printed electronic circuits, sensors and in particular RFID antenna. The complexity of this printing process with its shear and extensional flows of complex inks and flexible substrates can lead to undesirable surface morphology to the detriment of electronic performance of the print. This thesis reports work that progresses the understanding of the complex relationships amongst relevant factors, particularly focusing on the printability of features that have an impact on printed RFID antenna where increases in resistance increase the antennas resonant frequency. Flexography was successfully used to print RFID antenna. However, the large variation in print outcomes when using commercial inks and the limits on resistivity reduction even at the optimal print parameters necessitated the systematic development of an alternative silver flake ink. Increases in silver loading and TPU polymer viscosity grade (molecular weight) increased the viscosity. The ink maintained its geometry from the anilox cell between rollers, on to the substrate and print surface roughness increased. This, however, did not increase resistance of the track due to the high silver loading. Better understanding of the relationship between print parameters, print outcomes, ink rheology and performance of an RFID antenna has been achieved. Increases in silver loading up to 60wt.% improved conductivity. However, further increasing the silver loading produced negligible additional benefit. An adaption of Krieger-Dougherty suspension model equation has been proposed for silver at concentrations over 60wt.% after assessing existing suspension models. Such a model has proven to better predict relative viscosities of inks than Einstein-Batchelor, Krieger-Dougherty and Maron-Pierce equations. Increasing TPU viscosity grade was found to be a promising ink adjustment in the absence of changing print parameters, to produce a more consistent print. Better prediction of ink behaviour will allow for improved control of ink deposition, which for RFID applications can improve ink conductivity, essential for good response to signal. Further developments such as addition of non-flake particles and formulation refinement are required to enable the model ink to match the resistivity of the commercial ink

    AI/ML Algorithms and Applications in VLSI Design and Technology

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    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

    Microfluidics for Investigation of Electric-Induced Behaviors of Zebrafish Larvae

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    Zebrafish has emerged as a model organism for studying the genetic, neuronal and behavioral bases of diseases and for drug screening. Being a vertebrate, they are phylogenetically closer to humans than invertebrates, possess complex organs and the overall organization of their brain shows structural similarities with human. They are small at larval stages, optically transparent and easy to culture. In addition, zebrafish models of human diseases and genetic mutants are widely available. These characteristics make this vertebrate model an ideal organism for neurodegeneration study and drug screening from the molecule to whole organism level. Despite these attractive features, the conventional zebrafish screening methods used for movement-based behavioral tests are mostly time-consuming, uncontrollable, qualitative, low-throughput and inaccurate. Zebrafish larvae behavioral response to various stimulations including optical and chemical stimuli, have been already investigated. However, zebrafish sensory-motor responses to electrical signals, a controllable stimulus which its potential in inducing locomotion response was proven in research done before, have not been broadly studied. Examples of research questions remaining to be answered are if zebrafish electric induced response is sensitive to different electric current intensities, voltage drops, multiple electrical stimulation, and the electric field direction. The involvement of different pathways and genes in this response and its potential for utilization in disease studies and chemical screening, and drug discovery can also be investigated. This research aims to enhance our understanding of zebrafish electric-induced response via presenting novel microfluidic devices that address the challenges associated with monitoring the behavioral activities of zebrafish larvae in response to various electrical signals. In Objective 1 of the thesis, we designed a microfluidic device to deliver electrical stimuli to the awake and partially immobilized zebrafish larvae, screen and study their phenotypic behavioral responses and analyze the outputs. Behavioral response was characterized in terms of response duration and tail beat frequency. A multi-phenotypic microfluidic device was also developed to study the effect of electric stimulation on the heartrate. In Objective 2, attention was given to investigate the effect of electric current, voltage, and field direction on the zebrafish larvae’s response to find an optimized setting which can induce a traceable response in zebrafish. Using different habituation-dishabituation strategies, we also investigated if the zebrafish larvae show adaptation towards repeated exposures to electric stimuli. In Objective 3, we developed a quadruple-fish device to enhance the behavioral throughput of our microfluidic platform and showed the technique's effectiveness for larger sample size and faster behavioral assay. In Objective 4, our quadruple-fish device was employed to investigate the involvement of dopaminergic neurons in electric-induced movement response of zebrafish larvae. Lastly, since we could monitor the electric-induced behavioral responses of zebrafish larvae, in Objective 5, the applicability of our proposed technique in chemical toxicity and gene screening assays was investigated. This study is expected to introduce a microfluidic platform for on-demand and phenotypic behavioral screening of zebrafish larvae with applications in chemical screening and drug discovery
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