1,945 research outputs found

    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    Insights into temperature controls on rockfall occurrence and cliff erosion

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    A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 m². A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls. The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Study of Surface Morphology and Microstructure of Electrodeposited Polycrystalline Cu Films

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    The applications of polycrystalline films range from interconnects in the electronics and semiconductor industry to solar cells and as corrosion protection. Despite their significance, factors that determine their microstructure and morphology remain largely unsolved. The surface and microstructure of electrodeposited polycrystalline Cu films were investigated. This involves looking at the later growth stages of Cu films using different surface and bulk characterization techniques. The surface evolution of an electrodeposited Cu film was imaged in real-time using a Highspeed Atomic Force Microscope (HS-AFM). This provides details about how the film structure coarsens with time. The high-resolution video showed accelerated local grain growth and grain overgrowth at different locations of the film. A combination of both of these mechanisms could drive structural coarsening. The microstructure could play a role in inducing faster growth in certain grains. How the local and large-scale roughness varies with film thickness is studied by scaling analysis. As a complement to scaling analysis, variation in the local slope with thickness is calculated using slope analysis. Rapid growth was observed in the regions where the HS-AFM tip was scanning. The removal of oxygen adlayer from the surface by the tip could promote faster growth in these regions. Pulsed electrodeposition produced Cu films with hexagonal structures. They are known to be twinned which is a desirable feature in applications that require superior mechanical and electrical properties. The effect of electrode potential on grain size was studied. Using a watershed segmentation algorithm, the grain area was calculated from the AFM images. The grain area showed an increasing trend with increasing overpotential. Slope analysis on the ’hexagons’ and the complete films electrodeposited at higher potential revealed higher slopes and distinct slope distribution. Cross-sectional Focused Ion Beam (FIB) milling confirmed that horizontal twins are present in the pulse-deposited Cu films. The hexagonal pyramids with twins could be produced by one of the two mechanisms, stress relaxation during the ’OFF’ period of pulsing or driven by screw dislocation. We attribute the origin of the hexagons to spiralling screw dislocations. A template matching algorithm was developed to try and correlate the surface and microstructural data of a Cu film grown on a microelectrode. It involved matching the AFM and Electron Back Scatter Diffraction (EBSD) data on the later FIB milled sample, thus relating surface topography to crystallographic orientation. The crystallographic orientation of the edge of the microelectrode and its centre showed different orientations, switching from (111) to (110). Twinning was investigated at the edge and the centre of the microelectrode revealing the presence of stacking fault twins in both of these regions

    Examining the Relationship Between Lignocellulosic Biomass Structural Constituents and Its Flow Behavior

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    Lignocellulosic biomass material sourced from plants and herbaceous sources is a promising substrate of inexpensive, abundant, and potentially carbon-neutral energy. One of the leading limitations of using lignocellulosic biomass as a feedstock for bioenergy products is the flow issues encountered during biomass conveyance in biorefineries. In the biorefining process, the biomass feedstock undergoes flow through a variety of conveyance systems. The inherent variability of the feedstock materials, as evidenced by their complex microstructural composition and non-uniform morphology, coupled with the varying flow conditions in the conveyance systems, gives rise to flow issues such as bridging, ratholing, and clogging. These issues slow down the conveyance process, affect machine life, and potentially lead to partial or even complete shutdown of the biorefinery. Hence, we need to improve our fundamental understanding of biomass feedstock flow physics and mechanics to address the flow issues and improve biorefinery economics. This dissertation research examines the fundamental relationship between structural constituents of diverse lignocellulosic biomass materials, i.e., cellulose, hemicellulose, and lignin, their morphology, and the impact of the structural composition and morphology on their flow behavior. First, we prepared and characterized biomass feedstocks of different chemical compositions and morphologies. Then, we conducted our fundamental investigation experimentally, through physical flow characterization tests, and computationally through high-fidelity discrete element modeling. Finally, we statistically analyzed the relative influence of the properties of lignocellulosic biomass assemblies on flow behavior to determine the most critical properties and the optimum values of flow parameters. Our research provides an experimental and computational framework to generalize findings to a wider portfolio of biomass materials. It will help the bioenergy community to design more efficient biorefining machinery and equipment, reduce the risk of failure, and improve the overall commercial viability of the bioenergy industry

    High-throughput Single-Entity Analysis Methods: From Single-Cell Segmentation to Single-Molecule Force Measurements

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    This work is focused on the development of new microscopy-based analysis methods with single-entity resolution and high-throughput capabilities from the cellular to the molecular level to study biomembrane-associated interactions. Currently, there is a variety of methods available for obtaining quantitative information on cellular and molecular responses to external stimuli, but many of them lack either high sensitivity or high throughput. Yet, the combination of both aspects is critical for studying the weak but often complex and multivalent interactions at the interface of biological mem-branes. These interactions include binding of pathogens such as some viruses (e.g., influenza A virus, herpes simplex virus, and SARS-CoV-2), transmembrane signaling such as ligand-based oli-gomerization processes, and transduction of mechanical forces acting on cells. The goal of this work was to overcome the shortcomings of current methods by developing and es-tablishing new methods with unprecedented levels of automation, sensitivity, and parallelization. All methods are based on the combination of optical (video) microscopy followed by highly refined data analysis to study single cellular and molecular events, allowing the detection of rare events and the identification and quantification of cellular and molecular populations that would remain hidden in ensemble-averaging approaches. This work comprises four different projects. At the cellular level, two methods have been developed for single-cell segmentation and cell-by-cell readout of fluorescence reporter systems, mainly to study binding and inhibition of binding of viruses to host cells. The method developed in the first pro-ject features a high degree of automation and automatic estimation of sufficient analysis parameters (background threshold, segmentation sensitivity, and fluorescence cutoff) to reduce the manual ef-fort required for the analysis of cell-based infection assays. This method has been used for inhibition potency screening based on the IC50 value of various virus binding inhibitors. With the method used in the second project, the sensitivity of the first method is extended by providing an estimate of the number of fluorescent nanoparticles bound to the cells. The image resolution was chosen to allow many cells to be imaged in parallel. This allowed for the quantification of cell-to-cell heterogeneity of particle binding, at the expense of resolution of the individual fluorescent nanoparticles. To account for this, a new approach was developed and validated by simulations to estimate the number of fluo-rescent nanoparticles below the diffraction limit with an accuracy of about 80 to 100 %. In the third project, an approach for the analysis and refinement of two-dimensional single-particle tracking ex-periments was presented. It focused on the quality assessment of the derived tracks by providing a guide for the selection of an appropriate maximal linking distance. This tracking approach was used in the fourth project to quantify small molecule responses to hydrodynamic shear forces with sub-nm resolution. Here, the combination of TIRF microscopy, microfluidics, and single particle tracking enabled the development of a new single molecule force spectroscopy method with high resolution and parallelization capabilities. This method was validated by quantifying the mechanical response of well-defined PEG linkers and subsequently used to study the energy barriers of dissociation of mul-tivalent biotin-NeutrAvidin complexes under low (~ 1.5 to 12 pN) static forces. In summary, with this work, the repertoire of appropriate methods for high-throughput investigation of the properties and interactions of cells, nanoparticles, and molecules at single resolution is expand-ed. In the future, the methods developed here will be used to screen for additional virus binding inhib-itors, to study the oligomerization of membrane receptors on cells and model membranes, and to quantify the mechanical response of force-bearing proteins and ligand-receptor complexes under low force conditions

    Image processing for correlative light and electron microscopy

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    People have never stopped exploring the microscopic world. Studying the microstructure of cells helps people better understand the people themselves and has the potential to overcome specific diseases at a fundamental level. Correlative light and electron microscopy (CLEM) can let people intuitively understand the sample information through imaging. In CLEM measurements, samples are measured in both fluorescence microscopy and electron microscopy. Due to technical differences between LM and EM, images obtained from LM and EM contain different information. With the fluorescent labels, one can easily observe the structures of interest. However, due to the diffraction limit, LM image resolution is limited to a few hundred nanometers. EM images can capture the detailed structure of a sample down to the atomic level. However, grayscale images obtained from EM often contain very complex structures. Identifying structures of interest from these complex structures using only EM images is usually a challenge. The CLEM technology provides an opportunity to specify the structures of interest by comparing the CLEM images. However, due to the resolution difference between LM and EM, these structures are usually still not directly distinguishable by simply overlaying the fluorescence microscopy images on high-resolution grayscale electron microscopy images. This thesis aims to investigate a new deconvolution algorithm, EM-guided deconvolution, to automate fusing the LM information on the correlative EM image. We discuss the algorithm with simulated CLEM images and further apply it to experimental data sets. The algorithm can enhance the image resolution to nanometers from correlative wide-field (or confocal) fluorescence microscopy images. The algorithm can effectively recognise, e.g., membrane structures or identify the structures with a suitable point spread function and precise image registration

    Conditional Invertible Generative Models for Supervised Problems

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    Invertible neural networks (INNs), in the setting of normalizing flows, are a type of unconditional generative likelihood model. Despite various attractive properties compared to other common generative model types, they are rarely useful for supervised tasks or real applications due to their unguided outputs. In this work, we therefore present three new methods that extend the standard INN setting, falling under a broader category we term generative invertible models. These new methods allow leveraging the theoretical and practical benefits of INNs to solve supervised problems in new ways, including real-world applications from different branches of science. The key finding is that our approaches enhance many aspects of trustworthiness in comparison to conventional feed-forward networks, such as uncertainty estimation and quantification, explainability, and proper handling of outlier data

    Autonomisten metsäkoneiden koneaistijärjestelmät

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    A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator. In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors. In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.Metsäkoneiden autonomia-asteen kasvattaminen edellyttää, että robotilla on digitaalinen tilannetieto sekä ympäristöstä että robotin omasta toiminnasta. Tämän saavuttamiseksi työssä on kehitetty autonomisen tai puoliautonomisen metsäkoneen koneaistijärjestelmiä, jotka hyödyntävät konenäkö-, laserkeilaus- ja inertia-antureita sekä paikannusantureita. Työ liittää yhteen seitsemässä artikkelissa toteutetut havainnointimenetelmät, joissa useiden anturien mittauksia yhdistetään sensorifuusiomenetelmillä. Työssä puoliautonomialla tarkoitetaan hyödyllisiä kuljettajaa avustavia välivaiheita nykyisten mekanisoitujen ratkaisujen ja täyden autonomian välillä. Työssä esitettävissä autonomisen metsäkoneen koneaistijärjestelmissä koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekä siihen liitetyn työkalun asentoa suhteessa ympäristöön. Yleisnäkymä metsäkoneen ympärille toteutetaan pyörivällä laserkeilaimella, joka tuottaa lähes vakiotiheyksisiä 3D-mittauksia jokasuuntaisesti koneen ympäristöstä. Nuoret puut tunnistetaan muun kasvillisuuden joukosta käyttäen konenäkökameraa. Lisäksi puiden tunnistamisessa ja puulajien luokittelussa käytetään konenäkökameraa ja laserkeilainta yhdessä sensorifuusioratkaisun avulla. Lisäksi kuljettajan ohjaamassa puoliautonomisessa järjestelmässä kuljettaja tarvitsee toimivan tavan ymmärtää koneen tuottaman mallin ympäristöstä. Työssä tämä ehdotetaan toteutettavaksi lisätyn todellisuuden käyttöliittymän avulla, joka edellyttää metsäkoneen ohjaamossa istuvan kuljettajan lisätyn todellisuuden lasien paikan ja asennon mittaamista. Työssä se toteutetaan kypärään asennetun kameran ja inertia-anturien sensorifuusiona. Jotta metsäkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisätä, työssä keskitytään uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen käyttöön metsäkoneissa. Kaikki esitetyt koneaistijärjestelmät pyrkivät vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsäkoneiden käytössä. Siksi kaikki menetelmät on implementoitu prototyyppimetsäkoneisiin ja tulokset on testattu metsäympäristössä. Työssä esitetyt menetelmät mahdollistavat metsäkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisätyn todellisuuden lasien asennon mittaamisen metsäkoneen ohjaamossa, nuorten puiden havaitsemisen reikäperkauksessa, ympäröivien puiden puulajien tunnistuksen, sekä puun runkojen ja maanpinnan mittauksen

    Metaverse. Old urban issues in new virtual cities

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    Recent years have seen the arise of some early attempts to build virtual cities, utopias or affective dystopias in an embodied Internet, which in some respects appear to be the ultimate expression of the neoliberal city paradigma (even if virtual). Although there is an extensive disciplinary literature on the relationship between planning and virtual or augmented reality linked mainly to the gaming industry, this often avoids design and value issues. The observation of some of these early experiences - Decentraland, Minecraft, Liberland Metaverse, to name a few - poses important questions and problems that are gradually becoming inescapable for designers and urban planners, and allows us to make some partial considerations on the risks and potentialities of these early virtual cities
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