11,245 research outputs found

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    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

    Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

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    The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks

    Technology for Low Resolution Space Based RSO Detection and Characterisation

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    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    New Advances in Oil, Gas and Geothermal Reservoirs

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    The demand for global fossil energy continues to be strong, meaning that the exploitation of oil and gas resources is still very important. In addition, due to the continuous reduction in conventional oil and gas recoverable resources, the development of unconventional oil and gas resources and geothermal energy has gradually become an important replacement. Therefore, it is urgent to improve the existing mechanism analysis, research methods and engineering technology to improve the production and development efficiency of oil, gas, and geothermal resources. This reprint presents 11 recent works on the application of new theories and technologies in oil, gas, and geothermal reservoirs. The content covers well-drilling, cementing, hydraulic fracturing, improved oil recovery, conformance control, and geothermal energy development. The new progress presented in this reprint will help scientists and researchers to better understand and master the latest theories and techniques for oil, gas, and geothermal reservoirs, which has important practical significance for the economical and effective development of oil, gas, and geothermal resources

    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

    Near Field Electron Ptychography

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    Phase imaging in the Transmission Electron Microscope (TEM) has a long history, from the implementation of off-axis holography in TEM to Differential Phase Contrast (DPC) on the Scanning Transmission Electron Microscopy (STEM). The advent of modern computing has enabled the development of iterative algorithms which attempt to recover a phase image of a specimen from measurements of the way it diffracts an incident electron beam. One of the most successful of these iterative methods is focused probe ptychography, which relies on far field diffraction pattern measurements recorded as the incident beam is scanned through a grid of locations across the specimen. Focused probe ptychography implemented in the STEM has provided the highest resolution images available to date, allows for lens-less setups avoiding the aberrations typical in older STEMs and allows for simultaneous reconstruction of the illumination and specimen. Ptychography is computationally flexible (highly constrained), allowing for additional unknowns other than the phase of the specimen to be recovered, for example positions can be refined during reconstruction. Near field ptychography is a recent variation on ptychography that replaces the far-field diffraction data with diffraction patterns recorded in the near field, or Fresnel, region. It promises to obtain a much larger field of view with fewer diffraction patterns than focused probe ptychography. The main contribution of this thesis is the implementation of a new form of near field ptychography on the Transmission Electron Microscope (TEM), using an etched silicon nitride window to structure the electron beam. Proof-of-concept results show the method quantitatively recovers megapixel phase images from as few as 9 recorded diffraction patterns, compared to many hundreds of diffraction patterns required for focused probe ptychography. Additional sets of results show how near-field ptychography can recover extremely large fields of view, deal effectively with inelastic scattering, and accommodate several sources of uncertainty in the experimental process. Further contributions in the thesis include: experiments and results from visible-light versions of near field ptychography, which explain its limitations and practical application; a description and code for analysis tools that are used to assess phase imaging performance; DigitalMicrograph (DM) code and a data collection workflow to realise TEM-based near-field ptychography; details of the design, realisation and performance of the etched silicon nitride windows; and simulation studies aimed at furthering understanding of the frequency response of the technique. Future work is outlined, focusing on potential applications in a wide range of real-world specimens and improved TEM setups to implement near field ptychography

    Stereo Event-based Visual-Inertial Odometry

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    Event-based cameras are new type vision sensors whose pixels work independently and respond asynchronously to brightness change with microsecond resolution, instead of providing standard intensity frames. Compared with traditional cameras, event-based cameras have low latency, no motion blur, and high dynamic range (HDR), which provide possibilities for robots to deal with some challenging scenes. We propose a visual-inertial odometry for stereo event-based cameras based on Error-State Kalman Filter (ESKF). The visual module updates the pose relies on the edge alignment of a semi-dense 3D map to a 2D image, and the IMU module updates pose by median integral. We evaluate our method on public datasets with general 6-DoF motion and compare the results against ground truth. We show that our proposed pipeline provides improved accuracy over the result of the state-of-the-art visual odometry for stereo event-based cameras, while running in real-time on a standard CPU (low-resolution cameras). To the best of our knowledge, this is the first published visual-inertial odometry for stereo event-based cameras
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