10,500 research outputs found

    Exciton Dynamics, Interaction, and Transport in Monolayers of Transition Metal Dichalcogenides

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    Monolayers Transition metal dichalcogenides (TMDs) have attracted much attention in recent years due to their promising optical and electronic properties for applications in optoelectronic devices. The rich multivalley band structure and sizable spin-orbit coupling in monolayer TMDs result in several optically bright and dark excitonic states with different spin and valley configurations. In the proposed works, we have developed experimental techniques and theoretical models to study the dynamics, interactions, and transport of both dark and bright excitons. In W-based monolayers of TMDs, the momentum dark exciton cannot typically recombine optically, but they represent the lowest excitonic state of the system and can severely affect the overall optical performances. We performed theoretical and experimental studies that show that compressive strain allows us to visualize intervalley momentum dark exciton in the PL spectrum and that these excitons can find application in strain sensing. We show that in monolayer WS2 the formation of momentum dark exciton is greatly enhanced even with small compressive strain due to intervalley electron-phonon coupling, and their spectral properties strongly correlated with the strain magnitude. Furthermore, we show a similar mechanism for WSe2, however, with tensile due to its qualitatively different band structure than WS2. We exploited this correlation for strain sensing in two-dimensional semiconductors, revealing an optical gauge factor exceeding 104. We then focused on spin dark excitons that possess an out-of-plane optical transition dipole, strong binding energy, and long lifetime. Therefore, spin forbidden excitons are promising candidates for interaction-driven long-range transportation. Moreover, these excitons are characterized by lower energy and exhibit a significantly higher density as supported by our theoretical model. By employing a high-resolution spatially resolved PL setup in an encapsulated monolayer of WS2, we demonstrated that the strong repulsive interaction arising from their high density and longer lifetime enables these dark excitons to diffuse up to several micrometers. Furthermore, we conduct experiments in the energy landscape and show that the repulsive interaction can provide energy to dark excitons for transportation even in an uphill energy landscape. This repulsion-driven long-range transport of dark states provides a new route for excitonic devices that could be used for both classical and quantum information processing. Last, we investigated the optical properties of monolayers of TMDs in different structural phases. Monolayers of TMDs occur in the semiconducting 1H phase, whose optical properties are dominated by excitons, and the metallic 1T phase, however less stable than the 1H phase. We developed a method to engineer stable the 1H/1T mixed phase starting with a pristine 1H phase monolayer WS2 by plasma irradiation process. We can control the size of 1T patches by tuning plasma irradiation time. We observe a novel resonance in mixed-phase WS2 monolayers characterized by a lower excitonic energy compared to the bright exciton and exhibits enhanced absorption, extended lifetime, and circular polarization. We attribute the emergence of these unique excitonic states to the interface that forms between two distinct phases. This interpretation gains additional support from our calculations of the dielectric function carried out on the mixed-phase supercell containing both 1H and 1T phases, revealing a novel optical response at lower energies

    Sistema de Diagnostico del Alzheimer basado en imágenes de resonancia magnética mediante el algoritmo VGG16

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    Early diagnosis of Alzheimer's disease is essential to provide timely treatment to patients. In this regard, a system for diagnosing Alzheimer's disease based on magnetic resonance imaging and utilizing a convolutional neural network algorithm called VGG16, has been developed. Magnetic resonance images of patients with and without Alzheimer's disease were collected and processed. These images were used to train the algorithm, which learned to identify and associate patterns with the disease. Subsequently, tests were performed with a set of unseen images to evaluate the diagnostic ability of the system. Through the analysis of magnetic resonance images, the VGG16 algorithm has shown a capacity of over 82% to correctly recognize these signs. These results validate the effectiveness of the artificial intelligence-based approach for diagnosing Alzheimer's disease.El diagnóstico temprano del Alzheimer es fundamental para brindar un tratamiento oportuno a los pacientes. En este sentido se ha desarrollado un sistema de diagnóstico del Alzheimer basado en imágenes de resonancia magnética que utiliza un algoritmo de redes neuronales convolucionales denominado VGG16. Se recopilaron y procesaron imágenes de resonancia magnética de pacientes con y sin Alzheimer. Estas imágenes se utilizaron para entrenar al algoritmo, el cual aprendió a identificar y asociar patrones con la enfermedad. Posteriormente, se realizaron pruebas con un conjunto de imágenes no vistas para evaluar la capacidad de diagnóstico del sistema. Mediante el análisis de las imágenes de resonancia magnética, el algoritmo VGG16 ha demostrado una capacidad superior al 82% para reconocer correctamente dichos signos. Estos resultados validan la efectividad del enfoque basado en inteligencia artificial para el diagnóstico del Alzheimer

    Deep ensemble model-based moving object detection and classification using SAR images

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    In recent decades, image processing and computer vision models have played a vital role in moving object detection on the synthetic aperture radar (SAR) images. Capturing of moving objects in the SAR images is a difficult task. In this study, a new automated model for detecting moving objects is proposed using SAR images. The proposed model has four main steps, namely, preprocessing, segmentation, feature extraction, and classification. Initially, the input SAR image is pre-processed using a histogram equalization technique. Then, the weighted Otsu-based segmentation algorithm is applied for segmenting the object regions from the pre-processed images. When using the weighted Otsu, the segmented grayscale images are not only clear but also retain the detailed features of grayscale images. Next, feature extraction is carried out by gray-level co-occurrence matrix (GLCM), median binary patterns (MBPs), and additive harmonic mean estimated local Gabor binary pattern (AHME-LGBP). The final step is classification using deep ensemble models, where the objects are classified by employing the ensemble deep learning technique, combining the models like the bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), and improved deep belief network (IDBN), which is trained with the features extracted previously. The combined models increase the accuracy of the results significantly. Furthermore, ensemble modeling reduces the variance and modeling method bias, which decreases the chances of overfitting. Compared to a single contributing model, ensemble models perform better and make better predictions. Additionally, an ensemble lessens the spread or dispersion of the model performance and prediction accuracy. Finally, the performance of the proposed model is related to the conventional models with respect to different measures. In the mean-case scenario, the proposed ensemble model has a minimum error value of 0.032, which is better related to other models. In both median- and best-case scenario studies, the ensemble model has a lower error value of 0.029 and 0.015

    Modellbasierte Simulation und Kalibrierung eines multimodalen Systems aus OCT und Optoakustik zur nichtinvasiven, präoperativen Dickenbestimmung von melanomverdächtigen Hautläsionen

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    In this dissertation, methods for the calibration of optical coherence tomography (OCT) systems and for the simulation of optoacoustic signals are presented. The key question here is whether a multimodal system consisting of OCT and optoacoustics is suitable for noninvasive, preoperative thickness determination of skin lesions suspected of melanoma and what conditions, if any, must be met for this purpose. Given the current state of the art, such a modality for melanoma diagnosis would be very enriching for dermatology. In addition to the definition of malignant melanoma, the most common diagnostic procedures in dermatology will be explained. The current approach to melanoma diagnostics shows that there is a lot of potential for improvement in order to be able to make diagnoses preoperatively in the future and to prevent unnecessary surgical interventions. The project in which this work was developed is briefly presented. It also discusses the physical principles needed to simulate and calibrate the multimodal system. The methods presented in chapters 6 and 7 for calibrating the OCT and for simulating the optoacoustic signals then build on these fundamentals. The general setup of OCT systems as well as of two specific OCT devices is explained. The methods then presented for geometric calibration and refractive index correction are essential for the thickness determination of structures in OCT images. In chapter 7 different methods are presented which are suitable for the simulation of optoacoustic signals. On the one hand, the solution of the direct problem, i.e. the creation of optoacoustic signals, is shown as well as the solution of the indirect problem, in which conclusions can be drawn about the initial pressure profile if optoacoustic signals are available. Furthermore, optoacoustic signals of simulated melanomas are generated and evaluated, which is also important for answering the key question. The results of this dissertation are discussed in detail at the end and an outlook is given on how the work on the multimodal system will continue.In der vorliegenden Dissertation werden Methoden zur Kalibrierung von Optischen Kohärenztomographie (OCT)-Systemen und zur Simulation von Optoakustiksignalen präsentiert. Die Kernfrage hierbei ist, ob ein multimodales System aus OCT und Optoakustik für eine nichtinvasive, präoperative Dickenbestimmung von melanomverdächtigen Hautläsionen geeignet ist und welche Bedingungen hierfür gegebenenfalls erfüllt werden müssen. Beim derzeitigen Stand der Technik wäre solch eine Modalität für die Melanomdiagnostik sehr bereichernd f ¨ ur die Dermatologie. Neben der Definition eines malignen Melanoms werden die geläufigsten diagnostischen Verfahren in der Dermatologie erläutert. Das momentane Vorgehen bei der Melanomdiagnostik zeigt, dass hier sehr viel Potenzial für Verbesserungen ist, um zukünftig Diagnosen präoperativ vornehmen und unnötige operative Eingriffe verhindern zu können. Es wird kurz das Projekt vorgestellt, in dem diese Arbeit entstanden ist. Außerdem werden die physikalischen Grundlagen erörtert, die für die Simulation und Kalibrierung des multimodalen Systems benötigt werden. Auf diesen Grundlagen bauen dann die in Kapitel 6 und 7 vorgestellten Methoden zur Kalibrierung des OCT sowie zur Simulation der optoakustischen Signale auf. Es wird der allgemeine Aufbau von OCT-Systemen sowie von zwei speziellen OCT-Geräten erklärt. Die dann vorgestellten Methoden zur geometrischen Kalibrierung und zur Brechungsindexkorrektur sind unerlässlich für eine Dickenbestimmung von Strukturen in OCT-Bildern. In Kapitel 7 werden verschiedene Verfahren vorgestellt, die sich zur Simulation von optoakustischen Signalen eignen. Hier wird zum einen die Lösung des direkten Problems, also das Erzeugen von Optoakustiksignalen gezeigt sowie die Lösung des indirekten Problems, bei der Rückschluss auf das initiale Druckprofil geschlossen werden kann, wenn Optoakustiksignale vorliegen. Weiterhin werden Optoakustiksignale von simulierten Melanomen erzeugt und ausgewertet, was ebenfalls wichtig für die Beantwortung der Kernfrage ist. Die Ergebnisse dieser Dissertation werden zum Schluss ausführlich erörtert und es wird ein Ausblick darauf gegeben, wie die Arbeit am multimodalen System weitergeht

    A New Class of Linear Canonical Wavelet Transform

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    We define a new class of linear canonical wavelet transform (LCWT) and study its properties like inner product relation, reconstruction formula and also characterize its range. We obtain Donoho-Stark’s uncertainty principle for the LCWT and give a lower bound for the measure of its essential support. We also give the Shapiro’s mean dispersion theorem for the proposed LCWT

    SmartChoices: Augmenting Software with Learned Implementations

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    We are living in a golden age of machine learning. Powerful models are being trained to perform many tasks far better than is possible using traditional software engineering approaches alone. However, developing and deploying those models in existing software systems remains difficult. In this paper we present SmartChoices, a novel approach to incorporating machine learning into mature software stacks easily, safely, and effectively. We explain the overall design philosophy and present case studies using SmartChoices within large scale industrial systems

    Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection

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    Fabric defect segmentation is integral to textile quality control. Despite this, the scarcity of high-quality annotated data and the diversity of fabric defects present significant challenges to the application of deep learning in this field. These factors limit the generalization and segmentation performance of existing models, impeding their ability to handle the complexity of diverse fabric types and defects. To overcome these obstacles, this study introduces an innovative method to infuse specialized knowledge of fabric defects into the Segment Anything Model (SAM), a large-scale visual model. By introducing and training a unique set of fabric defect-related parameters, this approach seamlessly integrates domain-specific knowledge into SAM without the need for extensive modifications to the pre-existing model parameters. The revamped SAM model leverages generalized image understanding learned from large-scale natural image datasets while incorporating fabric defect-specific knowledge, ensuring its proficiency in fabric defect segmentation tasks. The experimental results reveal a significant improvement in the model's segmentation performance, attributable to this novel amalgamation of generic and fabric-specific knowledge. When benchmarking against popular existing segmentation models across three datasets, our proposed model demonstrates a substantial leap in performance. Its impressive results in cross-dataset comparisons and few-shot learning experiments further demonstrate its potential for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table

    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

    Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

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    Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes
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