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Dynamic Image Deblurring based on Crosshatch Attention Adversarial Network and Hybrid Loss

Dynamic Image Deblurring based on Crosshatch Attention Adver...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Ummama Aslam Arslan Shaukat Basim Azam Dept. of Computer and Software Engineering College of Electrical and Mechanical Engineering (CEME) National University of Sciences and Technology (NUST) Islamabad Pakistan Institute of Integrated and Intelligent Systems Griffith University Brisbane Australia

Image deblurring techniques that uses deep learning have shown great potential but due to low generalizability, noise immunity and the correlation among different pixels is not addressed in detail that results in unwanted artifact that appears in the deblurred image. To tackle this problem an end-to-end approach is proposed for the recovery of sharp image from blurred one without the estimation of blur kernel. A special type of attention module known as crosshatch attention is used after Residual Block of Generator model for removing noise and for the collection of correlation of different pixels in an image. Hybrid Loss function is defined which focus on different part of image and improve edges and texture details. The performance of the model for deblurring is measured on GoPro dataset. Our proposed model has slightly higher objective and subjective evaluation i-e PSNR, SSIM value and the visual results.

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Mingze Li Weihang Ren Lei Fan Yongpei Guan Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA Dept. of Industrial and Systems Engineering University of Florida FL USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation.

关键词： Heuristic algorithms Clustering methods Clustering algorithms Search problems Boundary conditions Power grids Complexity theory Optimization AC power transmission

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Time-Stretched Imaging Flow Cytometry and Photonic Neuromorphic Processing for Particle Classification

Time-Stretched Imaging Flow Cytometry and Photonic Neuromorp...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Tsirigotis, Aris Deligiannidis, Stavros Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. 83200 Karlovasi Samos Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece

ISBN: (纸本)9798350345995

In this work, we investigate the compression capabilities of a photonic neuromorphic accelerator relying on an optical spectrum slicing technique [1], following a high-flow 1D imaging cytometry setup, able to image 62000 particles/sec. The utilized image scheme relies on a technique, where spatial information is transfered to the spectrum and subsequently through dispersion to the temporal domain. Therefore, it can replace high speed spectrometers with a single photodetector [2]. On the other hand, the elevated image acquisition rate, trigger the generation of a staggering amount of data, which can cause a processing backlog at the digital backend. In this context, we explore, for the first time, the use of a photonic neuromorphic pre-processing unit, following the cytometer that can reduce the number of trainable parameters at a lightweight digital backend by a factor of 2.5, while at the same time, it can project incoming data to a high dimensional space boosting accuracy. © 2023 IEEE.

关键词： Photonics

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Mobile Application for Visualizing the Measurements 5

Mobile Application for Visualizing the Measurements

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Kiresova, Simona Guzan, Milan Sobota, Branislav Melnykov, Viacheslav Yatsiuk, Rostyslav Peryvertailo, Bohdan Technical University of Košice Dept. of Theoretical and Industrial Electrical Engineering Košice Slovakia Technical University of Košice Dept. of Computers and Informatics Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Dept. of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine National University Dept. of Computer Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This paper deals with the design of an Android mobile application and visualization of the measured values of particulate matter and meteorological factors from the measurement stations. The application can in principle be effectively used in any field of data visualization. The architecture of the mobile application, the security, the use of AWS services to access the data in the InfluxDB databases, as well as the user interface and graphical visualizations of the measured data are described and illustrated. The application is user tested and the paper documents their first experiences using the mobile application. © 2023 IEEE.

关键词： Visualization

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Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Deligiannidis, Stavros Tsirigotis, Aris Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Karlovasi Samos83200 Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach. © 2023 IEEE.

关键词： Cameras

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Detection of Ophthalmic Disorders Through Deep Feature Fusion

Detection of Ophthalmic Disorders Through Deep Feature Fusio...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Hussain, Muhammad Toheed Faraz, Muhammad Aziz, Sumair Iqtidar, Khushbakht Khan, Muhammad Umar Montes, Gabriel Axel University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences and Tech. Dept. of Computer and Software Engineering Islamabad Pakistan University of Newcastle CallaghanNSW Australia

ISBN: (纸本)9798350349719

Given the rising prevalence of ophthalmic disorders, this study aims to meet the urgent need for an automated technique capable of diagnosing and treating these conditions. Our approach combines deep learning and machine learning techniques to develop a diagnostic system that can detect and classify seven distinct ophthalmic disorders. Manual diagnosis of ophthalmic disorders is labor-intensive and slow, worsened by a shortage of medical experts. In this paper, we propose a medical diagnostic system that utilizes deep features extracted from a ResNet-50 CNN, which has been trained on retinal images enhanced through contrast-limited adaptive histogram equalization (CLAHE). These deep features serve as input for the machine learning model. To address the class imbalance problem, we employed ADASYN to generate synthetic features for all classes. An Ensemble subspace KNN classifier, trained on these deep features, achieved a remarkable mean accuracy of 99.18% compared to other classifiers evaluated in this study. The proposed technique has the potential to assist medical experts in achieving timely and accurate diagnoses of ophthalmic diseases. © 2024 IEEE.

关键词： Ophthalmology

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Weihang Ren Mingze Li Lei Fan Yongpei Guan Dept. of Industrial and Systems Engineering University of Florida FL USA Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks.

关键词： Runtime Scalability Power systems Planning Security Optimization AC power transmission Load flow

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Auction based Resource Allocation in Cloud Computing using Blockchain Techniques 14

Auction based Resource Allocation in Cloud Computing using B...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kamaleshwar, T. Jayaraj, R. Vijayaraj, N. Sundaranarayana, D. Dr.Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Computer Science & Engineering Chennai India SRM Institute of Science and technology Dept. of Data Science and Business systems Kattankulathur Chennai India

As the spine innovation of decentralized cryptocurrencies, blockchain has additionally proclaimed numerous applications in different fields, for example, resource allocation in cloud computing, Internet of Things (IoT) and resource offloading. The database state is decentrally kept up, and any part hub in the overlay blockchain organize is allowed to partake in the state support without character confirmation. The exchanges among part hubs are recorded in cryptographic hash-connected information structures known as squares. A progression of affirmed blocksis orchestrated in ordered request to shape a consecutive chain, henceforth named blockchain. All part hubs in the system are required to pursue consensus protocol on the value-based information, cryptographic hashes and computerized marks put away in the block to ensure trustworthiness of the blockchain. © Grenze Scientific Society, 2023.

关键词： Blockchain

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EEG-Based Fatigue Detection Using Binary Pattern Analysis and KNN Algorithm 10

EEG-Based Fatigue Detection Using Binary Pattern Analysis an...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Rahman, Faridah Abd Roslizan, Iszan Uwais Amer Gunawan, Teddy Surya Fitriawan, Helmy Kartiwi, Mira Habaebi, Mohamed Hadi International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universitas Lampung Department of Electrical Engineering Bandar Lampung35145 Indonesia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350365283

Fatigue is a prevalent issue that disrupts the overall well-being of individuals, leading to impaired cognitive functions such as learning, thinking, reasoning, remembering, and problem-solving. Chronic fatigue significantly increases the risk of accidents due to reduced focus, vigilance, and delayed reaction times. Traditional self-assessment methods for detecting fatigue are subjective and often unreliable. Recent advancements in neuroimaging have demonstrated that EEG signal analysis can objectively classify an individual's mental state. This research aims to develop a reliable and accurate EEG signal fatigue detection system. The EEG signals are decomposed into four levels using a one-dimensional discrete wavelet transform (1D-DWT). Textural features are extracted using binary pattern (BP) analysis and combined with seven statistical features. Then, these features are fed into a k-nearest neighbors (KNN) classifier to distinguish between the rest and fatigue states. Utilizing a dataset from the Mendeley Data website, the proposed system achieved an accuracy of 93.75%, precision between 93% and 95%, recall ranging from 92% to 95%, and an F1-score of 93% to 94%. This study highlights the potential of EEG-based systems to provide objective and accurate assessments of fatigue levels, thereby reducing the risks associated with chronic fatigue in daily life. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Deep Learning Methods with Pre-Trained Word Embeddings and Pre-Trained Transformers for Extreme Multi-Label Text Classification 6

Deep Learning Methods with Pre-Trained Word Embeddings and P...

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Erciyes, Necdet Eren Görür, Abdul Kadir Computer Engineering Dept. Çankaya University Ankara Turkey Software Engineering Dept. Çanicaya University Ankara Turkey

ISBN: (纸本)9781665429085

In recent years, there has been a considerable increase in textual documents online. This increase requires the creation of highly improved machine learning methods to classify text in many different domains. The effectiveness of these machine learning methods depends on the model capacity to understand the complex nature of the unstructured data and the relations of features that exist. Many different machine learning methods were proposed for a long time to solve text classification problems, such as SVM, kNN, and Rocchio classification. These shallow learning methods have achieved doubtless success in many different domains. For big and unstructured data like text, deep learning methods which can learn representations and features from the input data wtihout using any feature extraction methods have shown to be one of the major solutions. In this study, we explore the accuracy of recent recommended deep learning methods for multi-label text classification starting with simple RNN, CNN models to pretrained transformer models. We evaluated these methods' performances by computing multi-label evaluation metrics and compared the results with the previous studies. © 2021 IEEE

关键词： Embeddings

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