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Sunflower Array of Infinitesimal Dipoles for Constrained Antenna Modeling

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Sunflower Array of Infinitesimal Dipoles for Constrained Ant...

European Conference on Antennas and Propagation, EuCAP

作者： Nehir Berk Onat Alexander Yarovoy Yanki Aslan Department of Microelectronics Faculty of Electrical Engineering Microwave Sensing Signals and Systems Group Mathematics and Computer Science Delft University of Technology Delft The Netherlands

The sunflower array topology concept is introduced, for the first time, to the constrained infinitesimal dipole modeling (IDM) technique to increase the computational efficiency and reduce the modeling errors. The concept is applied to embedded element pattern predictions via matrix inversion. A novel study on the impact of the type and orientation of the dipoles on the IDM performance in pattern mean square error (MSE) and stability against noise (linked to the matrix condition number) is conducted. A 5 by 5 patch antenna array modeled with 81 dipoles is used for demonstration. It is shown that using magnetic dipoles (oriented in the direction of a radiating edge of the patch) in IDM yields the optimal performance. Besides, the sunflower topology significantly lowers the MSE (by 5 dB, on average), while reducing the condition number by a factor of 10.

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Image Compression with Isotropic and Anisotropic Shepard Inpainting

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arXiv 2024年

作者： Mohideen, Rahul Mohideen Kaja Alt, Tobias Peter, Pascal Weickert, Joachim Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Campus E1.7 Saarbrücken66041 Germany

Inpainting-based codecs store sparse selected pixel data and decode by reconstructing the discarded image parts by inpainting. Successful codecs (coders and decoders) traditionally use inpainting operators that solve partial differential equations. This requires some numerical expertise if efficient implementations are necessary. Our goal is to investigate variants of Shepard inpainting as simple alternatives for inpainting-based compression. They can be implemented efficiently when we localise their weighting function. To turn them into viable codecs, we have to introduce novel extensions of classical Shepard interpolation that adapt successful ideas from previous codecs: Anisotropy allows direction-dependent inpainting, which improves reconstruction quality. Additionally, we incorporate data selection by subdivision as an efficient way to tailor the stored information to the image structure. On the encoding side, we introduce the novel concept of joint inpainting and prediction for isotropic Shepard codecs, where storage cost can be reduced based on intermediate inpainting results. In an ablation study, we show the usefulness of these individual contributions and demonstrate that they offer synergies which elevate the performance of Shepard inpainting to surprising levels. Our resulting approaches offer a more favourable trade-off between simplicity and quality than traditional inpainting-based codecs. Experiments show that they can outperform JPEG and JPEG2000 at high compression ratios. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

Parallel Affine Transformation Tuning of Markov Chain Monte Carlo

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arXiv 2024年

作者： Schär, Philip Habeck, Michael Rudolf, Daniel Microscopic Image Analysis Group Friedrich Schiller University Jena Jena Germany Faculty of Computer Science and Mathematics University of Passau Passau Germany

The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior. We explore the use of bijective affine transformations of the sample space to improve the properties of the target distribution and thereby the performance of samplers running in the transformed space. In particular, we propose a flexible and user-friendly scheme for adaptively learning the affine transformation during sampling. Moreover, the combination of our scheme with Gibbsian polar slice sampling is shown to produce samples of high quality at comparatively low computational cost in several settings based on real-world *** Codes 60J22, 65C05, 60J05 Copyright © 2024, The Authors. All rights reserved.

关键词： Monte Carlo methods

Efficient Parallel Algorithms for Inpainting-Based Representations of 4K Images - Part I: Homogeneous Diffusion Inpainting

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arXiv 2024年

作者： Kämper, Niklas Chizhov, Vassillen Weickert, Joachim Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Campus E1.7 Saarbrücken66041 Germany

In recent years inpainting-based compression methods have been shown to be a viable alternative to classical codecs such as JPEG and JPEG2000. Unlike transform-based codecs, which store coefficients in the transform domain, inpainting-based approaches store a small subset of the original image pixels and reconstruct the image from those by using a suitable inpainting operator. A good candidate for such an inpainting operator is homogeneous diffusion inpainting, as it is simple, theoretically well-motivated, and can achieve good reconstruction quality for optimized data. However, a major challenge has been to design fast solvers for homogeneous diffusion inpainting that scale to 4K image resolution (3840×2160 pixels) and are real-time capable. We overcome this with a careful adaptation and fusion of two of the most efficient concept from numerical analysis: multigrid and domain decomposition. Our domain decomposition algorithm efficiently utilizes GPU parallelism by solving inpainting problems on small overlapping blocks. Unlike simple block decomposition strategies such as the ones in JPEG, our approach yields block artifact-free reconstructions. Furthermore, embedding domain decomposition in a full multigrid scheme provides global interactions and allows us to achieve optimal convergence by reducing both low- and high-frequency errors at the same rate. We are able to achieve 4K color image reconstruction at more than 60 frames per second even from very sparse data - something which has been previously *** Codes 65N22, 65N55, 65D18, 68U10, 94A08 Copyright © 2024, The Authors. All rights reserved.

关键词： Graphics processing unit

Developer's Cognitive Effort Maintaining Monoliths vs. Microservices - An Eye-Tracking Study

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Developer's Cognitive Effort Maintaining Monoliths vs. Micro...

Asia-Pacific Conference on Software Engineering

作者： Georg Simhandl Philipp Paulweber Uwe Zdun Faculty of Computer Science Research Group Software Architecture University of Vienna Vienna Austria fiskaly GmbH Vienna Austria

The microservices architectural style improves flexi-bility and performance but might come at the cost of complexity and cognitive effort. Understanding how architectural design decision affects cognitive effort can support software engineers in designing and building more maintainable systems. However, little research exists measuring the impact of architectural styles on developers' cognitive processes. To the best of our knowledge, no empirical study on navigation and comprehension strategies compares a microservice-based and monolithic variant of a trading system, which is widely used for empirical research on information system evolution. To assess the cognitive processes, we conducted an eye-tracking study (n=42) of typical maintenance tasks. We randomly assigned participants to two groups performing a feature location and a code change task using the trading system's monolithic or microservices-based implementation. Efficacy is significantly higher in the monolith variant. We investigate the reasons and propose a cognitively-grounded method to analyse comprehension strategies and estimate maintenance effort. We measure differences in comprehension strategies (top-down and bottom-up comprehension) and conclude that 1) there is a significant difference in visual effort and time taken to identify a specific feature, and 2) bottom-up comprehension is more frequently applied in microservices than in the monolith variant. Finally, we discuss our findings and their implications for practitioners and the research community.

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One-bit Compressed Sensing using Generative Models

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arXiv 2025年

作者： Kafle, Swatantra Joseph, Geethu Varshney, Pramod K. ANDRO Computational Solutions NY United States Electrical Engineering and Computer Science Department Syracuse University NY United States Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty the Delft University of Technology Netherlands

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pretrained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements. © 2025, CC BY.

关键词： Compressed sensing

Exploring the Potential of the Machine Learning Techniques in the Water Quality Assessment: A Review of Applications and Performance

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Exploring the Potential of the Machine Learning Techniques i...

International Conference on Advanced Engineering, Technology and Applications, ICAETA 2024

作者： Márquez, Fausto Pedro García Al-taie, Ali Hussein Shuaa Zakur, Yahya Asmar Alsadoon, Abeer Flaih, Laith R. Zakoor, Yousif Asmar Ingenium Research Group Universidad Castilla-La Mancha Ciudad Real13071 Spain Department of Mathematics Faculty of Education for Pure Sciences Wasit University Wasit Iraq Department of Statistics Faculty of Mathematical Science University of Mazandaran Babolsar Iran Sydney Australia Sydney Australia Department of Computer Science College of Science Cihan University-Erbil Erbil Iraq Department of Civil Engineering College of Engineering Wasit University Wasit Iraq

ISBN: (纸本)9783031709234

In this review, the application of machine learning (ML) algorithms in water environment research is proficiently explored. The quick increase in data size related to the water environment has necessitated the use of ML for data analysis, classification, and forecasting. Unlike classical models, machine learning models excel in solving complex problems. They have been successfully applied to various aspects of water management and treatment systems, such as construction, simulation, evaluation, water pollution surveillance, controlling, water quality amelioration, and watershed environmental security management. The survey specifically focuses on the evaluation of water quality in diverse water environments, including surface water, drinking water, groundwater, sewage, and seawater. Moreover, potential future implementations of machine learning in water environments are proposed. ML facilitates the detection and prediction of water contamination events, as well as the provision of decision support systems for water resource management. Real-time monitoring of water quality, anomaly detection, and prediction of potential contamination events are among the specific applications of machine learning high-lighted. The review covers the advantages and disadvantages of generally used ML algorithms, with a particular emphasis on new ML techniques that surpass classical methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sewage

Sarax: An Open-Source Software/Hardware Framework for Aerial Manipulators

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Sarax: An Open-Source Software/Hardware Framework for Aerial...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ayham Alharbat Dion Zwakenberg Hanieh Esmaeeli Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The use of Multi-Rotor Aerial Vehicles (MRAVs) in tasks that require physical interaction has been an active research field in the last decade which resulted in an increasing interest in Aerial Manipulators (AMs). This raises many challenges in the modeling, control, perception, and planning of these robots. However, designing and realizing an AM testbed is a complicated multi-disciplinary task, and there is a lack of standardization in the relatively new field of AMs. For this purpose, we introduce Sarax, an open-source hardware and software framework tailored for AMs research and innovation. The software of Sarax is built on top of open-source projects such as the Robot Operating System (ROS) and PX4 Autopilot, while the hardware is designed to be customizable, modular, and easily scalable through parameterized models. We verified and validated the proposed framework through indoor and outdoor experiments. We aim to open the door to accelerate AMs research and innovation, allow researchers and developers to focus on their core contributions, and take AMs technology to a higher readiness level.

关键词： Technological innovation Operating systems Standardization Manipulators Software Planning Open source hardware

Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Nallakaruppan, M.K. Srivastava, Gautam Gadekallu, Thippa Reddy Reddy, Praveen Kumar Krishnan, Sivarama Polap, Dawid School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Computer Science Brandon University Brandon Canada School of Information Technology and Engineering Vellore Institute of Technology Department of Electrical and Computer Engineering Lebanese American University Lebanon Zhongda Group Zhejiang Jiaxing314312 China Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning