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Symbolic Meta-Modeling of RIS-Assisted FSO Communication Channels

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TechRxiv

TechRxiv 2023年

作者： Ndjiongue, Alain Richard Ngatched, Telex M.N. Dobre, Octavia A. Haas, Harald Shin, Hyundong The Faculty of Engineering and Applied Science Memorial University of Newfoundland Canada The Department of Electrical and Computer Engineering McMaster University Canada The LiFi Research and Development Center Dpt. Electronic and Electrical Engineering The University of Strathclyde Glasgow United Kingdom The School of Electronics and Information Kyung Hee University 1 Seocheon Kihung Kyungki Yongin446-701 Korea Republic of

Due to their advantages and role in the beyond fifth-generation mobile network, free-space optical (FSO) communication systems continue to gain attention. The line-of-sight (LoS) link in FSO communications is fundamental as it delivers all the power detected by the user. Therefore, its obstruction is detrimental to the transmission system. In the event of an obstruction, the LoS requirements can be met by introducing the reconfigurable intelligent surface (RIS) technology. However, with the implementation of an RIS infrastructure, FSO channels are divided into two sub-channels, creating an inherent diversity end-to-end (e2e) channel model. In this paper, leveraging on the Meijer G-functions, which are symbolic representations of mathematical functions, we derive symbolic models for e2e RIS-assisted FSO systems that are built on the probability density functions (PDFs) of the fading model characterizing the sub-channels. We study some of the most popular models, derive closed-form expressions of PDFs, cumulative distributions, and moment-generating functions that are further exploited to analyze systems’ performance. By comparing scenarios in which the sub-channels are similar and different, we provide a clear understanding of how the overall RIS-assisted FSO channel behaves, along with comparative results based on commonly used models. © 2023, CC BY.

关键词： Probability density function

A letter on convergence of in-parameter-linear nonlinear neural architectures with gradient learnings

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arXiv 2021年

作者： Bukovsky, Ivo Dohnal, Gejza Benes, Peter M. Ichiji, Kei Homma, Noriyasu The Dpt. of Computer Science Faculty of Science University of South Bohemia Ceske Budejovice Czech Republic The Dpt. of Mechaniccs Biomechanics and Mechatronics Faculty of Mechanical Engineering Czech Technical University in Prague Czech Republic The Dpt. of Technical Mathematics Faculty of Mechanical Engineering Technical University Prague Czech Republic The Tohoku University Graduate School of Medicine 2-1 Seriyo-machi Aoba-ku Sendai980-8575 Japan

This letter summarizes and proves the concept of bounded-input bounded-state (BIBS) stability for weight convergence of a broad family of in-parameter-linear nonlinear neural architectures as it generally applies to a broad family of incremental gradient learning algorithms. A practical BIBS convergence condition results from the derived proofs for every individual learning point or batches for real-time applications. © 2021, CC BY-NC-ND.

关键词： Network architecture

Recognition of food-texture attributes using an in-ear microphone

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arXiv 2021年

作者： Mars, Monica Diou, Christos Papapanagiotou, Vasileios van den Boer, Janet Delopoulos, Anastasios Multimedia Understanding Group Dpt. Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece Dpt. of Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Division of Human Nutrition and Health Wageningen University Netherlands

Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness. Copyright © 2021, The Authors. All rights reserved.

关键词： Wearable technology

Quantile encoder: Tackling high cardinality categorical features in regression problems

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arXiv 2021年

作者： Mougan, Carlos Masip, David Nin, Jordi Pujol, Oriol Electronics and Computer Science University of Southampton United Kingdom Centre Recerca Matematica Universitat Autonoma de Barcelona Spain Universitat Ramon Llull ESADE Barcelona Spain Dpt. of Mathematics and Computer Science Universitat de Barcelona Spain

Regression problems have been widely studied in machine learning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem of how to incorporate categorical features to regression problems. Usually, categorical feature encoders are general enough to cover both classification and regression problems. This lack of specificity results in underperforming regression models. In this paper, we provide an in-depth analysis of how to tackle high cardinality categorical features with the quantile. Our proposal outperforms state-of-the-art encoders, including the traditional statistical mean target encoder, when considering the Mean Absolute Error, especially in the presence of long-tailed or skewed distributions. Besides, to deal with possible overfitting when there are categories with small support, our encoder benefits from additive smoothing. Finally, we describe how to expand the encoded values by creating a set of features with different quantiles. This expanded encoder provides a more informative output about the categorical feature in question, further boosting the performance of the regression *** Codes 68T05 Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

Reliability assessment of CubeSat nanosatellites flight software by high-order Markov chains

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Procedia computer science 2021年 192卷 447-456页

作者： Vitaliy Yakovyna Ivan Symets University of Warmia and Mazury in Olsztyn Faculty of Mathematics and Computer Science ul. Oczapowskiego 2 10-719 Olsztyn Poland Lviv Polytechnic National University Software Dpt. 12 S. Bandery St. 79013 Lviv Ukraine

In complex software, such as various intelligent monitoring and control systems, the requirements for its reliability and safety increase. However, traditional reliability models cannot longer describe its reliability behavior with sufficient accuracy. This paper presents an approach to software reliability assessment using high-order Markov chains on an example of CubeSat nanosatellites flight software. The method uses the developed algorithm of high-order Markov process representation through an equivalent first-order process. The simulation results show that high-order Markov chains can increase the accuracy of the failure rate assessment up to 50%. Hence, the usage of the high-order reliability models is obvious for accurate and reliable evaluation of software reliability and safety.

关键词： reliability software architecture-based model high-order Markov chain

Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement

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arXiv 2025年

作者： Torres, Daniel Duran, Joan Navarro, Julia Sbert, Catalina Dpt. Mathematics and Computer Science IAC3 Universitat de les Illes Balears Cra. de Valldemossa km. 7.5 Illes Balears Palma07122 Spain

Images captured under low-light conditions present significant limitations in many applications, as poor lighting can obscure details, reduce contrast, and hide noise. Removing the illumination effects and enhancing the quality of such images is crucial for many tasks, such as image segmentation and object detection. In this paper, we propose a variational method for low-light image enhancement based on the Retinex decomposition into illumination, reflectance, and noise components. A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition. Moreover, our model integrates a novel nonlocal gradient-type fidelity term designed to preserve structural details. Additionally, we propose an automatic gamma correction module. Building on the proposed variational approach, we extend the model by introducing its deep unfolding counterpart, in which the proximal operators are replaced with learnable networks. We propose cross-attention mechanisms to capture long-range dependencies in both the nonlocal prior of the reflectance and the nonlocal gradient-based constraint. Experimental results demonstrate that both methods compare favorably with several recent and state-of-the-art techniques across different datasets. In particular, despite not relying on learning strategies, the variational model outperforms most deep learning approaches both visually and in terms of quality metrics. © 2025, CC BY.

关键词： Variational techniques

On deploying the artificial sport trainer into practice

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arXiv 2021年

作者： Fister, Iztok Iglesias, Andres Deb, Suash Fister, Iztok Galvez, Akemi Fister, Dušan Faculty of Electrical Engineering and Computer Science University of Maribor SI- Maribor2000 Slovenia Dpt. of Applied Mathematics and Computational Sciences University of Cantabria Santander39005 Spain IT & educational Consultant Ranchi Jharkhand India Faculty of Economics and Business University of Maribor MariborSI-2000 Slovenia

—Computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines have achieved a mature phase. In order to confirm their added value, they have been deployed into practice. As a result, several methods have been developed for generating well formulated training plans on computers automatically that, typically, depend on the collection of past sport activities. However, monitoring the realization of the performed training sessions still represents a bottleneck in automating the process of sport training as a whole. The objective of this paper is to present a new low-cost and efficient embedded device for monitoring the realization of sport training sessions that is dedicated to monitor cycling training sessions. We designed and developed a new bike computer, i.e. the AST-Monitor, that can be mounted easily on almost every bicycle. The aforementioned bike computer is based on the Raspberry Pi device that supports different external sensors for capturing the data during the realization of sport training sessions. An adjusted GUI tailored to the needs of athletes is developed, along with the hardware. The proof of concept study, using the AST-Monitor in practice, revealed the potential of the proposed solution for monitoring of realized sport training sessions automatically. The new device also opens the door for the future utilization of Artificial Intelligence in a wide variety of sports. Copyright © 2021, The Authors. All rights reserved.

关键词： Sports

Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images

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arXiv 2020年

作者： Petrović, Nataša Moyà-Alcover, Gabriel Biel Jaume-i-Capó, Antoni González-Hidalgo, Manuel SCOPIA research group University of the Balearic Islands Dpt. of Mathematics and Computer Science Crta. Valldemossa km 7.5 PalmaE-07122 Spain PalmaE-07010 Spain UGiVIA research group University of the Balearic Islands Dpt. of Mathematics and Computer Science Crta. Valldemossa km 7.5 PalmaE-07122 Spain

In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cells. In our case we used samples of patients with sickle-cell disease which can be generalized for other study cases. To trust the behavior of the proposed system, we also analyzed the interpretability. We pre-processed and segmented microscopic images, to ensure high feature quality. We applied the methods used in the literature to extract the features from blood cells and the machine learning methods to classify their morphology. Next, we searched for their best parameters from the resulting data in the feature extraction phase. Then, we found the best parameters for every classifier using Randomized and Grid search. For the sake of scientific progress, we published parameters for each classifier, the implemented code library, the confusion matrices with the raw data, and we used the public erythrocytesIDB dataset for validation. We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models. Finally, comparing the best performing classification methods with the state-of-the-art, we obtained better results even with interpretable model classifiers. Copyright © 2020, The Authors. All rights reserved.

关键词： Blood

UlsaNA: Universal language semantic analyzer 12

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UlsaNA: Universal language semantic analyzer

12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019

作者： Pražák, Ondřej Konopík, Miloslav NTIS Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň306 14 Czech Republic Dpt. of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň306 14 Czech Republic

ISBN: (纸本)9789544520557

We present a live cross-lingual system capable of producing shallow semantic annotations of natural language sentences for 51 languages at this time. The domain of the input sentences is in principle unconstrained. The system uses single training data (in English) for all the languages. The resulting semantic annotations are therefore consistent across different languages. We use CoNLL Semantic Role Labeling training data and Universal dependencies as the basis for the system. The system is publicly available and supports processing data in batches;therefore, it can be easily used by the community for research tasks. © 2019 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Deep learning