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18th IEEE International Conference on computer science and Information Technologies, CSIT 2023

作者： Khudov, Hennadii Makoveichuk, Oleksandr Khudov, Vladyslav Khizhnyak, Irina Kalimulin, Temir Khudov, Rostyslav Ivan Kozhedub Kharkiv National Air Force University Department of Radar Troops Tactic Kharkiv Ukraine Academician Y. Bugay International Scientific and Technical University Department of Computer Science and Software Engineering Kyiv Ukraine Kharkiv National University of Radio Electronics Department of Electronic Computers Kharkiv Ukraine V.N.Karazin Kharkiv National University Department of Theoretical and Applied Informatics Kharkiv Ukraine

ISBN: (纸本)9798350360462

The article deals with the problem associated with the segmentation of color images. The main groups of color image segmentation methods operation, are considered. The main advantages and disadvantages of each group of methods are shown. The paper suggests the use of the Canny edge detector for optical-electronic images obtained from space observation systems. It is proposed to regulate the parameters of the Canny edge detector in order to improve the segmentation result. It's determined that the value of the lower threshold should be no more than 10-20 unit, and the value of the upper threshold should be within 60-100 unit. © 2023 IEEE.

关键词： Image segmentation

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Continuous Image Outpainting with Neural ODE

SSRN

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SSRN 2023年

作者： Gao, Penglei Yang, Xi Zhang, Rui Huang, Kaizhu Department of Computer Science University of Liverpool United Kingdom School of Advanced Technology Department of Foundational Mathematics Xi’an Jiaotong-Liverpool University China Institute of Applied Physical Sciences and Engineering Duke Kunshan University China

Different from image inpainting, generalised image outpainting aims to extend appealing content all-side around a given image. Existing state-of-the-art outpainting methods often rely on discrete extrapolation to extend the feature map in the bottleneck. They thus suffer from content unsmoothness, especially in circumstances where the outlines of objects in the extrapolated regions are incoherent with the input sub-images. To mitigate this issue, we design a novel bottleneck with Neural ODEs to make continuous extrapolation in latent space, which could be a plug-in for many deep learning frameworks. Our ODE-based network continuously transforms the state and makes accurate predictions by learning the incremental relationship among latent points, leading to both smooth and structured feature representation. Experimental results on three real-world datasets both applied on transformer-based and CNN-based frameworks show that our methods could generate more realistic and coherent images against the state-of-the-art image outpainting approaches. © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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Comparison of Google Analytics with Similar Web for Statistical Analysis of Website Traffic 1st

Comparison of Google Analytics with Similar Web for Statisti...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Garg, Bindu Kasar, Manisha Kotecha, Ketan Rahmani, Mohammad Khalid Imam University College of Engineering Pune India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031860713

This study addresses the burgeoning demand for website data collection and analysis in business operations, emphasizing the pivotal role of web analytics in providing crucial insights into customer behaviour. Despite the prevalence of various website traffic analysing tools, there exists a notable lack of comprehensive testing on the precision and reliability of these services. The research compares four key analytics measures from Google Analytics (GA) with SimilarWeb (SW) using average monthly statistics over a one-year period for 50+ websites across different nations and business verticals. Employing various statistical tests, including paired t-tests, Shapiro-Wilk tests, and correlation analysis, the study reveals statistically significant variations in key metrics between GA and SW. Notably, SimilarWeb tends to provide lower estimates for the number of visits and unique visitors compared to Google Analytics. However, the findings underscore the potential complementarity of using both tools in cases where direct access to a site’s analytics is not feasible, particularly for competitive intelligence and benchmarking purposes. The study’s extensive statistical analysis covers unique visitors, total visits, bounce rates, and average session duration, offering valuable insights into the nuanced variations and potential sources of error. There are statistically significant differences between the two web traffic tools in terms of unique visitors, total visits, average session time & bounce rates. In comparison to Google Analytics, SimilarWeb figures were on average, 40% less for unique visitors, 50% greater for average session time, 25% greater for bounce rate, and 20% lesser for total visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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Prediction of Passenger Car Friction Brake Application Based on Longitudinal Dynamics Using On-Road Vehicle Driving Data

SSRN

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SSRN 2023年

作者： Jensen, Kenneth M. Santos, Ilmar F. Clemmensen, Line Katrine Harder Theodorsen, Søren Corstens, Harry J.P. Department of Civil and Mechanical Engineering Technical University of Denmark Kgs. Lyngby Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark ILIAS Solutions Brussels Belgium

This work considers the problem of predicting the braking state of vehicles, i.e. when the friction brakes are being applied. The work is aimed at providing the foundation for the prediction of brake pad wear from vehicle usage data. The braking state is predicted from vehicle longitudinal dynamics, considering 6-axis inertial measurement unit (IMU), global navigation satellite system (GNSS), and vehicle on-board diagnostics (OBD-II) data, along with sensor fusion for more accurate vehicle state estimation. The proposed method aims at providing broad applicability to different vehicles, by using only standard OBD-II data instead of relying on manufacturer specific data. Rolling resistance coefficient and product of aerodynamic drag coefficient and frontal area are estimated using linear regression on data from vehicle coast-down tests with the drivetrain disengaged. Engine mass moment of inertia along with engine braking torque is estimated from similar tests with the drivetrain engaged in different gears. The engine braking torque is approximated using a polynomial expression in the engine speed. Braking state predictions are made by comparing measured longitudinal decelerations with estimated, assuming that if the measured exceeds the estimated, then the difference is caused by the application of the friction brakes. Experimental validation was carried out using two modern passenger cars. Comparisons of measured and estimated decelerations from coast-down tests with fitted parameters show good agreements when the drivetrain is disengaged, as well as engaged. For one vehicle labels for the braking state are obtained indirectly using a camera pointed at the brake pedal, using object tracking to determine if the brakes are being applied. For the other vehicle, the braking states are obtained more directly trough access to the brake pressure. Overall prediction performance is good with accuracy >94% and precision around 99%, while recall is around 90% across the two v

关键词： Parameter estimation

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions

Digital Twin Definition for Detecting Abnormal Perspiration ...

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Roedunet International Conference (RoEduNet)

作者： Cristian Vizitiu Ioan Turcin Kevin Alexander Dominey Selver Softic Adrian-Cătălin Dinculescu Vlad I. Bocăneţ Cosmin Dugan Sorin-Aurel Moraru The Space Applications and Technologies Laboratory Institute of Space Science -Subsidiary of INFLPR Magurele Romania Department of Automatics and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department Automation Technology CAMPUS 02 University of Applied Sciences Graz Austria Department IT & Business Informatics CAMPUS 02 University of Applied Sciences Graz Austria Department of Manufacturing Engineering Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation.

关键词： Adaptation models Generative AI Psychology Human factors Machine learning Predictive models Physiology Digital twins Modeling Biomedical monitoring

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Unitary-transformed projective squeezing: applications for circuit-knitting and state-preparation of non-Gaussian states

arXiv

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arXiv 2024年

作者： Anai, Keitaro Suzuki, Yasunari Tokunaga, Yuuki Matsuzaki, Yuichiro Takeda, Shuntaro Endo, Suguru Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino Tokyo180-8585 Japan Department of Electrical Electronic and Communication Engineering Faculty of Science and Engineering Chuo University 1-13-27 Kasuga Bunkyo-ku Tokyo112-8551 Japan

Continuous-variable (CV) quantum computing is a promising candidate for quantum computation because it can utilize infinite-dimensional Hilbert space even with one mode and efficiently handle continuous values. Although the photonic platforms have been considered as a leading platform for CV computation, hybrid systems comprising qubits and bosonic modes, e.g., superconducting hardware, have shown significant developments because they can prepare non-Gaussian state by utilizing the nonlinear interaction between the qubits and the bosonic modes. However, the experimentally realizable size of hybrid hardware is currently restricted. Moreover, the fidelity of the non-Gaussian state is still limited. In this work, by developing the projective squeezing method, we establish the formalism for projecting quantum states onto the states that are unitary-transformed from the squeezed vacuum at the expense of the sampling cost. Based on this formalism, we propose methods for simulating larger quantum devices and projecting the state onto one of the typical non-Gaussian states, the cubic phase state, with a higher squeezing level and higher nonlinearity. For practical implementation, we can apply the smeared projector either by linear-combination-of-unitaries or virtual quantum error detection algorithms by leveraging the interactions in hybrid systems of qubits and bosonic modes. We numerically verify the performance of our methods and also show that the effect of photon-loss errors can be suppressed due to projection. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Research on underwater biological detection method based on multi-scale fusion and attention mechanism 22

Research on underwater biological detection method based on ...

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Longyu Tang Yunong Yang Hong Wang College of Computer and Information Science Chongqing Normal University China Digital Education Engineering Center for Technology and Applied Research Chongqing Normal University China Modern Education Operation Section Chongqing Normal University China

ISBN: (纸本)9781450398343

Underwater robots are an important tool for exploiting marine resources, while underwater target detection is a key technology for marine fishing by underwater robots. For the problem that the traditional algorithm is difficult to detect small targets and multiple scales in the underwater environment, this paper proposes an underwater biological detection algorithm based on the multi-scale feature fusion and attention mechanism. First, a YOLOv5 network is constructed, and a bi-directionally weighted BiFPN feature pyramid structure is fed into the neck network so that multiscale target features can be efficiently merged. Second, a channel and spatial convolutional attention mechanism is introduced between the neck network and the prediction network in order to increase the saliency of the underwater target that needs to be detected, which effectively reduces the missed and erroneous detection of underwater targets. The experimental results show that the proposed algorithm can accurately detect multi-scale underwater targets in complex underwater environments with an average accuracy of 80.18%, which is 2.87% better than YOLOv5, and an average recall of 85.06%, which is 4.81% better than YOLOv5.

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Development of an modified Ant Colony Optimization algorithm for solving path planning problems of a robot system

Development of an modified Ant Colony Optimization algorithm...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Sudeep Sharan Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Avda. de la Universidad de Cádiz 10 11519 Puerto Real Cádiz Spain

In the modern world of high-speed technologies, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have created a Bioinspired algorithm to solve the problems encountered in realworld activities. The Ant Colony optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. In this work-in-progress article, we propose a new way of using the ACO algorithm which ensures solving the problems encountered in traditional ACO algorithms. This algorithm was tested on two environments to examine output efficiency and computational output time. The results show that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably better results were observed on testing it in the bigger-scale environment. The evaluations prove that the ACO algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test it in any real-world scenarios by increasing the number of ants.

关键词： Ant colony optimization Signal processing algorithms Signal processing Path planning Computational efficiency Robots Optimization

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Research on Optimizing Real-Time Data Processing in High-Frequency Trading Algorithms using Machine Learning

arXiv

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arXiv 2024年

作者： Fan, Yuxin Fu, Lei Wang, Liyang Hu, Zhuohuan Cheng, Yu Wang, Yuxiang School of Engineering and Applied Science University of Pennsylvania Toronto Canada School of Computer Science and Engineering Changshu Institute of Technology Suzhou China Olin Business School Washington University in St. Louis St. LouisMO63130 United States San Francisco United States Mclean VA22102 United States New York NY United States

High-frequency trading (HFT) represents a pivotal and intensely competitive domain within the financial markets. The velocity and accuracy of data processing exert a direct influence on profitability, underscoring the significance of this field. The objective of this work is to optimise the real-time processing of data in high-frequency trading algorithms. The dynamic feature selection mechanism is responsible for monitoring and analysing market data in real time through clustering and feature weight analysis, with the objective of automatically selecting the most relevant features. This process employs an adaptive feature extraction method, which enables the system to respond and adjust its feature set in a timely manner when the data input changes, thus ensuring the efficient utilisation of data. The lightweight neural networks are designed in a modular fashion, comprising fast convolutional layers and pruning techniques that facilitate the expeditious completion of data processing and output prediction. In contrast to conventional deep learning models, the neural network architecture has been specifically designed to minimise the number of parameters and computational complexity, thereby markedly reducing the inference time. The experimental results demonstrate that the model is capable of maintaining consistent performance in the context of varying market conditions, thereby illustrating its advantages in terms of processing speed and revenue enhancement. © 2024, CC BY.

关键词： Financial markets

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Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals

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Heliyon 2025年第3期11卷 e42257页

作者： Haq, Ihtisham Ul Husnain, Ghassan Ghadi, Yazeed Yasin Innab, Nisreen Alajmi, Masoud Aljuaid, Hanan Department of Computer Science Modeling Electronics and Systems (DIMES) University of Calabria CS Rende 87036 Italy Department of Computer Science CECOS University of IT and Emerging Sciences Peshawar 25000 Pakistan Department of Computer Science Al Ain University United Arab Emirates Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh 13713 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Computer Sciences Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60–650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment. © 2025 The Authors

关键词： Congenital heart disease Electrocardiogram Phonocardiogram Phonocardiography

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