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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

International Journal of Advanced computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Modified Wild Horse Optimizer for system reliability-redundancy allocation problems with cold- standby redundancy strategy

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International Journal of System Assurance engineering and Management 2025年 1-15页

作者： Pant, Sangeeta Singh, Manoj K. Kumar, Anuj Chaube, Shshank Ram, Mangey Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Maharashtra Pune 412115 India School of Computer Science Engineering and Technology Bennett University Greater Noida 201310 India School of Computer Science Engineering & Applications D. Y. Patil International University (DYPIU) Akrudi Maharashtra Pune 411044 India Department of Physical Sciences Jaypee University Anoopshahr Bulandshahr 203390 India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

The design of a reliable system depends greatly on several key factors, including the allocation of reliability to individual components, the use of redundancy, and the allocation of redundant components to enhance overall system reliability. This article proposes a Modified Wild Horse Optimizer (MWHO) for solving system reliability-redundancy allocation problems (RRAPs) with cold-standby redundancy strategy. The cold-standby redundancy is a common technique used to enhance system reliability by adding backup components that remain idle until a failure occurs. However, creating a trade-off between the number of redundant components and their reliabilities with the objective of maximizing overall system reliability remains a challenging problem. Since components can malfunction even when transitioning from standby to active mode, any issues caused by imperfect switching are also taken into account when evaluating the system's performance. The proposed MWHO algorithm incorporates a novel search strategy and a new update mechanism to deal with mixed-integer non-linear RRAPs. The proposed MWHO algorithm is demonstrated to be effective for solving system RRAPs with cold-standby redundancy strategy, outperforming other optimization algorithms in solution quality and convergence speed to yield higher system reliability values. © The Author(s) under exclusive licence to The Society for Reliability engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.

关键词： Cold-standby redundancy strategy Imperfect switching Metaheuristics Modified Wild Horse Optimizer Reliability-redundancy allocation problems

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A Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis

A Novel Approach for Performance Evaluation and Effectivenes...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Islam, Md Aminul Nag, Anindya Das, Ayontika Faruque, Jobaer Shuvo, Shabbir Ahmed Nur, Abdullah Hafez Rahman, Md Habibur School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom Department of Computer Science and Engineering Adamas University Kolkata India IRD Dhaka Bangladesh Offenburg University of Applied Sciences Offenburg Germany International Islamic University Chittagong Sonaichhari Bangladesh Department of Mathematics University of Chittagong Chittagong Bangladesh

ISBN: (纸本)9789819795062

Cardiovascular disease (CVD) has emerged as a prominent cause of mortality globally during the past few decades. To receive medical care for heart illness in a clinic or medical center, it is necessary to undergo costly diagnostic tests. Implementing machine learning (ML) algorithms presents a viable approach to monitoring the health of individuals with cardiac conditions, offering a potential solution to mitigate the financial burden of medical expenses. This study employed ten ML algorithms to predict heart disease, which distinguishes itself from others by incorporating three distinct cross-validation approaches and three sample techniques, enhancing our findings’ accuracy. Three different cross-validation strategies have been implemented in this study: holdout cross-validation, tenfold cross-validation, and stratified tenfold cross-validation. Each technique has been applied to every sample technique used in the study. DTC, RFC, KNN, and XGB algorithms exhibited the most favorable outcomes. Our recommended model is the RF Classifier implemented with near-miss under-sampling out of the four models considered. The model under consideration demonstrates the maximum level of accuracy, specifically 97.54%, 97.11%, and 99%, while employing the Extra Tree, Random Forest Classifier, and XGB. The ML model's performance was assessed using metrics like accuracy, error rate, precision, recall, F1-score, ROC AUC curve etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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VESPACE: A verifiable blockchain-based data space solution to empower the data economy

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computer Communications 2025年 239卷

作者： Costagliola, Andrea Roberta Mazzocca, Carlo Bujari, Armir Montanari, Rebecca Bellavista, Paolo Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy

In the rapidly evolving data economy, the ability to securely and efficiently share data between organizations has become paramount, unlocking new opportunities for innovation and growth. In this context, different initiatives have worked on conceptual proposals and enabling technological building blocks, addressing design aspects of data spaces. However, the current landscape lacks practical implementations and integration of secure data-sharing primitives supporting a decentralized data ecosystem. To this end, we conduct an analysis of previous efforts and initiatives, identifying gaps. We then introduce VESPACE, a blockchain-based platform for data spaces that enables participants to selectively and securely share verifiable data with authorized users while maintaining control over their access. Our framework incorporates data sovereignty principles implemented through Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and blockchain technology, qualifying decentralized identity and access control as key features to establish user trust in decentralized data ecosystems. We present a prototype system implementation of VESPACE, evaluating the design choices, showcasing the feasibility of our proposal. © 2025 The Authors

关键词： Authentication

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Long-term stability from adaptive auto-calibration in mid-infrared sensing instruments 25

Long-term stability from adaptive auto-calibration in mid-in...

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Optical Fibers and Sensors for Medical Diagnostics, Treatment, and Environmental Applications XXV 2025

作者： Alexis, Al Anderson, London Khan, Mohammad A. Division of Physics Engineering Mathematics and Computer Science Optical Science Center for Applied Research Delaware State University 1200 N. Dupont Hwy DoverDE19901 United States

ISBN: (纸本)9781510683686

Mid-infrared laser-based sensing is widely used for the detection and quantification of trace gases for environmental monitoring, industrial process control, defense, and security applications. However, such laser spectroscopy-based field instruments require frequent calibration to correct for long-term drifts and precision. In many situations, such calibrations may be redundant and may not be effective in monitoring instrument errors or discriminating "real"environmental effects with instrument drifts. This paper shows that physics and diffusion-based models can be developed to predict gradual or rapid changes in density at given temperatures and appropriate source characterizations. We used a 3.3μm, laser-based instrument to demonstrate an adaptive and auto-calibration methodology to account for drifts during field deployments and an adaptive method to validate environmental changes, e.g., plume detection, diurnal variations, and flux emissions. We field operation and implementation of adaptive auto-calibration. The instruments are used for the detection of methane and water vapor, but an adaptive auto-calibration technique can be employed for any laser sensing instrument. © 2025 SPIE.

关键词： Laser spectroscopy

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Education 4.0: Considering the Development of Teachers’ Psychological Dispositions Towards the Use of ICT with Kindergarten Children 14th

Education 4.0: Considering the Development of Teachers’ P...

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14th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, mis4TEL 2024

作者： Peretti, Sara Caruso, Federica Pino, Maria Chiara Di Mascio, Tania Centre of Excellence DEWS University of L’Aquila L’Aquila Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Department of Biotechnological and Applied Clinical Sciences L’Aquila Italy

ISBN: (纸本)9783031855603

Since 2020, the COVID-19 pandemic has forced teachers to rapidly adopt Information and Communication Technology (ICT) tools for distance learning, transforming education into an all-digital environment. This shift posed significant challenges, especially for kindergarten teachers accustomed to traditional, face-to-face methods. These changes will have long-term impacts on education, influencing teachers’ attitudes and beliefs, which take time to stabilize and evaluate. The present paper addresses this by posing the question: In 2024, four years after the COVID-19 pandemic, what attitudes have kindergarten teachers developed towards ICT integration in the classroom? For this purpose, the standardized "Intrapersonal Technology Integration Scale" was chosen to assess the beliefs of Italian kindergarten teachers. The study includes 49 kindergarten teachers, all women. Data analysis showed that they have a strong interest in using ICT, indicating a need for ongoing support and training initiatives. However, teachers did not report feeling self-efficacy in using ICT with children in the classroom. This finding is crucial as it can influence their expectations and motivation towards integrating these technologies into daily teaching activities. In conclusion, this study highlights the critical importance of teachers’ attitudes in shaping future learning environments within the Education 4.0 era and provides suggestions to maximize ICT integration starting from early education to better meet the needs of future citizens. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Teaching

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques 22

Automating Neural Model Selection in Spiking Neural Networks...

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22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural Computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Hesitant Bi-fuzzy Dombi Operator Based Group Decision-making Approach for Electric Vehicle Selection

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Process Integration and Optimization for Sustainability 2025年 1-35页

作者： Golui, S. Das, Bidyut Mahapatra, G.S. Mahapatra, B.S. Department of Engineering Sciences and Humanities Siliguri Institute of Technology Siliguri 734009 India Department of Applied Mathematics Maulana Abul Kalam Azad University of Technology Haringhata 741249 India Department of Master of Computer Applications Siliguri Institute of Technology Siliguri 734009 India Department of Mathematics National Institute of Technology Puducherry Karaikal 609609 India School of Applied Science and Humanities Haldia Institute of Technology Haldia 721657 India

Electric vehicles (EVs) are one of the sustainable modes of transportation that can reduce the air pollution caused by fossil fuel-powered automobiles. The literature overlooks non-technical criteria, including environmental considerations and social responsibility in the EV selection process, which may navigate the adoption of EVs. This study addresses the EV selection process in the hesitant bi-fuzzy context, considering technical and non-technical EV features. A multi-criteria group decision-making approach is developed to evaluate EVs utilizing a modified hesitant bi-fuzzy COmbinative Distance-based Assessment technique. This study proposes a novel hesitant bi-fuzzy Dombi weighted average operator to integrate the decision maker’s opinions with their weightage. The proposed approach incorporates the importance of the criteria evaluated by the method based on the removal effects of criteria and hesitant information in Euclidean and Taxicab metrics to produce the ranking sequence of EVs. The Python code for the suggested group decision-making model is provided to rationalize the computational process. This study highlights the relative importance of charging time, price, and range compared to the remaining features in selecting EVs. The study’s findings indicate that the technical features of an EV are still a primary consideration for the customers. These findings will assist policymakers and EV industry stakeholders in making decisions to focus on EV modification. A comparative analysis with the existing decision-making models encompasses the applicability of the proposed technique. In contrast, the sensitivity analysis demonstrates the suggested approach’s robustness through the alteration in decision makers’ s preferences, internal parameters, and criteria importance to assess their impact on the final rankings of EVs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CODAS Dombi operator Electric vehicle Hesitant bi-fuzzy set MCGDM

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Enhancing the Swanepoel method: precise envelope detection of thin-film transmission spectra

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Optics Express 2025年第6期33卷 13376-13400页

作者： Ballester, Manuel Marquez, Almudena P. Lopez-Tapia, Santiago Fernandez, Susana Bass, John Minkov, Dorian Wuersch, Christoph Willomitzer, Florian Marquez, Emilio Katsaggelos, Aggelos K. Computer Science Department Northwestern University EvanstonIL60208 United States Department of Mathematics Faculty of Science University of Cadiz Puerto Real11510 Spain Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States CIEMAT Avenida Complutense 40 Madrid28040 Spain Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Sofia1000 Bulgaria Institute for Computational Engineering ICE OST Eastern Switzerland University of Applied Sciences Switzerland Department of Condensed-matter Physics Faculty of Science University of Cadiz Puerto Real11510 Spain

The Swanepoel method is a widely used optical technique for characterizing thin films through normal-incidence transmission measurements. A critical step in this approach involves extracting the upper and lower envelopes of the measured oscillatory spectrum. By analyzing the transmission spectrum and its corresponding envelopes, the Swanepoel procedure enables precise determination of the film refractive index and extinction coefficient. However, even minor inaccuracies in envelope construction can propagate significant errors into the final characterization results. To address this challenge, we present what we believe to be a novel physics-informed optimization algorithm for envelope detection. Our mathematical model reformulates the envelope detection problem as a global optimization task that enforces the physical properties of the Swanepoel envelopes. Extensive validation on fifty randomly generated transmission spectra demonstrates unprecedented accuracy: the method achieves root-mean-square errors (RMSE) below 0.10% for the upper envelope and 0.06% for the lower envelope (more than doubling the accuracy of current state-of-the-art approaches). Furthermore, the proposed model has been assessed with two experimental transmission spectra, demonstrating its robustness and accuracy with real noisy data. We have developed an open-source Python software package (see text below). This software includes not only our innovative envelope construction algorithm but also additional envelope drawing algorithms for comparison and an efficient implementation of the Swanepoel method, enabling complete optical characterization of thin films. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Mean square error

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