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Academic Articles & Research Papers
Showing 20 of 32 academic articles
Yanbing Bai, Rui-Yang Ju, Lemeng Zhao, Junjie Hu et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
(2026)
Uncrewed Aerial Vehicles (UAVs) have become increasingly important in disaster emergency response by facilitating aerial video analysis. Due to the limited computational resources available on UAVs, large models cannot be run efficiently fo...
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Open Access (DOAJ)
DOI: 10.1109/JSTARS.2026.3685660
Yevhenii Kovryzhenko, Ehsan Taheri • Drones
(2025)
Motion planning is critical for ensuring precise and efficient operations of unmanned aerial vehicles (UAVs). While polynomial parameterization has been the prevailing approach, its limitations in handling complex trajectory requirements ha...
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Open Access (DOAJ)
DOI: 10.3390/drones9010077
Hoyoung Chung, Jin-Hwi Kim, Junseong Ahn, Yoona Chung et al. • Agriculture
(2026)
This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32...
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Open Access (DOAJ)
DOI: 10.3390/agriculture16020223
V. Luzhetsky, M. Tsikhotskyi • Ìнформаційні технології та компʼютерна інженерія
(2025)
In the conditions of processing large amounts of graphic data, the task arises of developing a reliable image encryption scheme with reduced computing costs. The purpose of the study was to develop a deterministic scheme for encrypting and ...
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Open Access (DOAJ)
DOI: 10.31649/vitce/3.2025.77
David Guerrero Martos, Alejandro Millán Calderón, Jorge Juan Chico, Julian Viejo Cortés et al. • EURASIP Journal on Advances in Signal Processing
(2020)
Abstract The computation of the sine and cosine functions is required in devices ranging from application-specific signal processors to general purpose floating-point units. Even in the latter case, the required functionality can be reduced...
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Open Access (DOAJ)
DOI: 10.1186/s13634-020-00692-5
Hocheol Lim, Hyeon-Nae Jeon, June-Koo Rhee, Byungdu Oh et al. • Scientific Reports
(2022)
Abstract Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and...
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Open Access (DOAJ)
DOI: 10.1038/s41598-022-11537-6
Pauline J. Ollitrault, Alexander Miessen, Ivano Tavernelli • Accounts of Chemical Research
(2021)
ConspectusSimulating molecular dynamics (MD) within a comprehensive quantum framework has been a long-standing challenge in computational chemistry. An exponential scaling of computational cost renders solving the time dependent Schrödinger...
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DOI: https://doi.org/10.1021/acs.accounts.1c00514
Ruofan Chen, Xiansong Xu, Chu Guo • New Journal of Physics
(2024)
Tensor-network-based methods are promising candidates to solve quantum impurity problems (QIP). They are free of sampling noises and the sign problem compared to state-of-the-art continuous-time quantum Monte Carlo methods. Recent progress ...
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Open Access (DOAJ)
DOI: 10.1088/1367-2630/ad19fa
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner et al. • IEEE Transactions on Neural Networks
(2008)
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper,...
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Open Access
DOI: https://doi.org/10.1109/tnn.2008.2005605
Vera von Burg, Guang Hao Low, Thomas Häner, Damian S. Steiger et al. • Physical Review Research
(2021)
The quantum computation of electronic energies can break the curse of dimensionality that plagues manyparticle quantum mechanics.It is for this reason that a universal quantum computer has the potential to fundamentally change computational...
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DOI: https://doi.org/10.1103/physrevresearch.3.033055
Huanjie Zhang, Yang Chen, Shenghao Chen, Zilong Zhao et al. • Sensors
(2026)
With the rapid development of quantum computing, in response to its disruptive threats to traditional cryptosystems and the urgent demand for lightweight and highly scalable secure group communication among resource-constrained devices in l...
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Open Access (DOAJ)
DOI: 10.3390/s26154840
Nicholas E. Protonotarios, George A. Kastis, Andreas D. Fotopoulos, Andreas G. Tzakos et al. • Mathematics
(2022)
The effective resolution of positron emission tomography (PET) can be significantly degraded by patient motion during data acquisition. This is especially true in the thorax due to respiratory motion. This study concentrates on the improvem...
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Open Access (DOAJ)
DOI: 10.3390/math11010055
Cristian Cosentino, Simone Gatto, Pietro Liò, Fabrizio Marozzo • Future Internet
(2026)
Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. ...
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Open Access (DOAJ)
DOI: 10.3390/fi18030174
Guoyu Zuo, Minghui Zhang, Jingwei Ren, Qifei Wu et al. • CAAI Transactions on Intelligence Technology
(2026)
ABSTRACT Knee joint moment estimation is a critical component in biomechanical analysis, with profound implications for rehabilitation assessment and the development of assistive devices like exoskeletons. However, existing data‐driven appr...
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Open Access (DOAJ)
DOI: 10.1049/cit2.70165
Hang Li • National Science Review
(2017)
Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...
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DOI: https://doi.org/10.1093/nsr/nwx110
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens et al. • arXiv (Cornell University)
(2015)
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1512.00567
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens et al. • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
(2016)
Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...
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DOI: https://doi.org/10.1109/cvpr.2016.308
Hao Huang, G.T.S. Ho, V. Tang, E.W.H. Chow et al. • Journal of Advanced Research
(2026)
Introduction: Crack damage is a significant issue in tunnel environments, requiring efficient and reliable detection to ensure structural integrity. Many existing solutions are computationally expensive, making them unsuitable for the tight...
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Open Access (DOAJ)
DOI: 10.1016/j.jare.2025.11.052
Francisco J Zamora-Martínez, Salvador España-Boquera, Maria Jose Castro-Bleda, Adrian Palacios-Corella • PLoS ONE
(2018)
This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different le...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0200884
Yuchao Feng, Jianwei Zheng, Mengjie Qin, Cong Bai et al. • Remote Sensing
(2021)
Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well know...
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Open Access (DOAJ)
DOI: 10.3390/rs13214407