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arXiv 2022年

作者： Bavdekar, Ritik Chopde, Eashan Jayant Bhatia, Ashutosh Tiwari, Kamlesh Daniel, Sandeep Joshua Atul Dept. of Computer Science and Information Systems Birla Insitute of Technology and Science Pilani Pilani Campus Jhunjhunu Rajasthan 333031 India Department of Electrical and Electronics Engineering Birla Insitute of Technology and Science Pilani Pilani Campus Jhunjhunu Rajasthan 333031 India

The development of large quantum computers will have dire consequences for cryptography. Most of the symmetric and asymmetric cryptographic algorithms are vulnerable to quantum algorithms. Grover’s search algorithm gives a square root time boost for the searching of the key in symmetric schemes like AES and 3DES. The security of asymmetric algorithms like RSA, Diffie Hellman, and ECC is based on the mathematical hardness of prime factorization and discrete logarithm. The best classical algorithms available take exponential time. Shor’s factoring algorithm can solve the problems in polynomial time. Major breakthroughs in quantum computing will render all the present-day widely used asymmetric cryptosystems insecure. This paper analyzes the vulnerability of the classical cryptosystems in the context of quantum computers, discusses various post-quantum cryptosystem families, discusses the status of the NIST post-quantum cryptography standardization process, and finally provides a couple of future research directions in this field. © 2022, CC BY.

关键词： Quantum computers

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection

arXiv

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arXiv 2024年

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Co. Limerick CastletroyV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems Tuam GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © 2024, CC BY.

关键词： Automobile simulators

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Multiresolution Self-Supervised Feature Integration via Attention Multiple Instance Learning for Histopathology Analysis

Multiresolution Self-Supervised Feature Integration via Atte...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nikos Tsiknakis Evangelos Tzoras Ioannis Zerdes Georgios C. Manikis Balazs Acs Johan Hartman Thomas Hatschek Theodoros Foukakis Kostas Marias Oncology-Pathology Karolinska Institutet Stockholm Sweden Institute of Computer Science Foundation for Research and Technology Hellas Heraklion Greece Breast Cancer Center Karolinska University Hospital Stockholm Sweden Department of Clinical Pathology and Cancer Diagnostics Karolinska University Hospital Stockholm Sweden Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

Digital histopathology image analysis of tumor tissue sections has seen great research interest for automating standard diagnostic tasks, but also for developing novel prognostic biomarkers. However, research has mainly been focused on developing uniresolution models, capturing either high-resolution cellular features or low-resolution tissue architectural features. In addition, in the patch-based weakly-supervised training of deep learning models, the features which represent the intratumoral heterogeneity are lost. In this study, we propose a multiresolution attention-based multiple instance learning framework that can capture cellular and contextual features from the whole tissue for predicting patient-level outcomes. Several basic mathematical operations were examined for integrating multiresolution features, i.e. addition, mean, multiplication and concatenation. The proposed multiplication-based multiresolution model performed the best (AUC=0.864), while all multiresolution models outperformed the uniresolution baseline models (AUC=0.669, 0.713) for breast-cancer grading. (Implementation: https://***/tsikup/multiresolution-clam)

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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

arXiv

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arXiv 2022年

作者： de Lange, Matthis H. Verhoek, Chris Preda, Valentin Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands ESTEC European Space Agency Noordwijk2200AG Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation control design. Automated conversion methods for nonlinear models can result in complex LPV representation, which are not suitable for controller synthesis. We apply several state-of-the-art techniques, including learning based approaches, to optimize the complexity and conservatism of the LPV embedding for an GPRV. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is optimal. © 2022, CC BY-NC-SA.

关键词： Principal component analysis

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Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

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Journal of engineering and Technological Sciences 2023年第5期55卷 587-599页

作者： Anam, Khairul Ismail, Harun Hanggara, Faruq S. Avian, Cries Nahela, Safri Sasono, Muchamad Arif Hana Dept. of Electrical Engineering Faculty of Engineering University of Jember Kalimantan Street No. 37 Sumbersari Krajan Timur Sumbersari Jember Regency East Java Sumbersari District68121 Indonesia Dept. of Electrical and Computer Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Da’an District Taiwan 106 Taipei City Taiwan Center for Development of Advanced Science and Technology University of Jember Kalimantan Street No. 37 Sumbersari Krajan Timur Sumbersari Jember Regency East Java Sumbersari District68121 Indonesia

The deployment of electromyography (EMG) signals can be used in decoding finger movements for exoskeleton robotics, prosthetic hands, and powered wheelchairs and thus has attracted the attention of many researchers. However, decoding any movement is a challenging task. The success of using EMG signals depends on the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study conducted an eight-feature extraction evaluation on various machine learning methods, i.e., Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). Datasets from four intact subjects were used to classify twelve finger movements. Through five cross-validations, the result showed that almost all feature extractions combined with SVM outperformed the other combinations of features and classifiers. Mean absolute value (MAV) as a feature and SVM as a classifier were the best combination, with an accuracy of 94.01%. © 2023 Published by IRCS-ITB.

关键词： Extraction

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Momentum-based ICA for Self Interference Cancellation in In-Band Full-Duplex systems

Momentum-based ICA for Self Interference Cancellation in In-...

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Asilomar Conference on Signals, systems & computers

作者： Chi Lee Chung-An Shen Mohammed E. Fouda Ahmed Eltawil Electronic and Computer Engineering Dept. National Taiwan University of Science and Technology Taipei Taiwan Center for Embedded & Cyber-physical Systems University of California-Irvine Irvine CA USA King Abdullah University of Science and Technology Thuwal Saudi Arabia

Recently, Independent Component Analysis (ICA) has proven its effectiveness as a self-interference cancellation method for in-band full duplex systems. However, ICA could suffer slow convergence due to the iterative estimation of the independent components which limits its usage in real-time applications. In this paper, we introduce a momentum-based ICA to accelerate convergence via incorporating gradient history. The proposed momentum-based ICA is evaluated and tested on different ICA algorithms including real-valued and complexvalued FastICA and entropy bound minimization based ICA. The results show significant speedup improvement compared to native ICA based on gradient descent approach. The proposed algorithm shows consistent results under different transceiver non-linearity and for different frame lengths.

关键词： Interference cancellation Estimation Full-duplex system Independent component analysis Minimization Transceivers Real-time systems

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Convergence Analysis of the Feedforward Hybrid Active Noise Control

Convergence Analysis of the Feedforward Hybrid Active Noise ...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Y. Ma Y. Xiao L. Ma K. Khorasani K. Hasegawa Jiangnan University Wuxi China Jiangsu University Zhenjiang China Wuxi Heshuofeng Technology Co. Ltd. China Prefectural University of Hiroshima Hiroshima Japan Dept. of Electrical & Computer Engineering Concordia University Montreal Canada Western Region Industrial Research Center Hiroshima Prefectural Technology Research Institute Hiroshima Japan

The conventional feedforward broadband active noise control (ANC, BANC, FFBANC) generates “firework noise” if it is set to converge fast and its reference signal contains both a broadband and a low-frequency sinusoidal component. The feedforward hybrid ANC (FFHANC) converges fast without producing any firework noise in the presence of such a reference signal. In this paper, statistical convergence analysis is conducted for the original FFHANC system. Difference equations are derived for the mean errors of the sinusoidal noise canceller (SNC) coefficients, the FFBANC and feedforward narrowband ANC (FFNANC) subcontroller filter weights. The mean residual error expression is also derived for the FFHANC system. Extensive simulations are performed to verify the validity of the analytical results.

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Optimization Efficiency of 5G MIMO Cooperative Spectrum Sharing NOMA Networks

Optimization Efficiency of 5G MIMO Cooperative Spectrum Shar...

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International Conference on Mechatronics (ICOM)

作者： Mohamed Hassan Babikir Khalid Hamid Ghada A.M. Abdu Imadeldin Elsayed Rashid A. Saeed Othman O. Khalifa Dept. of Comm. Systems Uni. of Science & Technology Khartoum Sudan Dept. of Electrical and Electronic Eng. Uni. Malaysia Pahang Pahang Malaysia Faculty of Eng. School of Elecs Eng. Sudan Uni. of Sci. and Tech. Khartoum Sudan Dept of Elec and Comp Eng Inter. Islamic Uni. Malaysia KL Malaysia Libyan Center for Engineering Research Info. Tech. Bani Walid Libya

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Non-orthogonal multiple access (NOMA) may improve spectrum efficiency (SE) in 5G and other future networks. Cognitive radio (CR) and multiple accesses might be beneficial for SE. A new era of productive communication with the combination of NOMA's network-oriented multi-access capabilities and the CR network (CRN) was anticipated. This research enhances the SE of the NOMA power domain (PD) in the downlink (DL) by utilizing cooperative cognitive radio networks (CCRNs). If the primary user (PU) is unable to receive data on the dedicated channel due to noise or interference, this strategy is expected to prove beneficial. Two network topologies with 100 MHz bandwidths and quadrature phase shift keying (QPSK) were proposed. Also used are 8×8, 16×16, and 32×32 Multiple Input Multiple Output (MIMO) topologies, which differ in transmit powers, user distances, and power location coefficients. Channel instability and successive interference cancellation (SIC) are also considered in performance studies. Fading channels are Rayleigh-fading, with frequency selectivity. MATLAB calculates the model's SE. SE performance is 35%, 44%, and 73% better for 8×8, 16×16, and 32×32 MIMO NOMA, while CCRN 8×8, 16×16, and 32×32 MIMO NOMA enhances SE performance by 50%, 65%, and 80%, respectively, when compared with the standard NOMA model. Using MIMO technology boosts SE significantly.

关键词： NOMA Interference cancellation Network topology 5G mobile communication Rayleigh channels Mathematical models Topology

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Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

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Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

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Improved Backward Scenario Reduction Method for Generation Planning Considering Extreme Scenarios

Improved Backward Scenario Reduction Method for Generation P...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yigong Xie Ziyu Huo Chen Wu Wenfeng Yao Jiayang Wang Ying Qiao Power dispatch control center Yunnan Power Grid Guangzhou China State Key Lab of Power Systems Dept. of Electrical Engineering Tsinghua University Beijing China Planning and construction research Center Yunnan Power Grid Kunming China China Southern Power Grid Research Institute State Key Laboratory of Current Transmission Technology Guangzhou China

Renewable energy sources exhibit significant variability and uncertainty, showing numerous distinct scenarios. In the context of generation planning, it's imperative to employ scenario reduction techniques to select representative scenarios that capture the characteristics of renewable energy outputs. However, traditional scenario reduction methods often fail to adequately preserve the extreme output scenarios of these renewables. This paper improves the simultaneous backward reduction method (SBR), enhancing its capability to effectively capture the extreme output scenarios of renewable sources. As a result, the generation planning becomes more economically viable and more reliable. The efficacy of the improved SBR method is demonstrated using data from the Qinhuangdao region in China.

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